GEO satellites on-orbit repairing mission planning with mission deadline constraint using a large neighborhood search-genetic algorithm

• DOI: 10.48550/arxiv.2110.03878
• Publication Date: 2021-01-01
• Authors: Han, Peng; Guo, Yanning; Li, Chuanjiang; Zhi, Hui; Lv, Yueyong
• Keywords: FOS: Computer and information sciences; 0203 mechanical engineering; FOS: Electrical engineering, electronic engineering, information engineering; Computer Science - Neural and Evolutionary Computing; 02 engineering and technology; Systems and Control (eess.SY); Neural and Evolutionary Computing (cs.NE); Electrical Engineering and Systems Science - Systems and Control

This paper proposed a novel large neighborhood search-adaptive genetic algorithm (LNS-AGA) for many-to-many on-orbit repairing mission planning of geosynchronous orbit (GEO) satellites with mission deadline constraint. In the many-to-many on-orbit repairing scenario, several servicing spacecrafts and target satellites are located in GEO orbits which have different inclination, RAAN and true anomaly. Each servicing spacecraft need to rendezvous with target satellites to perform repairing missions under limited fuel. The mission objective is to find the optimal servicing sequence and orbit rendezvous time of every servicing spacecraft to minimize total cost of all servicing spacecrafts with all target satellites repaired. Firstly, a time-dependent orbital rendezvous strategy is proposed, which can handle the mission deadline constraint. Besides, it is also cost-effective compared with the existing strategy. Based on this strategy, the many-to-many on-orbit repairing mission planning model can be simplified to an integer programming problem, which is established based on the vehicle routing problem with time windows (VRPTW) model. In order to efficiently find a feasible optimal solution under complicated constraints, a hybrid adaptive genetic algorithm combining the large neighborhood search procedure is designed. The operations of "destroy" and "repair" are used on the elite individuals in each generation of the genetic algorithm to enhance local search capabilities. Finally, the simulations under different scenarios are carried out to verify the effectiveness of the presented algorithm and orbital rendezvous strategy, which performs better than the traditional genetic algorithm.