空间目标协同探测轨迹优化研究进展  

Research Progress on Trajectory Optimization of Space Target Cooperative Detection

作　　者：张众 宝音贺西[1] 李俊峰[1] ZHANG Zhong;BAOYIN Hexi;LI Junfeng(School of Aerospace Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]清华大学航天航空学院,北京100084

出　　处：《深空探测学报(中英文)》2025年第1期3-14,共12页Journal Of Deep Space Exploration

基　　金：国家自然科学基金(12372047);国家重点研发计划(2020YFC2201200)。

摘　　要：面对未来大规模、复杂化、智能化航天任务发展趋势,针对空间目标协同探测任务中面临的轨迹优化难题,回顾了航天器轨迹优化的主要技术方法,包括最优控制方法、智能优化方法,以及机器学习方法的研究现状与进展。在此基础上,进一步探讨了以空间碎片清理、卫星对地观测、小天体探测、空间在轨服务等为代表的轨迹优化场景、任务设计问题与优化特性。最后,分析了现有研究存在的挑战,并展望了轨迹优化的设计需求,以期为未来复杂航天任务提供新的技术视角和解决方案。In response to the development trend of large-scale,complex,and intelligent space missions,this paper addressed the trajectory optimization challenges faced in space target cooperative exploration missions by reviewing the principal technological methods of spacecraft trajectory optimization.These methods include the current state of research and advancements in optimal control methods,intelligent optimization methods,and machine learning approaches.Based on this,the paper further explored trajectory optimization scenarios,mission design issues,and optimization characteristics represented by space debris removal,Earth observation satellites,small body exploration,and in-orbit servicing.Finally,the paper discussed the existing research challenges and anticipated the design requirements for trajectory optimization,aiming to provide new technological perspectives and solutions for future complex space missions.

关 键 词：空间协同探测 轨迹优化 最优控制 智能优化 机器学习 

分 类 号：V412.4[航空宇航科学与技术—航空宇航推进理论与工程]