Spacecraft rendezvous enables on-orbit servicing, debris removal, and crewed docking, forming the foundation for a scalable space economy. Designing such missions requires rapid exploration of the tradespace between control cost and flight time across multiple candidate targets. However, multi-objective optimization in this setting is challenging, as the underlying constraints are often nonconvex, and mission designers must balance accuracy (e.g., solving the full problem) with efficiency (e.g., convex relaxations), slowing iteration and limiting design agility. To address these challenges, this paper proposes an AI-powered framework that enables agile and generalized rendezvous mission design. Given the orbital information of the target spacecraft, boundary conditions of the servicer, and a range of flight times, a transformer model generates a set of near-Pareto optimal trajectories across varying flight times in a single parallelized inference step, thereby enabling rapid mission trade studies. The model is further extended to accommodate variable flight times and perturbed orbital dynamics, supporting realistic multi-objective trade-offs. Validation on chance-constrained rendezvous problems in Earth orbits with passive safety constraints demonstrates that the model generalizes across both flight times and dynamics, consistently providing high-quality initial guesses that converge to superior solutions in fewer iterations. Moreover, the framework efficiently approximates the Pareto front, achieving runtimes comparable to convex relaxation by exploiting parallelized inference. Together, these results position the proposed framework as a practical surrogate for nonconvex trajectory generation and mark an important step toward AI-driven trajectory design for accelerating preliminary mission planning in real-world rendezvous applications.

翻译：航天器交会技术为在轨服务、碎片清除和载人对接提供了基础，构成了可扩展空间经济的基石。设计此类任务需要快速探索多个候选目标之间控制成本与飞行时间的设计权衡空间。然而，该场景下的多目标优化具有挑战性，因为底层约束通常是非凸的，且任务设计者必须在精度（例如求解完整问题）与效率（例如凸松弛）之间取得平衡，这降低了迭代速度并限制了设计敏捷性。为应对这些挑战，本文提出一种人工智能驱动的框架，以实现敏捷且通用的交会任务设计。给定目标航天器的轨道信息、服务航天器的边界条件以及飞行时间范围，Transformer模型通过单次并行化推理步骤，生成覆盖不同飞行时间的一组近似帕累托最优轨迹，从而实现快速的任务权衡研究。该模型进一步扩展以适应可变飞行时间及受扰轨道动力学，支持现实的多目标权衡分析。在地球轨道上带有被动安全约束的机会约束交会问题上的验证表明，该模型在飞行时间和动力学方面均具有良好的泛化能力，能够持续提供高质量的初始猜测，使优化在更少迭代次数内收敛至更优解。此外，该框架通过利用并行化推理，高效近似帕累托前沿，其运行时间与凸松弛方法相当。综上，这些结果确立了所提框架作为非凸轨迹生成实用替代方案的可行性，标志着在现实交会应用中利用人工智能驱动轨迹设计以加速初步任务规划的重要进展。