mlsanityData to 7 Oct 2026

Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

arXiv 2003.06169 ↗First posted 13 Mar 2020astro-ph.IM cs.AI eess.SPcited by 20

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Abstract (arXiv)

Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this paper are thus to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multi-objective function and autonomous model. A detailed literature review from 1997 up to 2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic and machine learning. Finally, we discuss a number of topics worth pursuing in the future.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 2; 2025: 4; 2026: 9.

20 papers.

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  1. 2023Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunitiescited by 8
  2. 2022RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problemcited by 7
  3. 2024Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problemscited by 4
  4. 2021Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portionscited by 3
  5. 2025Towards Realistic Earth-Observation Constellation Scheduling: Benchmark and Methodologycited by 3
  6. 2022Large region targets observation scheduling by multiple satellites using resampling particle swarm optimizationcited by 2
  7. 2025An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problemcited by 2
  8. 2026Scalable Dynamic Distributed Constraint Optimization with Metareasoning and Application to Continual Satellite Operationscited by 2
  9. 2026A Constraint Programming Model for the Super-Agile Earth Observation Satellite Imaging Scheduling Problemcited by 1
  10. 2022An adaptive bi-objective optimization algorithm for the satellite image data downlink scheduling problem considering request splitcited by 0
  11. 2024Earth Observation Satellite Scheduling with Graph Neural Networks and Monte Carlo Tree Searchcited by 0
  12. 2025Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Studycited by 0
8 more
  1. 2025On the Role of AI in Managing Satellite Constellations: Insights from the ConstellAI Projectcited by 0
  2. 2026Efficient Policy Learning with Hybrid Evaluation-Based Genetic Programming for Uncertain Agile Earth Observation Satellite Schedulingcited by 0
  3. 2026Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approachcited by 0
  4. 2026HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Clustercited by 0
  5. 2026Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Schedulingcited by 0
  6. 2026Task-Driven Three-Layer Distributed Scheduling for Emergency Earth Observation in Large Low-Earth-Orbit Constellationscited by 0
  7. 2026Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Schedulingcited by 0
  8. 2026Implicit Q-learning-bootstrapped ant colony optimization for maritime moving-target observation scheduling with agile satellitescited by 0