Spacecraft detection avoidance maneuver optimization using reinforcement learning

Jason A. Reiter, David B. Spencer, Richard Linares

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationSpaceflight Mechanics 2019
EditorsFrancesco Topputo, Andrew J. Sinclair, Matthew P. Wilkins, Renato Zanetti
PublisherUnivelt Inc.
Number of pages15
ISBN (Print)9780877036593
StatePublished - Jan 1 2019
Event29th AAS/AIAA Space Flight Mechanics Meeting, 2019 - Maui, United States
Duration: Jan 13 2019Jan 17 2019

Publication series

NameAdvances in the Astronautical Sciences
ISSN (Print)0065-3438


Conference29th AAS/AIAA Space Flight Mechanics Meeting, 2019
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science


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