Deep Reinforcement Learning approach for Small Bodies Shape Reconstruction Enhancement

This paper tackles Autonomous Small Bodies Exploration (ASBE), i.e. the problem of mapping an unknown comet or asteroid while autonomously navigating in its proximity. A Deep Reinforcement Learning (DRL) based planning policy is designed to increase mapping efficiency with a smart autonomous selection of the images acquisition times. Such policy is verified with an Image Processing algorithm that generates a point cloud of the body shape model, based on the acquired images, via feature detection and matching.