This paper deals with the problem of mapping unknown small celestial bodies while autonomously navigating in their proximity with an optical camera. A Deep Reinforcement Learning (DRL) based planning policy is here proposed to increase the surface mapping efficiency with a smart autonomous selection of the images acquisition epochs. Two techniques are compared, Neural Fitted Q (NFQ) and Deep Q Network (DQN), and the trained policies are tested against benchmark policies over a wide range of different possible scenarios. Then, the compatibility with an on-board application is successfully verified, investigating the policy performance against navigation uncertainties.

Deep Reinforcement Learning-based policy for autonomous imaging planning of small celestial bodies mapping

Piccinin, Margherita;Lunghi, Paolo;Lavagna, Michèle
2022

Abstract

This paper deals with the problem of mapping unknown small celestial bodies while autonomously navigating in their proximity with an optical camera. A Deep Reinforcement Learning (DRL) based planning policy is here proposed to increase the surface mapping efficiency with a smart autonomous selection of the images acquisition epochs. Two techniques are compared, Neural Fitted Q (NFQ) and Deep Q Network (DQN), and the trained policies are tested against benchmark policies over a wide range of different possible scenarios. Then, the compatibility with an on-board application is successfully verified, investigating the policy performance against navigation uncertainties.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1191060
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