Game-Based Multiple Spacecraft Scheduling of Cooperative Active Debris Removal Service Under Incomplete Information

This study addresses the challenge of ensuring comprehensive mission coverage in active debris removal (ADR) services for large constellations under scenarios of incomplete target information. A two-stage mission framework that includes observation and recovery is proposed, where the satellite scheduling problem is modeled as a potential game, with each spacecraft acting as a player in the game-theoretic formulation. An optimized model for the Lambert problem is developed to address fuel and time efficiency, employing a hierarchical optimization method that integrates these solutions into mission scheduling cost calculations. A distributed task scheduling algorithm is introduced to solve globally optimal solutions for multi-constrained mission assignment problems, incorporating an event-triggered mechanism to manage state transitions between the observation and removal stages. It is analytically proven that the Nash equilibrium in the potential game corresponds to the global optimum, enabling the derivation of optimal task assignments through equilibrium computation. Comparative evaluations demonstrate that the proposed approach outperforms conventional task scheduling methods in terms of computational speed and mission cost. This work advances the efficiency of ADR mission planning under uncertainty, offering a scalable framework for large-scale debris removal operations.