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

Online scheduling for low-Earth-orbit (LEO) remote-sensing constellations must coordinate short observation windows, sequence-dependent slews, finite onboard resources, intermittent downlink contacts, and rolling command commitment. We study an acquisition-driven downlink-constrained observation problem in which retained observations carry the primary mission value, while their generated data must remain storable until transmitted. This formulation targets observation-centric missions where downlink supports storage feasibility and future acquisition capability; missions with explicit delivery-value or latency requirements can include downlink completion and delivery delay directly in the objective. The central difficulty is suffix-dependent feasibility: a single locally attractive insertion can cascade into downstream infeasibility through altered slew margins, resource levels, and downlink opportunities. We propose Graph-Relational Insertion-Preference Multi-Agent Proximal Policy Optimization (GRIP-MAPPO), a feasibility-preserving learning-to-dispatch framework comprising a relation-aware satellite–task–ground encoder, orbital-group actors that output bounded insertion-preference vectors, and a deterministic scheduler that enforces hard constraints via insertion, repair, and rollback. Thus, GRIP-MAPPO learns dispatch preferences rather than direct task, satellite, or window assignments, while hard-constraint satisfaction remains in an auditable scheduler interface. Controlled benchmark simulations over 24–288 satellites and six operating regimes show that GRIP-MAPPO improves observation completion and transmitted-data fraction over greedy, heuristic, and graph-based online baselines in the simulated setting, while retaining sub-second replanning latency at reference scale.