基于协同进化的多平台联合对地观测优化调度
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摘要
在分析了卫星与无人机在执行观测与资源调度上的特性差异基础上,建立了多平台联合对地观测调度问题的数学模型,提出了多平台协同进化调度算法(MPCCPSA)进行求解。MPCCPSA采用分层式协同进化架构解决了不同类型观测方案统一调度生成问题。根据不同类型平台使用特性以及观测目标集合特点,采用分治-合作策略将其分解分配到各平台,顶层的交叉、变异操作保证各种群的多样性,底层的分治、合作算子保证卫星与无人机之间保持观测能力动态互补,在确保可行解的前提下加快收敛速度。仿真实验表明该方法能够有效解决空-天基多类型平台联合观测优化调度问题。
Based on the analysis of the features and differences between satellite and UAV in earth observation and scheduling,a mathematical model was presented to formulate the scheduling problem for the multi-platforms collaborative observation,and a multi-platforms cooperative evolutionary planning and scheduling algorithm(MPCCPSA) was proposed to solve this problem.MPCCPSA uses co-evolution framework for solving the different platforms' observing plans generated in a unified manner.Based on the divide-cooperation strategy and different characteristics of platforms,the observed targets were allocated to each platform.On the top-level,crossover and mutation operations ensured the diversity of the various groups,and the underlying divide-cooperation operators ensured the dynamic complementarity between different platforms.Simulation results show that this method can effectively solve the multi space-aeronautics collaborative observation tasks scheduling problem.
Based on the analysis of the features and differences between satellite and UAV in earth observation and scheduling,a mathematical model was presented to formulate the scheduling problem for the multi-platforms collaborative observation,and a multi-platforms cooperative evolutionary planning and scheduling algorithm(MPCCPSA) was proposed to solve this problem.MPCCPSA uses co-evolution framework for solving the different platforms' observing plans generated in a unified manner.Based on the divide-cooperation strategy and different characteristics of platforms,the observed targets were allocated to each platform.On the top-level,crossover and mutation operations ensured the diversity of the various groups,and the underlying divide-cooperation operators ensured the dynamic complementarity between different platforms.Simulation results show that this method can effectively solve the multi space-aeronautics collaborative observation tasks scheduling problem.
引文
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[5]郭华东,刘良云,范湘涛,等.对地观测技术用于汶川和玉树地震灾害的研究[J].高校地质学报,2011,17(1):01-12.GUO Huadong,LIU Liangyun,FAN Xiangtao,et al.Study ofearth observation for disaster reduction in Wenchuan and YushuEarthquakes[J].Geological Journal of China Universities,2011,17(1):01-12.(in Chinese)
[6]Wegener S,Sullivan D,Ambrosia V.New remote sensingtechnologies for resource mangaers[R].Salt Lake City,2006.
[7]United States Departments of Defense.Joint Publication 3-60:Joint Targeting[Z].2007.
[8]陈盈果,刘晓路,陈英武,等.优化多星协同观测的改进广义模式搜索算法[J].国防科技大学学报,2012,34(1):88-93.CHEN Yingguo,LIU Xiaolu,CHEN Yingwu,et al.Usingimprovement generalized pattern search to optimize designmulti-satellites'cooperated observation[J].Journal of NationalUniversity of Defense Technology,2012,34(1):88-93.(in Chinese)
[9]王冲,景宁,李军,等.一种基于多Agent强化学习的多星协同任务规划算法[J].国防科技大学学报,2011,33(1):53-58.WANG Chong,JING Ning,LI Jun,et al.An algorithm ofcooperative multiple satellites mission planning based on multi-agent reinforcement learning[J].Journal of National Universityof Defense Technology,2011,33(1):53-58.(in Chinese)
[10]田菁.多无人机协同侦察任务规划问题建模与优化技术研究[D].长沙:国防科学技术大学,2007.TIAN Jing.Modeling and optimization methods for multi-UAVcooperative reconnaissance mission planning problem[D].Changsha:National University of Defense Technology,2007.(in Chinese)
[11]Thomas M H.Asynchronous,distributed optimization for thecoordinated planning of air and sapce assets[D].Massachusetts Institute of Technology,2010.
[12]Jackson S E.Planning coverage of points of interest viamultiple imaging surveillance asset:a multi-modal proach[D].Air University,2003.
[13]Morris R A,Dungan J L,Bresina J L.Space missionchallenges for information technology[C].SMC-IT.SecondIEEE International Conference,2006:101-108.
[14]Verfaillie G,Bensana E,Bataillie N.Exact&Inexactmethods for daily management of earth observation satellite[C]//Proceedings of the 4th International Symposium onSpace Mission Operations and Ground Data Systems,Germany,European Space Agency,1996.
[15]Potter M A,De K A Jong.Cooperative coevolution:Anarchitecture for evolving coadapted subcomponents[J].Evolutionary Computation,2000,8(1):1-29.
[16]Martin W,Savelsbergh P.The vehicle routing problem withtime windows:minimizing route duration[J].INFORMSJournal on Computing,1992,4(2):146–154.
[17]Siwate R.Parallel machine scheduling with time windows[D].University of Texas,2003.
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