Task-Driven Three-Layer Distributed Scheduling for Emergency Earth Observation in Large Low-Earth-Orbit Constellations
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Abstract (arXiv)
Large low-Earth-orbit (LEO) Earth-observation (EO) constellations offer frequent access to geographically dispersed ground targets, but emergency requests may arrive after committed routine-plan execution has begun. The resulting dynamic emergency observation scheduling problem (DEOSP) requires urgent tasks to be inserted under intermittent ground contact without excessive routine-plan disruption. To address DEOSP, we propose a task-driven three-layer distributed scheduling (T3L-DS) method, which represents task demand and sensor footprints on a common geographic grid and forms temporary clusters from observation capabilities and current inter-satellite links. For intra-cluster coordination, T3L-DS introduces onboard dual-plan bidding and joint marginal evaluation. It also designs an inter-cluster coordination mechanism for unresolved demand. Extensive computational experiments compare T3L-DS with centralised simulated annealing (SA), an adapted selective time-variant better reply process (A-SeTVBRP), and a conventional contract-net protocol (CNP). T3L-DS achieves the highest emergency coverage among the distributed methods, with average relative improvements of approximately 2.8% and 17.1% over A-SeTVBRP and CNP, respectively. Its average relative gap from SA is approximately 7.1%. Under conflict-enhanced loads, it reduces routine-coverage loss by approximately 57.9% and 87.7% relative to A-SeTVBRP and CNP, respectively. The ablation study confirms the contribution of the proposed coordination enhancements. Overall, the results show that T3L-DS provides an effective distributed approach to DEOSP.
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References · 8
- 2020Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directionscited by 20
- 2020Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertaintycited by 8
- 2022RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problemcited by 8
- 2020A Maximum Independent Set Method for Scheduling Earth Observing Satellite Constellationscited by 7
- 2022Large region targets observation scheduling by multiple satellites using resampling particle swarm optimizationcited by 2
- 2018Scheduling multiple agile Earth observation satellites with multiple observationscited by 1
- 2020Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resourcescited by 1
- 2022Multi-strip observation scheduling problem for ac-tive-imaging agile earth observation satellitescited by 1
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