Title:
Small Body Reconnaissance by Multiple Spacecraft via Deep Reinforcement Learning

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Tomita, Kento
Shimane, Yuri
Ho, Koki
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Abstract
Small-body investigations by spacecraft are one of the most scientifically important space exploration missions. Due to the strong uncertainty of the dynamics around the body, geological surface features, and scientific values of candidate target sites, these missions require dedicated planning and execution from the ground. As a study of automated operations for asteroid investigation, this paper investigates how small-body reconnaissance operations could be performed by multiple spacecraft. By comparing baseline policies with different model parameters and a policy trained via deep reinforcement learning, we discuss the optimal balance of exploration and exploitation for our science model.
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2022-08
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