Interstellar objects (ISOs) 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 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 loss function directly penalizing the MPC state trajectory tracking error. We show that Neural-Rendezvous provides a high probability exponential bound on the expected spacecraft delivery error, the proof of which leverages stochastic incremental stability analysis. In particular, it is used to construct a non-negative function with a supermartingale property, explicitly accounting for the ISO state uncertainty and the local nature of nonlinear state estimation guarantees. In numerical simulations, Neural-Rendezvous is demonstrated to satisfy the expected error bound for 100 ISO candidates. This performance is also empirically validated using our spacecraft simulator and in high-conflict and distributed UAV swarm reconfiguration with up to 20 UAVs.

翻译：摘要：星际天体（ISOs）很可能是原始物质的代表，对于理解系外行星系统具有不可估量的价值。然而，由于其轨道约束较差，通常具有高倾角和相对速度，因此使用传统的人机交互方法探索ISOs极具挑战性。本文提出了一种基于深度学习的制导与控制框架——神经交会（Neural-Rendezvous），能够实时、鲁棒、精确且自主地遭遇包括ISOs在内的快速移动目标。该框架在由谱归一化深度神经网络建模的制导策略之上采用逐点最小范数跟踪控制，其超参数通过直接惩罚模型预测控制（MPC）状态轨迹跟踪误差的损失函数进行调整。我们证明神经交会能够以高概率给出航天器期望交付误差的指数界，其证明利用了随机增量稳定性分析。具体而言，通过构造一个具有超鞅性质的非负函数，明确考虑ISO状态不确定性及非线性状态估计保证的局部特性。数值模拟表明，神经交会满足100个ISO候选体的期望误差界。该性能还通过我们的航天器模拟器以及多达20架无人机的分布式无人机蜂群高冲突重配置实验得到实证验证。