多UAV协同任务资源分配与编队轨迹优化方法研究
|
摘要
多无人机(Unmanned Aerial Vehicle, UAV)协同任务规划技术是发挥多UAV协同优势的关键之一。然而,战场环境中多UAV系统将面临信息不确定性、计算复杂性、时间紧迫性的严峻挑战。针对上述难点,论文以多平台、多任务、多目标的UAV协同对地打击任务为背景,应用闭环控制系统理论对任务规划问题进行抽象。重点围绕资源分配以及编队轨迹优化两个环节,建立数学模型,研究优化理论,设计求解算法,开展仿真与实验验证。
(1)建立了部分可观条件下的多UAV协同任务动态资源分配模型。给出了多UAV对地打击中的基本任务定义,可描述不同任务的执行效果及时间、资源等属性。在部分可观的马尔可夫决策过程(Partially Observable Markov Decision Processes, POMDP)模型框架下,将待打击的目标集合视为被控系统,以打击任务为系统输入,以评估任务为状态反馈,结合资源的有限性,建立了部分可观条件下的动态系统优化模型。模型揭示了评估与打击两类任务之间的内在联系,能够实现对多UAV系统中火力资源与信息资源的统一调配,可有效反映实际作战过程中任务执行效果以及目标状态信息带有的不确定性与时间延迟,并可克服传统静态分配模型不能适应目标状态动态变化的缺陷。
(2)建立了多UAV松散编队飞行的轨迹优化模型。将多UAV编队飞行过程划分为编队构成与编队保持两个阶段。基于李导数对单UAV运动学方程进行精确线性化,采用图模型描述编队内UAV平台间的通信拓扑。在平台运动学模型与通信拓扑模型的基础上,借鉴动态系统最优控制理论,构建两个阶段的优化模型。针对多UAV编队构成过程,建立了自由终端约束条件下的轨迹优化模型,可通过选择编队汇合点降低总体能量消耗。针对多UAV编队保持过程,定义了平台的个体指标与协同指标,能够兼顾减小能量消耗、跟踪参考航线、保持编队结构等多个控制目标。
(3)从资源分配与编队轨迹优化模型的共性特点出发,对弱耦合动态系统分解优化方法的适应性进行拓展,并给出相应定理及理论证明。剖析了多UAV协同行为与数学模型中耦合现象之间的关联关系,引入基于凸优化理论的分解方法实现问题解耦。针对分布式计算的网络特点,构建有界时延网络的等价无时延增广网络,相应定义增广优化问题,从网络节点信息变迁矩阵的收敛性入手,理论证明了在局部连接、拓扑可变以及带有时间延迟的网络环境中,异步并行次梯度算法的一致性和收敛性。针对系统状态部分可观的弱耦合随机动态规划问题,基于带约束的POMDP值迭代公式定义对偶函数,证明子系统初始信念状态独立的条件下,对偶函数的可分离性,采用对偶分解法实现问题解耦,并给出对偶函数次梯度的构造方法。
(4)提出了离线优化与在线决策相结合的动态资源分配算法。基于无限阶段POMDP建立理想条件下对单目标的动态资源指派模型,证明了最优平稳策略的性质,据此将模型转化为非线性整数规划问题,并设计求解算法。基于有限阶段POMDP建立一般条件下对单目标的动态资源指派模型,提出了改进的线性支撑算法,并证明了算法精度的可控性。将对多目标的动态资源分配过程划分为离线优化和在线决策两个阶段。离线优化阶段采用对偶分解法实现问题解耦,得到多个对单目标的POMDP子问题,并通过资源成本协调子问题最优策略的资源期望使用量。在线决策阶段综合考虑离线阶段所得资源指派策略与实际信息,基于贪婪策略确定对各目标的任务。仿真结果表明,该方法可有效克服目标状态信息不准确所带来的不利影响,能够满足实时决策的需求。
(5)提出了基于协商的分布式多UAV编队轨迹优化算法。针对编队构成轨迹优化模型变量耦合,以及编队保持轨迹优化模型指标耦合的特点,分别采用原始分解法与间接分解法实现问题解耦。证明了分解后协调层主问题指标函数的性质,给出其次梯度的构造方法,并设计了基于协商的分布式轨迹优化算法。编队构成阶段中,各UAV平台独立求解固定终端状态下的最优轨迹,得到对自身有利的汇合点变化方向,更新期望的汇合点并发送至相邻平台。经过反复协商,各平台提出的汇合点趋于一致,并收敛至最优解。编队保持阶段中,各UAV平台在对偶变量的控制下独立求解自身最优轨迹,更新并向相邻平台发送对偶变量。经过反复协商,各平台在优化局部指标的同时,实现对编队几何结构的保持。仿真与飞行实验验证结果表明,该方法具有良好的计算效率与优化能力,在给定指标函数下可显著提升性能,能够有效支持多UAV编队轨迹优化设计。
Mission planning for multiple Unmanned Aerial Vehicles (UAV) cooperative operation is one of the focuses to exert the advantages of cooperation of multiple UAVs. But multi-UAV systems in battle fields have to confront the uncertainty of information, the complexity of mathematical model, and the pressure of computing time. Under the background of multiple UAVs cooperative ground attack mission including multiple UAVs, multiple missions, and multiple targets, this dissertation applies closed-loop control system theory to abstracting of mission planning problem, focuses on the resource allocation problem and formation trajectory optimization problem, builds mathematical model, researches optimization theory, designs algorithm, carries out simulation and experiments.
