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航天器任务调度模型、算法与通用求解技术综述

杜永浩 邢立宁 姚锋 陈盈果

杜永浩, 邢立宁, 姚锋, 陈盈果. 航天器任务调度模型、算法与通用求解技术综述. 自动化学报, 2020, 46(x): 1−27 doi: 10.16383/j.aas.c190656
引用本文: 杜永浩, 邢立宁, 姚锋, 陈盈果. 航天器任务调度模型、算法与通用求解技术综述. 自动化学报, 2020, 46(x): 1−27 doi: 10.16383/j.aas.c190656
Du Yong-Hao, Xing Li-Ning, Yao Feng, Chen Ying-Guo. survey on models, algorithms and general techniques for spacecraft mission scheduling. Acta Automatica Sinica, 2020, 46(x): 1−27 doi: 10.16383/j.aas.c190656
Citation: Du Yong-Hao, Xing Li-Ning, Yao Feng, Chen Ying-Guo. survey on models, algorithms and general techniques for spacecraft mission scheduling. Acta Automatica Sinica, 2020, 46(x): 1−27 doi: 10.16383/j.aas.c190656

航天器任务调度模型、算法与通用求解技术综述

doi: 10.16383/j.aas.c190656
基金项目: 国家自然科学基金(61773120, 61873328), 国家杰出青年科学基金(61525304), 高等学校全国优秀博士学位论文作者专项资金(2014-92), 国家建设高水平大学公派研究生项目(201903170181), 湖南省研究生科研创新项目(CX2018B022)资助
详细信息
    作者简介:

    杜永浩:国防科技大学系统工程学院博士研究生. 2017年获国防科技大学硕士学位. 主要研究方向为智能优化理论、方法与应用. E-mail: duyonghao15@163.com

    邢立宁:国防科技大学系统工程学院研究员, 全国优秀博士学位论文获得者, 入选教育部新世纪优秀人才计划, 湖南省自然科学杰出青年基金获得者. 2009 年获国防科技大学博士学位. 主要研究方向为智能优化理论、方法与应用. 本文通讯作者. E-mail: xinglining@gmail.com

    姚锋:国防科技大学系统工程学院研究员. 2013年获国防科技大学博士学位. 主要研究方向为智能优化理论、方法与应用. E-mail: yaofeng@nudt.edu.cn

    陈盈果:国防科技大学系统工程学院副教授. 2014年获国防科技大学博士学位. 主要研究方向为智能优化理论、方法与应用. E-mail: argguo@163.com

    通讯作者:

    国防科技大学系统工程学院研究员, 全国优秀博士学位论文获得者, 入选教育部新世纪优秀人才计划, 湖南省自然科学杰出青年基金获得者. 2009 年获国防科技大学博士学位. 主要研究方向为智能优化理论、方法与应用. 本文通讯作者. E-mail: xinglining@gmail.com

Survey on Models, Algorithms and General Techniques for Spacecraft Mission Scheduling

Funds: Supported by National Natural Science Foundation of China (61773120, 61873328), National Science Fund for Distinguished Young Scholars (61525304), National Excellent Doctoral Dissertation Foundation of China (2014-92), State Scholarship Fund of the China Scholarship Council (201903170181), and Hunan Postgraduate Research Innovation Project (CX2018B022)
More Information
    Corresponding author: XING Li-Ning Professor at the College of Systems Engineering, National University of Defense Technology. He was awarded with the National Excellent Ph. D. Dissertation of China and the New Century Excellent Researcher of Ministry of Education. He is also supported by the Natural Science Funds for Distinguished Young Scholar of Hunan Province. He received his Ph. D. degree from National University of Defense Technology in 2009. His research interest covers intelligent optimization theory, method, and application. Corresponding author of this paper
  • 摘要: 针对航天器任务调度大规模、复杂化的新常态和灵活组网、快速响应的新要求, 综述了航天器任务调度模型、算法与通用求解技术的发展现状. 首先, 基于遥感卫星、通讯中继卫星、导航卫星和航天测控等航天器任务, 从任务排序模型和时间窗口分配模型两个角度出发, 揭示了不同航天器任务调度模型的决策形式和共性特征, 阐明提升模型兼容性、适用性的必要性. 其次, 基于启发式算法、精确求解算法和元启发式算法, 探讨了航天器任务调度算法的适用模型与编码特色, 指明“算法-模型”解耦、算法深度融合的重要性. 在此基础上, 介绍了CPLEX、STK/Scheduler、Europa2和“高景一号”任务调度分系统等航天器任务调度通用求解技术的模型、算法与主要功能, 说明我国自主研发通用求解技术的必要性和新的应用思路. 最后, 指出了开发航天器任务调度统一化建模语言、打造算法库与测试集等未来航天器任务调度研究的新方向.
  • 图  1  遥感卫星任务排序模型示例. (a) 双星VRP模型. (b) 单星图论模型. (c) 基于最早开始原则的双星JSP模型

