DualSynNet: A Dual-Center Collaborative Space Network with Federated Graph Reinforcement Learning for Autonomous Task Optimization
Xuewei Niu, Jiabin Yuan, Lili Fan, Keke Zha · Aerospace · 2025
Recent space exploration roadmaps from China, the United States, and Russia highlight the establishment of Mars bases as a major objective. Future deep-space missions will span the inner solar system and extend beyond the asteroid belt, demanding network control systems that sustain reliable communication and efficient scheduling across vast distances. Current centralized or regionalized technologies, such as the Deep-Space Network and planetary relay constellations, are limited by long delays, sparse visibility, and heterogeneous onboard resources, and thus cannot meet these demands. To address these challenges, we propose a dual-center architecture, DualSynNet, anchored at Earth and Mars and enhanced by Lagrange-point relays and a minimal heliocentric constellation to provide scalable multi-mission coverage. On this basis, we develop a federated multi-agent reinforcement learning framework with graph attention (Fed-GAT-MADDPG), integrating centralized critics, decentralized actors, and interplanetary parameter synchronization for adaptive, resource-aware scheduling. A unified metric system: Reachability, Rapidity, and Availability, is introduced to evaluate connectivity, latency, and resource sustainability. Simulation results demonstrate that our method increases task completion to 52.4%, reduces deadline expiration, constrains rover low-state-of-charge exposure to approximately 0.8%, and maintains consistently high hardware reliability across rover and satellite nodes. End-to-end latency is reduced, with a shorter tail distribution due to fewer prolonged buffering or stagnation periods. Ablation studies confirm the essential role of graph attention, as removing it reduces completion and raises expiration. These results indicate that the integration of a dual-center architecture with federated graph reinforcement learning yields a robust, scalable, and resource-efficient framework suitable for next-generation interplanetary exploration.