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Abstract

Oxcav has an ongoing collaboration with the European Space Agency (ESA) that involves applying Reinforcement Learning (RL) algorithms to a satellite called OPS-SAT, which has been launched in 2019 and is a flying laboratory that allows ESA’s partners to test and validate new techniques (more information can be found at https://www.esa.int/Enabling_Support/Operations/OPS-SAT). This project aims at designing controllers that will be used to perform nadir pointing and sun-tracking of OPS-SAT, while meeting some specifications (e.g., admissible nadir pointing errors). The focus will be on data-driven methods that leverage available sensors (gyroscopes, GPS, fine sun sensor, magnetometer) and actuators data using a RL architecture to come up with a safe policy that can yield an adequate performance. The main tasks of the project will consist in (1) exploring an ESA platform called MUST to collect all the necessary data and (2) implementing a RL scheme that will be later deployed in the satellite. Throughout the project you will have the opportunity to work with state-of-the-art data-driven techniques that have been developed at Oxcav, under the supervision of Prof. Alessandro Abate and Dr. Licio Romao.