Mission planning for repeated multi-spacecraft non-contact debris removal

Non-contact debris removal methods are fuel-efficient in a single operation compared to contact-based strategies as spacecraft don’t need to match debris velocity. To comprehensively analyze this scheme, maneuvering schemes for maximum debris removal with minimum fuel consumption, including task assignment, sequence planning, and trajectory planning, must be formulated. The coupling between variables’ dimensions and optimization results in task assignment poses challenges, as debris removal is repetitive and uncertain, leading to a vast search space. This paper proposes a novel Greedy Randomized Adaptive Search Procedure with Large Neighborhood and Crossover Mechanisms (GRASP-LNCM) to address this problem. The hybrid dynamic iteration mechanism improves computational efficiency and enhances the optimality of results. The model innovatively considers unsuccessful single removal by using a quantitative method to assess removal percentage. In addition, to improve the efficiency of sequence and trajectory planning, a Suboptimal Search Algorithm (SSA) based on the Lambert property and accelerated Multi-Revolution Lambert Problem (MRLP) solving strategy is established. Finally, a real Iridium-33 debris removal mission is studied. The simulation demonstrates that the proposed algorithm achieves state-of-the-art performance in several typical scenarios. Compared to the contact-based scheme, the new one is simpler, saving more fuel under certain conditions.