Satellite Observation and Data-Transmission Scheduling Using Imitation Learning Based on Mixed Integer Linear Programming

The Earth observation satellites (EOSs) scheduling problem is generally considered as a complex combinatorial optimization problem due to various technical constraints. It is significant to develop efficient computational frameworks to solve this problem. In this article, an intelligent EOSs scheduling framework is developed using imitation learning based on mixed integer linear programming (MILP). The scheduling framework is composed of two processes: preprocessing, modeling, and solving process. In the preprocessing process, an analytical method to generate the available time windows of an EOS is derived after considering the effects of Earth's $J_{2}$ perturbation on the elliptic orbit. Based on the preprocessing results, this problem is formulated as an MILP model in the modeling process. In the solving process, a smart algorithm is proposed based on imitation learning for branch-and-bound to accelerate the solving process. Compared with normal imitation learning, a data selection method works in our algorithm to avoid potential misleading for learning. Besides, an iterative view is also adopted to improve the performance of the trained strategy. In the end, several real-world EOSs scheduling scenarios are investigated to demonstrate the reliability and high efficiency of this framework.