Task-Driven Three-Layer Distributed Scheduling for Emergency Earth Observation in Large Low-Earth-Orbit Constellations

2026-08-14

AI Summary

This study addresses the conflict between emergency task insertion and routine scheduling in large-scale low Earth orbit constellations by proposing a task-driven, three-layer distributed scheduling framework. By unifying demand representation via geographic grids and constructing temporary satellite clusters, the method employs intra-cluster dual-plan bidding, joint marginal evaluation, and inter-cluster coordination mechanisms. This approach enables efficient emergency task insertion under intermittent communication while minimizing disruption to routine operations. Experimental results demonstrate that the proposed method outperforms existing distributed algorithms in emergency coverage and reduces routine coverage loss by up to 87.7%. Achieving performance comparable to centralized approaches, this work effectively balances emergency response timeliness with overall system stability.

Abstract

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.

Problem

• Dynamic Emergency Observation Scheduling
• LEO Constellation
• Earth Observation
• Distributed Scheduling

Innovation

• Distributed Scheduling
• Emergency Earth Observation
• Task-Driven Coordination
• Dual-Plan Bidding
• LEO Constellation