A Study on Adaptive Multi-Satellite Mission Allocation Algorithm for Efficient Large-Scale Constellation Planning

This paper addresses the mission planning and scheduling problem for satellite constellations by proposing an Adaptive Multi-Satellite Mission Allocation Algorithm (AMMAA). With the advancement of satellite imaging technology, satellite constellations are facing complex mission planning challenges involving multiple satellites, payloads, and constraints. This paper establishes a mathematical optimization model for mission planning with multiple constraints, including attitude maneuver time, power energy, and storage capacity, and designs a rule-based AMMAA. Through simulation experiments, the effectiveness of the algorithm in maximizing the target coverage rate and minimizing average revisit time is verified. The experimental results show that, in the simulation scenario, the algorithm achieves 100%target coverage, with high-priority targets being observed on average 15.7 times and an average revisit time of 19.15 minutes for all targets. The algorithm not only provides a feasible solution for multi-satellite mission planning problems but also offers a basis for quantitative analysis for the optimization of satellite constellation system schemes.