Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects
arxiv(2022)
摘要
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.
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