基于混合算法的航天器轨道规划方法

Spacecraft orbit planning method based on a hybrid algorithm

摘要:

空间在轨服务过程中，当目标航天器周围有若干小卫星环绕时，服务航天器要避开小卫星的安全范围，与目标航天器成功交会并进行在轨服务，航天器的机动轨道规划是其重要前提。在路径规划中，遗传算法应用广泛，但是求解实际问题的时间容易受到染色体基因等算子数目的影响，求解效率未得到保证。提出了一种混合遗传算法，将遗传算法全局搜索能力和模拟退火算法较强的局部搜索能力进行整合，以服务航天器机动轨道的路径安全、任务时间、燃料消耗、总路程等为约束条件，并对算子进行特殊设计，规划出最优机动轨道路径。通过场景假设和仿真实验证明，该混合遗传算法能够规划出符合约束条件的最优机动轨道路径，并且极大提高了求解效率。

Abstract:

In the process of space on-orbit service, when there are several small satellites around the target spacecraft, the serving spacecraft must avoid the safety range of the small satellites, successfully rendezvous with the target spacecraft, and perform on-orbit services. The maneuvering orbit planning of the service spacecraft is an important indicator. Genetic algorithm has a wide range of applications in path planning, but the time to solve actual problems is easily affected by the number of operators such as chromosome genes, and the solution efficiency cannot be guaranteed. A hybrid genetic algorithm is proposed, which integrates the global search capability of the genetic algorithm and the strong local search capability of the simulated annealing algorithm and taking the path safety, mission time, fuel consumption, total distance, etc. of the maneuvering orbit serving the spacecraft as constraints, and special design of the operator to plan the optimal maneuvering orbit path. Through scenario assumptions and simulation experiments, it is proved that the hybrid genetic algorithm can plan the optimal maneuver orbit path that meets the constraints, and greatly improves the efficiency of the solution.