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

2023/09/21 by Mahya Ramezani, M. Amin Alandihallaj, Ramezani, Mahya +5 · 3 citations
Computer Science · Engineering · #Age of Information Optimization #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Satellite Communication Systems #Spacecraft Design and Technology

paper · pdf · doi:10.48550/arxiv.2309.12004

openalex publication_date 2023/09/21 · openalex created_date 2023/09/23 · openalex updated_date 2026/07/28

Abstract

This paper presents a Hierarchical Reinforcement Learning methodology tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO). Incorporating a high-level policy for global task distribution and a low-level policy for real-time adaptations as a safety mechanism, our approach integrates the Similarity Attention-based Encoder (SABE) for task prioritization and an MLP estimator for energy consumption forecasting. Integrating this mechanism creates a safe and fault-tolerant system for CubeSat task scheduling. Simulation results validate the Hierarchical Reinforcement Learning superior convergence and task success rate, outperforming both the MADDPG model and traditional random scheduling across multiple CubeSat configurations.

Cited by

• An Automated Tip-and-Cue Framework for Optimized Satellite Tasking and Visual Intelligence
• MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation
• CubeSat Orbit Insertion Maneuvering Using J2 Perturbation

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