(1) The dynamic resources allocation model for multiple UAVs cooperative mission with partially observable targets states is presented. The basic tasks in multiple UAVs ground attack mission are defined, so as to describe the execution effects, time and resource properties about different tasks. Under the framework of Partially Observable Markov Decision Processes (POMDP), this paper takes the targets set to be struck as a controllable system, the strike tasks as system inputs, the assessment tasks as states feedback, and combines the finity of resources, to build a dynamic system optimization model under partially observable environment. The proposed model reveals inner relationship between strike and assessment tasks, coequally assigns fire and information resources in multiple UAVs system, and preferably describes the execution effects of tasks and information of targets states with time delay and uncertainty during operation process. Compared with traditional static models, the model overcomes the disadvantage of adapting to dynamic targets states changes.
(2) The trajectory optimization model for multiple UAVs formation flying with loose structure is suggested. The process of multiple UAVs formation flying is divided into two stages, i.e. formation configuration stage and formation maintaining stage. Then, the UAV kinematics equation is linearized based on the Lie derivative, and the communication topology is described by graph model. On this basis, the optimal control theory is used for reference to build optimization models for two stages. For formation configuration stage, the trajectory optimization model with free terminal constraints is build, so as to reduce over all energy consumption by properly choosing a consensus point. For formation maintaining stage, the local and global index functions are defined respectively to achieve the control objectives about reducing energy consumption, follow reference path, and maintaining formation structure at the same time.
(3) Starting from common feature of the resource allocation model and the formation trajectory optimization model, the adaptability of decomposition optimization method for weakly coupled dynamic system is expended, and corresponding theorems with theoretical proof is presented. Relation between cooperative behaviors of multiple UAVs and the coupling of mathematic model is analyzed thoroughly, then the convex optimization theory based decomposition methods are introduced to achieve the decoupling of problems. For the features about networks of distributed computing, the equivalent augmented network model without time delay of networks with time delay is build, and the augmented optimization problem is defined. Then, starting from the convergence of network nodes information transition matrix, the consistency and convergence of distributed asynchronous subgradient algorithm running in networks with local connection and time varying topology, and with time delay is theoretically proofed. For weakly coupled stochastic dynamic programming problem with partially observable system states, the dual function is defined on the basis of POMDP value iteration equation with constraints. Then, under premise of independency of each sub system’s initial belief state, the separability of dual function is theoretically proofed. Finally, the problem is decoupled by dual decomposition method, and construction method for subgradient of dual function is given.
(4) The dynamic resources allocation algorithm, which combines off-line optimization with on-line decision algorithm, is presented. Dynamic resource assignment model for single target under ideal conditions is build on the basis of infinite horizon POMDP. The model is conversed into non-linear integral programming problem based on properties of optimal stationary policy, and the resolve algorithm is designed. Dynamic resource assignment model for single target under general conditions is build on the basis of finite horizon POMDP. The improved linear support algorithm is proposed, and the controllable property of solution precision of the algorithm is proofed. Resources allocation process for multiple targets is divided into two stages, i.e. off-line optimization stage and on-line decision stage. In off-line optimization stage, the dual decomposition method is used to decompose the problem into multiple POMPD sub-problems for single targets, and costs of resources are used to coordinate expectation resources consumption of sub-problem optimal policies. In on-line decision stage, policies obtained in off-line stage and practical information gathered during tasks execution were took into account, in order to decide tasks to be executed for all targets based on greedy principle. Simulation results indicate that, the proposed method overcomes the disadvantage brought by uncertainty of targets states, and meets the demand of real time decision.
