面向GEO目标的快速在线在轨服务任务规划

针对地球同步轨道多对多在轨服务任务规划问题，提出了一种基于拍卖算法和先验知识的改进启发式拍卖算法(IHAA)。首先，建立了基于Lambert变轨的任务规划问题表征，设计了给定变轨时长约束下的快速燃料最优估计策略。其次，针对考虑J 2摄动影响的航天器在轨服务规划问题，提出了一种基于拍卖算法的快速求解流程。其中，为确定每一轮拍卖算法任务列表，提出了基于变轨先验知识的目标优先级排序启发式策略，实现了快速的服务时序确定方法；此外，为减少因部分航天器服务能力低导致的分配失败情况，设计了一种基于失败任务再分配的重拍机制。最后，将所提算法与遗传算法、模拟退火算法进行了对比，结果表明IHAA可以快速给出相对适应度较高的规划结果，可用于突发事件星上在线自主规划，亦可提供优化算法初值以提升规划效率。

Fast On board On orbit Servicing Task Planning for GEO Targets

Based on the auction algorithm and prior knowledge, a novel improved heuristic auction algorithm (IHAA) is proposed to solve the many to many on orbit service mission planning problem in geosynchronous orbit (GEO). Firstly, a representation of the on orbit servicing planning problem based on Lambert transfer is established, and a fast fuel optimal estimation strategy is designed for one to  one servicing. Secondly, for the mission planning problem considering  J 2 perturbation, a fast solution process is proposed based on auction algorithm. Specifically, to determine the task list of each round in the auction algorithm, a heuristic strategy of target priority ranking based on prior knowledge of Lambert transfer is proposed, which helps determine the order of tasks. In addition, in order to reduce the possibility of allocation failure due to the low servicing capacity of some spacecraft, a re auction mechanism based on the redistribution of failed tasks is also added. Finally, the proposed algorithm is compared with the genetic algorithm and simulated annealing algorithm, the results show that IHAA can quickly give relatively high fitness planning results, and can be used for on board mission planning in the face of emergencies or provide initial value of the optimization algorithm to improve the planning efficiency.