Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumption

Abstract-- This paper presents a Hierarchical Reinforcement Learning (HierRL) methodology tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO). Integrating this mechanism creates a safe and fault-tolerant system for CubeSat task scheduling. I. INTRODUCTION CubeSats have transformed the space industry, providing a cost-effective and efficient way to conduct diverse space A rising focus is on equipping involves a constrained optimization [8]. However, the inherent spacecraft with advanced autonomous decision-making uncertainties and complexities of space environments, capabilities [3, 4]. Achieving this relies on using automated combined with task variability and unpredictability, often planning tools to reduce human involvement and effectively surpass the capabilities of traditional tools [9]. Implementing on-board planning mechanisms in spacecraft missions brings One promising solution gaining attention involves substantial benefits, including increased spacecraft applying artificial intelligence to dynamic task scheduling availability, heightened reliability, and reduced ground [10].