(5) The negotiation based distributed multiple UAVs formation trajectory optimization algorithm is presented. For variable coupling in trajectory optimization model of formation configuration stage, and index coupling in trajectory optimization model of formation maintaining stage, the primal decomposition method and indirect decomposition method is used to decouple the models respectively. The properties of master problems in coordination level obtained after decomposition of above models were proofed. On this basis, the construction methods for subgradients of index function in master problem is proposed, and the negotiation based distributed trajectory optimization algorithm is designed. In formation configuration stage, each UAV resolves its own optimal trajectory with given terminal states independently, gets alteration direction of consensus point to its advantage, and then updates and sends the point to its neighbors. After repeatedly negotiations, consensus points proposed by all UAVs consistence and convergent to the optimal solution. In formation maintaining stage, each UAV resolves its own optimal trajectory independently under control of dual variables, then updates and sends dual variables to its neighbors. After repeatedly negotiations, each UAV achieve to maintaining formation geometrical structure while optimize its local index. Simulation and experiments results indicate that, the proposed method has good computational efficiency and optimization capabilities, achieves performance improvements obviously under given index function, and supports multiple UAVs formation trajectory optimization effectively.
(1)建立了部分可观条件下的多UAV协同任务动态资源分配模型。给出了多UAV对地打击中的基本任务定义,可描述不同任务的执行效果及时间、资源等属性。在部分可观的马尔可夫决策过程(Partially Observable Markov Decision Processes, POMDP)模型框架下,将待打击的目标集合视为被控系统,以打击任务为系统输入,以评估任务为状态反馈,结合资源的有限性,建立了部分可观条件下的动态系统优化模型。模型揭示了评估与打击两类任务之间的内在联系,能够实现对多UAV系统中火力资源与信息资源的统一调配,可有效反映实际作战过程中任务执行效果以及目标状态信息带有的不确定性与时间延迟,并可克服传统静态分配模型不能适应目标状态动态变化的缺陷。
(2)建立了多UAV松散编队飞行的轨迹优化模型。将多UAV编队飞行过程划分为编队构成与编队保持两个阶段。基于李导数对单UAV运动学方程进行精确线性化,采用图模型描述编队内UAV平台间的通信拓扑。在平台运动学模型与通信拓扑模型的基础上,借鉴动态系统最优控制理论,构建两个阶段的优化模型。针对多UAV编队构成过程,建立了自由终端约束条件下的轨迹优化模型,可通过选择编队汇合点降低总体能量消耗。针对多UAV编队保持过程,定义了平台的个体指标与协同指标,能够兼顾减小能量消耗、跟踪参考航线、保持编队结构等多个控制目标。
(3)从资源分配与编队轨迹优化模型的共性特点出发,对弱耦合动态系统分解优化方法的适应性进行拓展,并给出相应定理及理论证明。剖析了多UAV协同行为与数学模型中耦合现象之间的关联关系,引入基于凸优化理论的分解方法实现问题解耦。针对分布式计算的网络特点,构建有界时延网络的等价无时延增广网络,相应定义增广优化问题,从网络节点信息变迁矩阵的收敛性入手,理论证明了在局部连接、拓扑可变以及带有时间延迟的网络环境中,异步并行次梯度算法的一致性和收敛性。针对系统状态部分可观的弱耦合随机动态规划问题,基于带约束的POMDP值迭代公式定义对偶函数,证明子系统初始信念状态独立的条件下,对偶函数的可分离性,采用对偶分解法实现问题解耦,并给出对偶函数次梯度的构造方法。
(4)提出了离线优化与在线决策相结合的动态资源分配算法。基于无限阶段POMDP建立理想条件下对单目标的动态资源指派模型,证明了最优平稳策略的性质,据此将模型转化为非线性整数规划问题,并设计求解算法。基于有限阶段POMDP建立一般条件下对单目标的动态资源指派模型,提出了改进的线性支撑算法,并证明了算法精度的可控性。将对多目标的动态资源分配过程划分为离线优化和在线决策两个阶段。离线优化阶段采用对偶分解法实现问题解耦,得到多个对单目标的POMDP子问题,并通过资源成本协调子问题最优策略的资源期望使用量。在线决策阶段综合考虑离线阶段所得资源指派策略与实际信息,基于贪婪策略确定对各目标的任务。仿真结果表明,该方法可有效克服目标状态信息不准确所带来的不利影响,能够满足实时决策的需求。
(5)提出了基于协商的分布式多UAV编队轨迹优化算法。针对编队构成轨迹优化模型变量耦合,以及编队保持轨迹优化模型指标耦合的特点,分别采用原始分解法与间接分解法实现问题解耦。证明了分解后协调层主问题指标函数的性质,给出其次梯度的构造方法,并设计了基于协商的分布式轨迹优化算法。编队构成阶段中,各UAV平台独立求解固定终端状态下的最优轨迹,得到对自身有利的汇合点变化方向,更新期望的汇合点并发送至相邻平台。经过反复协商,各平台提出的汇合点趋于一致,并收敛至最优解。编队保持阶段中,各UAV平台在对偶变量的控制下独立求解自身最优轨迹,更新并向相邻平台发送对偶变量。经过反复协商,各平台在优化局部指标的同时,实现对编队几何结构的保持。仿真与飞行实验验证结果表明,该方法具有良好的计算效率与优化能力,在给定指标函数下可显著提升性能,能够有效支持多UAV编队轨迹优化设计。
Mission planning for multiple Unmanned Aerial Vehicles (UAV) cooperative operation is one of the focuses to exert the advantages of cooperation of multiple UAVs. But multi-UAV systems in battle fields have to confront the uncertainty of information, the complexity of mathematical model, and the pressure of computing time. Under the background of multiple UAVs cooperative ground attack mission including multiple UAVs, multiple missions, and multiple targets, this dissertation applies closed-loop control system theory to abstracting of mission planning problem, focuses on the resource allocation problem and formation trajectory optimization problem, builds mathematical model, researches optimization theory, designs algorithm, carries out simulation and experiments.
