HiPGen: An approach for fast generation of multi-satellite observation plans via a hierarchical multi-channel transformer network

Abstract

Effective and fast Earth Observation Satellite (EOS) scheduling plays an essential role in new generation space-based information services, especially in emergency scenarios. Most existing research regards the EOS scheduling as a combinatorial optimization problem and adopts metaheuristic local search-based methods to solve it in a batch-wise fashion. To the best of our knowledge, no practical scheduling approach has been able to generate an optimized multi-EOS observation plan in an immediate response style. This paper proposes a novel real-time multi-satellite scheduling method consisting of a machine learning-based hierarchical prediction model and a heuristic local search algorithm. Firstly, the hierarchical prediction model based on a stacked multi-channel transformer network can learn from existing historical multi-EOS observation plans and generate a high-quality initial solution for the current scheduling scenario. Then, the local search algorithm based on Random Hill Climbing does further improvement and heuristic constraint handling on the initial solution and generates the final solution. To verify the effectiveness of the proposed method, simulation experiments are carried out. The results show that the method proposed in this paper consumes a short calculation time and has high planning profits compared with the state-of-the-art approaches.