Reinforcement learning-based ground stations scheduling algorithm for LEO satellites

Chen Weihong; Xiao Bowen; Sun Jicheng; Wang Zheng; Guo Congcong; Li Shichao

doi:10.1117/12.2645148

23 November 2022 Reinforcement learning-based ground stations scheduling algorithm for LEO satellites

Chen Weihong, Xiao Bowen, Sun Jicheng, Wang Zheng, Guo Congcong, Li Shichao

Proceedings Volume 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022); 1230244 (2022) https://doi.org/10.1117/12.2645148
Event: Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 2022, Guangzhou, China

Abstract

With the rapidly increasing number and movement of satellites, the ground stations scheduling problem for Low Earth Orbit (LEO) satellites becomes more complex due to its over-constraint nature. In this paper, we propose a novel ground stations scheduling algorithm named Reinforcement Learning-Based Ground Stations Scheduling Algorithm (RLBGSSA). Its batch tasks planning branch (RLBGSBSA) ensures ground stations efficiently track satellites and achieve timeliness. Under the state constraints, the individual task planning branch of RLBGSSA (RLBGSISA) generates the feasible planning combinations based on reinforcement learning, and gains the combination constraints to filter the planning combinations. To maintain the stability of tasks and equipment, the optimal combination among the screened results is selected via a designed value function, which can achieve minimal changes to planned tasks. Furthermore, RLBGSBSA can reset the scheduling of ground stations to void planning overflow, when the number of individual tasks newly added is too large. Finally, the rationality and effectiveness of our algorithm are verified by numerical simulation.

Citation Download Citation

Chen Weihong, Xiao Bowen, Sun Jicheng, Wang Zheng, Guo Congcong, and Li Shichao "Reinforcement learning-based ground stations scheduling algorithm for LEO satellites", Proc. SPIE 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 1230244 (23 November 2022); https://doi.org/10.1117/12.2645148

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