Augmenting spacecraft maneuver strategy optimization for detection avoidance with competitive coevolution

Jason A. Reiter, David B. Spencer

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


Optimization in the spacecraft detection avoidance problem is computationally cost prohibitive given the size of the state and action spaces available to both players. Competitive coevolution can be used to augment strategy optimization by reinforcement learning in a manner that results in dynamic search spaces. An evading spacecraft and a pursuing sensor compete directly with each other and reciprocally drive one another to increasing levels of performance and complexity. This is accomplished by gradually increasing the size and complexity of the strategies. Using coevolution provides significant computational cost savings compared to traditional optimization methods while ensuring a globally optimal result. It is found using competitive coevolution that a spacecraft is able to successfully evade a tasked sensor nearly half of the observation window time with only one 19m/s maneuver every 5days.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2020 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105951
StatePublished - 2020
EventAIAA Scitech Forum, 2020 - Orlando, United States
Duration: Jan 6 2020Jan 10 2020

Publication series

NameAIAA Scitech 2020 Forum
Volume1 PartF


ConferenceAIAA Scitech Forum, 2020
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering


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