(1) The dynamic resources allocation model for multiple UAVs cooperative mission with partially observable targets states is presented. The basic tasks in multiple UAVs ground attack mission are defined, so as to describe the execution effects, time and resource properties about different tasks. Under the framework of Partially Observable Markov Decision Processes (POMDP), this paper takes the targets set to be struck as a controllable system, the strike tasks as system inputs, the assessment tasks as states feedback, and combines the finity of resources, to build a dynamic system optimization model under partially observable environment. The proposed model reveals inner relationship between strike and assessment tasks, coequally assigns fire and information resources in multiple UAVs system, and preferably describes the execution effects of tasks and information of targets states with time delay and uncertainty during operation process. Compared with traditional static models, the model overcomes the disadvantage of adapting to dynamic targets states changes.
(2) The trajectory optimization model for multiple UAVs formation flying with loose structure is suggested. The process of multiple UAVs formation flying is divided into two stages, i.e. formation configuration stage and formation maintaining stage. Then, the UAV kinematics equation is linearized based on the Lie derivative, and the communication topology is described by graph model. On this basis, the optimal control theory is used for reference to build optimization models for two stages. For formation configuration stage, the trajectory optimization model with free terminal constraints is build, so as to reduce over all energy consumption by properly choosing a consensus point. For formation maintaining stage, the local and global index functions are defined respectively to achieve the control objectives about reducing energy consumption, follow reference path, and maintaining formation structure at the same time.
(3) Starting from common feature of the resource allocation model and the formation trajectory optimization model, the adaptability of decomposition optimization method for weakly coupled dynamic system is expended, and corresponding theorems with theoretical proof is presented. Relation between cooperative behaviors of multiple UAVs and the coupling of mathematic model is analyzed thoroughly, then the convex optimization theory based decomposition methods are introduced to achieve the decoupling of problems. For the features about networks of distributed computing, the equivalent augmented network model without time delay of networks with time delay is build, and the augmented optimization problem is defined. Then, starting from the convergence of network nodes information transition matrix, the consistency and convergence of distributed asynchronous subgradient algorithm running in networks with local connection and time varying topology, and with time delay is theoretically proofed. For weakly coupled stochastic dynamic programming problem with partially observable system states, the dual function is defined on the basis of POMDP value iteration equation with constraints. Then, under premise of independency of each sub system’s initial belief state, the separability of dual function is theoretically proofed. Finally, the problem is decoupled by dual decomposition method, and construction method for subgradient of dual function is given.
