Interstellar Object Accessibility and Mission Design

Abstract

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.

Figures (6)

• Figure 1: Most trajectories have relative velocities at $>$60 km/s and phase angles of $>$120$^\circ$.
• Figure 2: ISO Encounter Concept of Operations
• Figure 3: M-STAR spacecraft at Caltech.
• Figure 4: N-R algorithm achieves 25-30x better performance at the cost of 4x more $\Delta V$ as compared to AutoNav
• Figure 5: N-R algorithm performance degrades with decreasing control frequency. Left: Absolute miss distance as a function of ISO relative velocity. Right: $\Delta V$ consumption as a function of relative velocity.