Dynamic Distributed Constraint Optimization and Metareasoning for Continual, Large-Scale Satellite Operations
Researchers have developed a new framework for managing large constellations of Earth-observing satellites, addressing the challenge of scheduling observations for hundreds of spacecraft. The proposed dynamic distributed constraint optimization problem (DCOSP) formulation integrates scheduling and execution, featuring a novel optimality condition and an exact offline algorithm. To manage resource constraints, the framework incorporates metareasoning to control computation expenditure and introduces the dynamic incremental neighborhood stochastic search (D-NSS) algorithm for efficient online problem repair. Simulations show D-NSS outperforms standard baselines in solution quality, computation time, and message volume, laying the groundwork for a significant in-space demonstration of distributed multi-agent AI. AI
IMPACT Enables more efficient and autonomous operations for large satellite constellations, potentially leading to more sophisticated in-space AI demonstrations.