Distributed Spacecraft Missions present challenges for current trajectory optimization capabilities. When tasked with the global optimization of interplanetary Multi-Vehicle Mission (MVM) trajectories specifically, state-of-the-art techniques are hindered by their need to treat the MVM as multiple decoupled trajectory optimization subproblems. This shortfall blunts their ability to utilize inter-spacecraft coordination constraints and may lead to suboptimal solutions to the coupled MVM problem. Only a handful of platforms capable of fully-automated multi-objective interplanetary global trajectory optimization exist for single-vehicle missions (SVMs), but none can perform this task for interplanetary MVMs. We present a fully-automated technique that frames interplanetary MVMs as Multi-Objective, Multi-Agent, Hybrid Optimal Control Problems (MOMA HOCP). This framework is introduced with three novel coordination constraints to explore different coupled decision spaces. The technique is applied to explore the preliminary design of a dual-manifest mission to the Ice Giants: Uranus, and Neptune, which has been shown to be infeasible using only a single spacecraft anytime between 2020 and 2070.

分布式航天器任务对当前的轨迹优化能力提出了挑战。当专门负责行星际多车任务（MVM）轨迹的全局优化时，由于需要将MVM视为多个解耦的轨迹优化子问题，因此阻碍了最新技术的发展。这种不足削弱了它们利用航天器间协调约束的能力，并可能导致耦合MVM问题的次优解决方案。对于单个系统，仅存在少数几个能够实现全自动多目标行星际全局轨迹优化的平台车载任务（SVM），但没有人可以对行星际MVM执行此任务。我们提出了一种将星际MVM构成多目标，多代理，混合最优控制问题（MOMA HOCP）的全自动技术。该框架引入了三个新颖的协调约束，以探索不同的耦合决策空间。该技术被用于探索对“冰巨人：天王星”和“海王星”的双舱任务的初步设计，这被证明在2020年至2070年之间的任何时间仅使用单个航天器是不可行的。