Interstellar objects (ISOs) represent a compelling and under-explored category of celestial bodies, providing physical laboratories to understand the formation of our solar system and probe the composition and properties of material formed in exoplanetary systems. In this work, we investigate existing approaches to designing successful flyby missions to ISOs, including a deep learning-driven guidance and control algorithm for ISOs traveling at velocities over 60 km/s. We have generated spacecraft trajectories to a series of synthetic representative ISOs, simulating a ground campaign to observe the target and resolve its state, thereby determining the cruise and close approach delta-Vs required for the encounter. We discuss the accessibility of and mission design to ISOs with varying characteristics, with special focuses on 1) state covariance estimation throughout the cruise, 2) handoffs from traditional navigation approaches to novel autonomous navigation for fast flyby regimes, and 3) overall recommendations about preparing for the future in situ exploration of these targets. The lessons learned also apply to the fast flyby of other small bodies, e.g., long-period comets and potentially hazardous asteroids, which also require tactical responses with similar characteristics.

翻译：星际天体（ISOs）是一类极具研究价值但尚未被充分探索的天体，它们为理解太阳系形成过程及探测系外行星系统中物质的组成与性质提供了天然的物理实验室。本研究探讨了现有成功飞越星际天体任务的设计方法，包括针对速度超过60公里/秒的星际天体所开发的深度学习驱动制导控制算法。我们针对一系列合成的代表性星际天体生成了航天器轨迹，模拟了地面观测活动以确定目标状态，进而计算出交会所需的巡航段与近距离接近段速度增量。本文讨论了具有不同特性的星际天体的可访问性及相应任务设计，重点聚焦于：1）巡航全程的状态协方差估计；2）从传统导航方法向新型自主导航的交接策略（适用于高速飞越场景）；3）关于未来对这些目标开展原位探测准备工作的总体建议。本研究获得的经验同样适用于其他高速飞越小天体的任务，例如长周期彗星与潜在威胁小行星，这些任务同样需要具备类似特征的快速响应能力。