    Fig.  1  Illustrations of mission-sequencing models for remote sensing satellites. (a) A VRP model for two satellites. (b) A graph-based model for single satellite. (c) A JSP model for two satellites based on the earliest-beginning principle

    图  2  遥感卫星VTW分配模型示例. (a) 非敏捷卫星VTW分配模型. (b) 敏捷卫星VTW分配模型

    Fig.  2  Illustrations of VTW-allocation models for remote sensing satellites. (a) A model for non-agile satellites. (b) A model for agile satellites

    图  3  敏捷遥感卫星任务离散VTW分配模型示例

    Fig.  3  An illustration of the discrete VTW-allocation model for agile remote sensing satellites

    图  4  通讯中继卫星任务调度问题与遥感卫星任务调度问题的类比. (a) 基于卫星上行链路的通讯中继任务. (b) 基于卫星下行链路的通讯中继任务

    Fig.  4  A comparison between the mission scheduling of communication relay satellites and remote sensing satellites. (a) Uplink-based missions. (b) Downlink-based missions

    图  5  航天器测控任务调度示例. (a) 地基高轨、低轨航天器测控任务. (b) 天基高轨、低轨航天器测控任务

    Fig.  5  Illustrations of spacecraft tracking mission scheduling. (a) Land-based high-orbit and low-orbit tracking missions. (b) Space-based high-orbit and low-orbit tracking missions

    图  6  航天器任务调度模型主要参数关系的一种类图表示(以遥感卫星为例)

    Fig.  6  A class diagram of the relationships among the parameters in spacecraft mission scheduling (exampled by remote sensing satellites)

    图  7  遗传算法求解航天器任务调度问题编码示例. (a) 任务排序模型的编码形式. (b) VTW分配模型的编码形式

    Fig.  7  Illustrations of the GA encoding for spacecraft mission scheduling. (a) The encoding of a mission-sequencing model. (b) The encoding of a VTW-allocation model

    图  8  操作界面示例. (a) 场景(目标与资源)建模界面. (b) 资源属性设置界面

    Fig.  8  A view of the STK/Scheduler. (a) The scenario (targets and resources) modeling. (b) The resource parameter setting

    表  1  航天器任务调度模型研究现状总结

    Table  1  A summary of the studied models for spacecraft mission scheduling

    模型类型模型子类编码过程解码过程模型优缺点常见约束极少考虑的业务约束
    面向资源的任务排序模型VRP模型决策变量表示资源执行任务的顺序规则与约束决定开始时间, 解码后为可行解1. 表示了任务顺序, 便于检查任务顺序和转换时间约束
    2. 在“顺序回放”工作机制下同时决策成像与数传序列
    3. 不便于描述长VTW的任务调度问题与复杂约束
    4. “最优序列”不一定等于“最优结果”, 约束越复杂, 解码过程越可能丢失优质解, 解码的时间成本也越高
    1. 任务执行唯一性约束
    2. 任务可见性约束(即时、空、频域VTW)
    3. 任务顺序约束(如条带拼接、立体成像、顺序回放等)
    4. 任务工作模式约束(如遥感卫星记录、回放、实传模式与录放比约束)
    5. 资源能力约束(如卫星电池、固存容量、任务次数; 测控与数传站负载、工作频率、时长约束等)
    6. 资源转换时间约束
    7. 资源可抢占性与非抢占性约束(如测控任务相控阵等待期可执行其他任务)
    1. 非线性、函数化、过程依赖的资源能力、转换时间约束(如遥感卫星单轨成像时限与该轨侧摆次数、最小摆角、是否实传等相关)
    2. 区分连续、非连续任务的资源能力、转换时间约束(如“高景一号”单轨连续成像次数、时间限制)
    3. 区分阳照条件的资源能力、转换时间约束(如地影区中继卫星工作时限)
    4. 区分待机、工作等的资源能力、转换时间约束(如载荷开关机、执行任务时限)
    5. 单载荷资源模式切换约束(如“北斗”地面站时间同步与数据传输模式的切换)
    6. 多载荷资源模式切换约束(如“资源一号”可见光与高光谱模式的切换)
    7. 遥感卫星基于“整块擦除”的固存管理约束
    图论模型
    JSP模型
    优先级排序模型通过决策变量表示任务分配资源的顺序
    面向任务的VTW分配模型基于规则的分配模型通过0-1变量表示任务的VTW基于规则与约束决定开始时间, 解码后为可行解1. 直接表达了任务VTW和开始时间, 反映了任务顺序
    2. 不限于“顺序回放”, 可分别调度成像与数传序列
    3. 能够描述长VTW的任务调度问题与复杂约束
    4. 基于规则的分配模型解空间较小, 但也存在解码过程
    5. 多重决策和离散VTW分配模型更加完备, 提升了解的多样性, 但解空间较大
    多重决策的分配模型通过0-1变量和整数变量分别表示VTW与开始时间不检查约束, 解码后可能为不可行解
    离散化VTW分配模型通过0-1变量直接表示离散VTW及开始时间
    下载: 导出CSV

