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Neural-Rendezvous: Learning-based Robust Guidance and Control to Encounter Interstellar Objects

Tsukamoto, Hiroyasu and Chung, Soon-Jo and Donitz, Benjamin and Ingham, Michel and Mages, Declan and Nakka, Yashwanth Kumar (2022) Neural-Rendezvous: Learning-based Robust Guidance and Control to Encounter Interstellar Objects. . (Unpublished)

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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.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper
Tsukamoto, Hiroyasu0000-0002-6337-2667
Chung, Soon-Jo0000-0002-6657-3907
Ingham, Michel0000-0001-5893-543X
Mages, Declan0000-0002-2783-2144
Nakka, Yashwanth Kumar0000-0001-7897-3644
Additional Information:We thank Stefano Campagnola (NASA JPL) for providing his useful simulation codes utilized in Sec. VII, and thank Julie Castillo-Rogez (NASA JPL), Fred Y. Hadaegh (NASA JPL), and Karen Meech (University of Hawaii), and Robert Jedicke (University of Hawaii) for their insightful inputs and technical discussions. This work is funded by the NASA Jet Propulsion Laboratory, California Institute of Technology.
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Record Number:CaltechAUTHORS:20230316-225917431
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:120119
Deposited By: George Porter
Deposited On:17 Mar 2023 00:14
Last Modified:17 Mar 2023 00:14

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