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In this paper, a distributed algorithm for heterogeneous multi-satellite mission planning based on potential game and hybrid particle swarm optimization is proposed. The mission planning problem is formulated as an exact potential game through locally designed utility functions precisely correlated with global objectives, allowing satellites to coordinate decisions autonomously via limited neighborhood communication. A regret-driven mechanism with a memory list is incorporated to guide the system toward high-quality Nash equilibria. Simulation experiments conducted with realistic STK-generated data demonstrate the algorithm’s superior performance in system utility and task completion rates compared to existing methods. However, the approach assumes stable communication links and pre-calculated visibility windows. The proposed approach provides a practical solution for autonomous cooperative planning in large-scale satellite constellations.

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Potential Game-Based Distributed Mission Planning Methods for Heterogeneous Multi-satellite Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Ruohan Sun,
  • Qingrui Zhou,
  • Yuanhua Ni,
  • Zhongxin Liu

摘要

In this paper, a distributed algorithm for heterogeneous multi-satellite mission planning based on potential game and hybrid particle swarm optimization is proposed. The mission planning problem is formulated as an exact potential game through locally designed utility functions precisely correlated with global objectives, allowing satellites to coordinate decisions autonomously via limited neighborhood communication. A regret-driven mechanism with a memory list is incorporated to guide the system toward high-quality Nash equilibria. Simulation experiments conducted with realistic STK-generated data demonstrate the algorithm’s superior performance in system utility and task completion rates compared to existing methods. However, the approach assumes stable communication links and pre-calculated visibility windows. The proposed approach provides a practical solution for autonomous cooperative planning in large-scale satellite constellations.