    表  2  航天器任务调度研究中常用算法总结

    Table  2  A summary of commonly used algorithms for spacecraft mission scheduling

    算法类型算法子类常用编码模型算法优点算法不足
    启发式算法优先级排序算法任务排序模型1. 结构简单、运算速度快
    2. 符合人的主观认识, 有经验支撑
    3. 有助于缩减问题解空间, 在与其他算法组合使用过程中提升求解效率
    1. 与问题特征和调度模型紧耦合, 通用化程度不高
    2. 求解效果欠佳, 常用于辅助决策, 很少直接用于调度方案的生成
    冲突消解算法以任务排序模型为主
    任务分配算法以VTW分配模型为主
    精确算法分支定界算法任务排序或VTW分配模型1. 可以求得最优解, 在不确定性条件下也能保证解的最优性
    2. 算法思想对其他算法有指导意义, 与其他算法有良好的组合应用前景
    1. 只适用于小规模、简单约束问题, 求解大规模、复杂约束问题能力有限
    2. 通常需要较大程度的问题简化
    动态规划算法
    元启发式算法演化算法遗传算法任务排序或VTW分配模型1. 具有隐并行性和多样的解表示能力
    2. 具有出色的全局寻优能力
    3. 与其他算法混合的兼容性好
    4. 任务排序模型的编码形式简洁、操作简单
    1. 局部搜索能力有限
    2. 任务排序模型的编码方式可能丢失优质解
    3. VTW分配模型的编码方式复杂且冗长, 可能影响优化效率
    蚁群算法以任务排序模型为主
    粒子群算法以VTW分配模型为主
    局部搜索禁忌搜索算法任务排序或VTW分配模型1. 原理简单、易于实现、适用性好
    2. 具有出色的局部搜索能力
    3. 具有一定的局部最优逃逸能力
    1. 全局搜索能力有限, 易陷入局部最优
    2. 依赖于邻域结构的设计
    模拟退火算法以VTW分配模型为主
    其他算法模因算法任务排序或VTW分配模型对局部搜索和演化算法取长补短依赖于框架设计, 可能降低迭代效率
    机器学习决策算法以任务排序模型为主简单、快速并且自适应、自学习“来一个决策一个”, 缺乏全局优化性
    下载: 导出CSV

    表  3  用于航天任务调度的通用求解器总结

    Table  3  A summary of the general techniques for spacecraft mission scheduling

    通用求解器常用调度模型求解算法求解器优点求解器不足
    CPLEX线性规划模型表示的任务排序模型或VTW分配模型分支定界算法等精确求解算法1. 语言简洁, 兼容性好
    2. 内置算法丰富, 降低算法设计难度, 同时开放了算法设计与改进接口
    3. 可以求得最优解, 在不确定性条件下也能保证解的最优性
    4. 用户基础大, 版本持续更新
    1. 很难描述非线性约束或收益, 复杂约束线性化难度大
    2. 很难求解大规模任务调度问题
    3. 算法改进对线性规划理论基础要求高
    4. 缺乏航天领域特色
    STK/Scheduler任务排序模型任务排序算法, 冲突消解算法 1. 与STK兼容, 建模简单
    2. 语言简洁, 兼容性好
    3. STK/Scheduler Online具有远程服务功能
    1. 封装程度较高, 难以描述复杂约束, 二次开发难度大
    2. 缺乏动态调整功能
    3. 算法优化能力有限, 已不再更新
    VTW分配模型 任务分配算法, 冲突消解算法
    Europa2通过标记、事务、对象、类、时间线、规划解描述一个规划模型约束传播等约束规划算法1. 面向状态变量和动作序列, 求解过程直观
    2. 约束传播算法有助于动态调度机制的设计
    1. 缺乏收益函数和调度优化机制
    2. 只适用于小规模(单星单轨)任务调度, 已不再更新
    “高景一号”任务调度分系统任务排序模型基于最早开始原则的任务排序算法1. 模型与算法简洁
    2. 可以考虑全部复杂约束
    1. 调度质量有待提高
    2. 动态调整以人工为主, 结果无法实时反馈
    下载: 导出CSV
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