Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Engineering & Applied Sciences · Depth: Expert, extended

Summary

A new method, Conservative Constraint Acquisition (CCA) embedded in the Learn&Optimize (L&O) framework, addresses Earth Observation (EO) satellite scheduling where operational constraints are unknown and hidden behind a binary oracle. Traditional EO scheduling assumes fully specified constraint models, but in practice, constraints like observation separation, power budgets, and thermal limits are often implicit in engineering artifacts or high-fidelity simulators. This research focuses on a simplified model with pairwise separation and global capacity constraints. L&O, which interleaves constraint acquisition with CP-SAT optimization, significantly improves over a no-knowledge greedy baseline, reducing the average gap from 65-68% to 17.7-35.8% for instances with up to 30 tasks. For 50 tasks, L&O achieves a 17.9% gap, outperforming the acquire-then-solve (FAO) baseline (20.3%) while using 21.3 main oracle queries instead of 100 and approximately 5 times less execution time.

Key takeaway

For AI Scientists and Research Scientists developing scheduling systems where operational constraints are implicit or dynamic, consider adopting an active constraint acquisition approach. Your team can achieve significant performance gains and reduce computational time by interleaving constraint learning with optimization, rather than a two-phase acquire-then-solve strategy. Focus on identifying critical constraints that steer the solver towards feasible solutions, as full model recovery is often unnecessary for optimal outcomes.

Key insights

Interleaving constraint acquisition with optimization efficiently solves EO satellite scheduling under unknown operational constraints.

Principles

• Feasibility can be learned interactively from a binary oracle.
• Exact constraint recovery is not required for optimal solutions.
• Early termination improves efficiency over full acquisition.

Method

Conservative Constraint Acquisition (CCA) refines a constraint model by querying an oracle with rejected schedules, binary-searching for justified separation gaps, or identifying violated capacity candidates, then prunes dominated candidates from the basis.

In practice

• Use CP-SAT as a time-limited anytime solver for OptSol.
• Prioritize learning constraints that rule out strong competing assignments.
• Implement early termination when an oracle-accepted proposal is found.

Topics

• Earth Observation Scheduling
• Constraint Acquisition
• Unknown Operational Constraints
• Learn&Optimize Framework
• Conservative Constraint Acquisition

Best for: AI Scientist, Research Scientist, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.