北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (2): 223-230.doi: 10.13700/j.bh.1001-5965.2020.0277

• 论文 • 上一篇    下一篇

基于CGAPIO的航天器编队重构路径规划方法

华冰, 孙胜刚, 吴云华, 陈志明   

  1. 南京航空航天大学 航天学院, 南京 210016
  • 收稿日期:2020-06-17 发布日期:2021-03-08
  • 通讯作者: 华冰 E-mail:huabing@nuaa.edu.cn
  • 作者简介:华冰,女,博士,副研究员,硕士生导师。主要研究方向:导航制导与控制;孙胜刚,男,硕士研究生。主要研究方向:航天器智能编队技术。
  • 基金资助:
    国家自然科学基金(61973513,61673208)

Path planning method for spacecraft formation reconfiguration based on CGAPIO

HUA Bing, SUN Shenggang, WU Yunhua, CHEN Zhiming   

  1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2020-06-17 Published:2021-03-08

摘要: 针对航天器编队重构的路径规划问题,考虑燃料消耗和碰撞概率等约束条件,以及基本鸽群算法存在的问题,提出一种基于混沌初始化和高斯扰动的自适应鸽群(CGAPIO)算法。为了得到多样性和覆盖性更好的鸽群初始值,采用Tent Map混沌模型进行鸽群初始化操作;在地图和指南针算子阶段,为提高全局搜索能力,引入了自适应的权重因子和学习因子更新个体的位置和速度;在地标算子阶段,为避免算法陷入局部最优,将高斯扰动加入到鸽群中心位置。仿真实验结果表明:CGAPIO算法与基本鸽群算法和粒子群算法相比,提高了全局搜索能力,避免了局部最优,规划得到的路径更加平滑,各航天器碰撞概率较低,编队重构消耗的总燃料至少减少了12%。

关键词: 航天器编队, 路径规划, 鸽群(PIO)算法, 编队重构, 自适应因子

Abstract: Aimed at the path planning problem of spacecraft formation reconfiguration, an Adaptive Pigeon-Inspired Optimization algorithm based on Chaos initialization and Gaussian disturbance (CGAPIO)is proposed.In order to make the initial value of the pigeons more diverse and uniform, the Tent Map chaotic model is used to initialize the pigeons. In the map and compass operator, in order to improve the global search ability, adaptive weight factors and learning factors are introduced to update the individual's position and speed; in the landmark operator, in order to avoid the algorithm falling into the local optimum, the Gaussian disturbance is added to the center of the pigeon population.Simulation experiment results show that the CGAPIO significantly improves the global search ability and avoids the local optimum. The planned path is smoother and has lower collision probability of each spacecraft. The total fuel consumed by the formation reconfiguration is significantly reduced by 12% at least compared with the basic pigeon-inspired optimization algorithm and particle swarm optimization algorithm.

Key words: spacecraft formation, path planning, Pigeon-Inspired Optimization (PIO) algorithm, formation reconfiguration, adaptive factor

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