@inproceedings{49817d848872486294bdf4864c62eb62,
title = "Spacecraft detection avoidance maneuver optimization using reinforcement learning",
abstract = "Spacecraft maneuvers are planned with operational objectives in mind, usually ranging from making up for orbit perturbations to maneuvering to avoid a possible collision. Though these areas have been researched in depth, little work has been done exploring maneuvers performed to avoid detection by sensors. This paper explores the optimization of detection avoidance maneuvers using reinforcement learning. Numerical transcription is used for comparison purposes, but the open-loop nature of optimal control is not conducive to solving the entirety of the detection avoidance problem. Reinforcement learning produces reliable results for maneuver optimization which will provide a unique alternative for maneuver planning.",
author = "Reiter, {Jason A.} and Spencer, {David B.} and Richard Linares",
note = "Publisher Copyright: {\textcopyright} 2019, Univelt Inc. All rights reserved.; 29th AAS/AIAA Space Flight Mechanics Meeting, 2019 ; Conference date: 13-01-2019 Through 17-01-2019",
year = "2019",
language = "English (US)",
isbn = "9780877036593",
series = "Advances in the Astronautical Sciences",
publisher = "Univelt Inc.",
pages = "3055--3069",
editor = "Francesco Topputo and Sinclair, {Andrew J.} and Wilkins, {Matthew P.} and Renato Zanetti",
booktitle = "Spaceflight Mechanics 2019",
address = "United States",
}