Rapid Sequence Cost Evaluation for Multi-Spacecraft Rendezvous: A Permutation-Invariant Set Transformer

Abstract

Multi-spacecraft multi-rendezvous mission design requires repeated evaluations of the total cost of visiting a target set within a given time window, and the efficiency of this evaluation is the primary barrier to global search. Existing methods either optimize the order and epochs of each candidate sequence, which is expensive, or estimate the cost leg by leg, in which case the error accumulates with the sequence length. This paper proposes a deep model that regresses the total cost of an unordered target set directly: the pairwise transfer cost surface of each target pair is compressed into a latent feature, and a permutation-invariant transformer encoder regresses the sequence cost from these features and the window parameters, without ordering information. Trained on about 0.85 million labeled sequences, the model attains a mean relative error near 5\% and a sub-millisecond evaluation time, about five orders of magnitude faster than precise optimization. Embedded into an evolutionary global search and applied to the GTOC9 problem, it yields the best known solution at every fleet size considered, from seven to ten spacecraft: directly, it produces near-optimal solutions cheaply, and as the initial guess of a differential-evolution refinement it improves the best published solutions further. The evaluator is not specific to this problem or to debris removal: it requires only a pairwise transfer cost function and a sequence-level cost metric monotone in the leg costs, and therefore applies equally to the servicing of satellites in low Earth orbit and to multi-target visits in deep space.