Game-Based Deductive Optimization for Spacecraft Avoidance of Detective Threat via HS-NSGA-II

The increasing risk of collision requires spacecraft to autonomously avoid orbital threats, where the detective threat poses significant danger. To address this challenge, this paper proposes a game-based deductive optimization approach to derive an effective avoidance strategy for the spacecraft. Firstly, an emergency-phase game model is established within the fixed terminal time between the spacecraft and the detective threat, accounting for the effects of J₂ perturbation. The terminal conditions of the game are determined, which ensure the spacecraft maintains a safe distance from the threat and resides in the backlight region. Secondly, the game model and terminal constraints are transformed into a multi-objective optimization problem (MOP). To obtain more representative avoidance strategies, a hybrid selection-based non-dominated sorting genetic algorithm-II (HS-NSGA-II) is designed, which improves the diversity and the distribution of the Pareto set. Finally, simulation results demonstrate that the proposed approach effectively enables the spacecraft to avoid the detective threat while achieving optimal concealment.