An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem

• URL: http://arxiv.org/abs/2503.04803v1
• Date: Mon, 03 Mar 2025 12:01:27 GMT
• Title: An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem
• Authors: Antonio M. Mercado-Martínez, Beatriz Soret, Antonio Jurado-Navas,
• Abstract summary: The Agile Earth Observation Satellite Scheduling Problem (AEOSSP) involves finding the subset of observation targets to be scheduled along the satellite's orbit.<n>This paper presents a Deep Reinforcement Learning (DRL) approach for addressing the AEOSSP with time-dependent profits.<n>Our results demonstrate that the proposed algorithm reduces the capture of images that fail to meet quality requirements by > 60% and consequently decreases energy waste from attitude maneuvers by up to 78%.
• Score: 5.8700233733489515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Agile Earth Observation Satellite Scheduling Problem (AEOSSP) entails finding the subset of observation targets to be scheduled along the satellite's orbit while meeting operational constraints of time, energy and memory. The problem of deciding what and when to observe is inherently complex, and becomes even more challenging when considering several issues that compromise the quality of the captured images, such as cloud occlusion, atmospheric turbulence, and image resolution. This paper presents a Deep Reinforcement Learning (DRL) approach for addressing the AEOSSP with time-dependent profits, integrating these three factors to optimize the use of energy and memory resources. The proposed method involves a dual decision-making process: selecting the sequence of targets and determining the optimal observation time for each. Our results demonstrate that the proposed algorithm reduces the capture of images that fail to meet quality requirements by > 60% and consequently decreases energy waste from attitude maneuvers by up to 78%, all while maintaining strong observation performance.

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