多星协同观测遗传-演进双层任务规划算法
Genetic-evolutionary bi-level mission planning algorithm for multi-satellite cooperative observation
李阳阳 1罗俊仁 1张万鹏 1项凤涛1
作者信息
- 1. 国防科技大学智能科学学院,湖南长沙 410073
- 折叠
摘要
多星协同任务规划方法是天基卫星系统管控的关键支撑.围绕多星协同对地观测任务展开分析,首先建立多星协同任务规划模型,包括卫星轨道参数、约束条件和待观测 目标点等;其次设计了遗传-演进双层求解架构,将多星任务规划问题拆解为顶层多星任务分配问题和底层单星任务调度问题,上层采用基于引导的多种群遗传算法(multi-population genetic algorithm,MPGA),将启发式结果融入到任务分配算法中,下层采用改进遗传算法对单星任务调度问题进行求解;最后针对适用性问题,设定随机和均匀分布两组目标,采用不同卫星数量设计实验验证了遗传-演进双层求解框架的有效性.
Abstract
Multi-satellite cooperative mission planning method is a key node in the space-based satellite system architecture.Firstly,the multi-satellite cooperative for Earth observation mission is analyzed and a multi-satellite cooperative mission planning model is established,including satellite orbit parameters,constraint conditions,and target points to be observed.Then,a genetic-evolutionary bi-level solution architecture is designed,which decomposes the multi-satellite mission planning problem into a top multi-satellite mission assignment problem and a bottom single-satellite mission scheduling problem.The upper level uses the guided multi-population genetic algorithm(MPGA)to integrate the heuristic results into the task allocation algorithm,and the lower level uses the improved genetic algorithm to solve the single-satellite task scheduling problem.Finally,aiming at the applicability problem,two groups of objectives are set randomly and uniformly distributed,and experiments are designed with different numbers of satellites to prove the effectiveness of the genetic-evolutionary bi-level solution framework.
关键词
卫星任务规划/遗传-演进架构/多种群遗传算法/并行算法Key words
satellite task planning/genetic-evolutionary architecture/multi-population genetic algorithm(MPGA)/parallel algorithm引用本文复制引用
基金项目
国家自然科学基金(U1734208)
湖南省自然科学基金(2021JJ40693)
出版年
2024
NETL
NSTL
万方数据