Interstellar objects (ISOs), astronomical objects not gravitationally bound
to the Sun, are likely representatives of primitive materials invaluable in
understanding exoplanetary star systems. Due to their poorly constrained orbits
with generally high inclinations and relative velocities, however, exploring
ISOs with conventional human-in-the-loop approaches is significantly
challenging. This paper presents Neural-Rendezvous -- a deep learning-based
guidance and control framework for encountering any fast-moving objects,
including ISOs, robustly, accurately, and autonomously in real-time. It uses
pointwise minimum norm tracking control on top of a guidance policy modeled by
a spectrally-normalized deep neural network, where its hyperparameters are
tuned with a newly introduced loss function directly penalizing the state
trajectory tracking error. We rigorously show that, even in the challenging
case of ISO exploration, Neural-Rendezvous provides 1) a high probability
exponential bound on the expected spacecraft delivery error; and 2) a finite
optimality gap with respect to the solution of model predictive control, both
of which are indispensable especially for such a critical space mission. In
numerical simulations, Neural-Rendezvous is demonstrated to achieve a
terminal-time delivery error of less than 0.2 km for 99% of the ISO candidates
with realistic state uncertainty, whilst retaining computational efficiency
sufficient for real-time implementation.

本文提出了一种称为神经约会（Neural-Rendezvous）的基于深度学习的指导和控制框架，使探测任何快速运动的天体尤其是星际物体（ISOs）变得高效且精确，同时提供一种方法在数值仿真中尝试所述方案。