LEO spacecraft task scheduling by AI-based search

This paper explores utility of AI-based search methods in a complex optimization scenario, the optimal allotment of ground station support time to low earth orbiting (LEO) spacecraft with clashing visibilities being the context. Orbiting once every 100 or so minutes at a 800 km height, LEOs now form a critical infrastructure for natural resource management, rescue, crop yield estimation, flood control, communication, and space research and travel support worldwide. The problem is NP-complete. The present methodology exploits the structure of the profit function and constraints and invokes meta-heuristic methods to determine the optimum allocation. The paper solves a practical satellite support optimization problem routinely faced by mission managers. A spin-off of this work is that it can enable the decision maker to also determine optimal ground station locations and support capability deployment in diverse scenarios.