Future planetary exploration missions investigating habitability and potential life on distant bodies (e.g., Titan, Enceladus) will face communication constraints with limited transfer rates and short communication windows. Operations of existing missions such as to Mars rely heavily on ground-in-the-loop interactions that may not be suitable for such remote targets [1]. To address this, new missions will require greater autonomy to achieve their desired science return [2].

Our research leverages machine learning (ML) and data science techniques to enable science autonomy onboard space missions [3]. Science autonomy would enable a spacecraft to make closed-loop scientific decisions in situ without the need for constant communication with Earth’s science operations teams. It has the potential to greatly enhance decision-making speed, the level of science return, and the overall efficiency of the mission. This paper discusses the current progress and future roadmap for integrating ML and data science in planetary missions to achieve science autonomy.