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This research aims to develop a learning framework for efficiently scheduling remote-sensing tasks under downlink constraints.

July 2, 2026Remote SensingOpen Access

GRIP-MAPPO: Feasibility-Preserving Graph-Relational Insertion-Preference Learning for Downlink-Constrained LEO Remote-Sensing Scheduling

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This research aims to develop a learning framework for efficiently scheduling remote-sensing tasks under downlink constraints.
Proposed the GRIP-MAPPO framework to learn dispatch preferences for remote-sensing tasks.
Utilized simulations with 24–288 satellites across six operational regimes to evaluate performance.
Implemented a scheduler that accurately respects hard constraints during dispatch.
GRIP-MAPPO improved observation completion rates compared to conventional greedy and heuristic methods.
Achieved a higher fraction of transmitted data under the same operational conditions.
Maintained sub-second replanning latency during task scheduling.

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Randomized trial investigates downlink-constrained scheduling in LEO remote-sensing constellations, suggesting improved efficiency.

Zhou et al. (2026) studied this question.

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