(4) The dynamic resources allocation algorithm, which combines off-line optimization with on-line decision algorithm, is presented. Dynamic resource assignment model for single target under ideal conditions is build on the basis of infinite horizon POMDP. The model is conversed into non-linear integral programming problem based on properties of optimal stationary policy, and the resolve algorithm is designed. Dynamic resource assignment model for single target under general conditions is build on the basis of finite horizon POMDP. The improved linear support algorithm is proposed, and the controllable property of solution precision of the algorithm is proofed. Resources allocation process for multiple targets is divided into two stages, i.e. off-line optimization stage and on-line decision stage. In off-line optimization stage, the dual decomposition method is used to decompose the problem into multiple POMPD sub-problems for single targets, and costs of resources are used to coordinate expectation resources consumption of sub-problem optimal policies. In on-line decision stage, policies obtained in off-line stage and practical information gathered during tasks execution were took into account, in order to decide tasks to be executed for all targets based on greedy principle. Simulation results indicate that, the proposed method overcomes the disadvantage brought by uncertainty of targets states, and meets the demand of real time decision.
(5) The negotiation based distributed multiple UAVs formation trajectory optimization algorithm is presented. For variable coupling in trajectory optimization model of formation configuration stage, and index coupling in trajectory optimization model of formation maintaining stage, the primal decomposition method and indirect decomposition method is used to decouple the models respectively. The properties of master problems in coordination level obtained after decomposition of above models were proofed. On this basis, the construction methods for subgradients of index function in master problem is proposed, and the negotiation based distributed trajectory optimization algorithm is designed. In formation configuration stage, each UAV resolves its own optimal trajectory with given terminal states independently, gets alteration direction of consensus point to its advantage, and then updates and sends the point to its neighbors. After repeatedly negotiations, consensus points proposed by all UAVs consistence and convergent to the optimal solution. In formation maintaining stage, each UAV resolves its own optimal trajectory independently under control of dual variables, then updates and sends dual variables to its neighbors. After repeatedly negotiations, each UAV achieve to maintaining formation geometrical structure while optimize its local index. Simulation and experiments results indicate that, the proposed method has good computational efficiency and optimization capabilities, achieves performance improvements obviously under given index function, and supports multiple UAVs formation trajectory optimization effectively.
引文
[1] Fahlstrom P G, Gleason T J.著,吴汉平等译.无人机系统导论[M].电子工业出版社, 2003.
[2] Flach J M, Eggleston R G, Kuperman G G.无人作战飞机的未来作战用途[J].国际航空. 2003, (7).
[3] Office of the Secretary of Defense. Unmanned Aircraft Systems Roadmap 2005-2030[R]. Department of Defense, Washington DC, 2005.
[4] Banda S, Doyle J, Murray R. Research Needs in Dynamics and Control for Uninhabited Aerial Vehicles (UAVs) [EB/OL]. www.cds.caltech. edu/ ~murray/notes/uav-nov97.pdf.
[5] Murphey R, Pardalos P M. Cooperative Control and Optimization (Applied Optimization Vol. 66) [M]. Springer, 2002.
[6]人民网.作战技术超前数十年:美空军六大基础研究课题揭秘[EB/OL]. http:// www.people.com.cn/GIG5/junshi/62/20020624/759999.html.
[7] Christopher J L. Inverting the Control Ratio: Human Control of Large Autonomous Teams[C]. In Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems. Melbourne, Australia, 2003.
[8] Ownby M. Mixed Initiative Control of Automa-teams (MICA)– A Progress Report[C]. In Proceedings of AIAA 3rd“Unmanned Unlimited”Technical Conference. Chicago, Illinois, 2004.
[9] Autonomous Negotiating Teams Program (ANT)[EB/OL]. http://www.if.afrl.af. mil/div/iftb/ants.html/.
[10] Hirschberg M. Joint Unmanned Combat Aerial System: J-UCAS Overview [EB/OL]. http//:www.darpar.mil/j-ucas. 2004-03-16/2007-10-04.
[11] Schumacher C, Chandler P R, Rasmussen S R. Task Allocation for Wide Area Search Munitions Via Iterative Network Flow[C]. In Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, California, 2002.
[12] Nygard K E, Chandler P R, Pachter M. Dynamic Network Flow Optimization Models for Air Vehicle Resource Allocation[C]. In Proceedings of American Control Conference. Arlington, VA, 2001: 1853-1858.
[13] Ollero A, Lacroix S, Merino L, et al. Multiple Eyes in the Skies: Architecture and Perception Issues in the COMETS Unmanned Air Vehicles Project[J]. IEEE Robotics & Automation Magazine. 2005:46-57.
[14] Rasmussen S J, Chandler P R. MultiUAV: A Multiple UAV Simulation for Investigation of Cooperative Control[C]. In Proceedings of the IEEE Conferenceon Decision and Control. 2004: 602-607.
[15] Chandler P R, Pachter M. Reaserch Issues in Autonomous Control of Tactical UAVs[C]. In Proceedings of American Control Conference. 1998:394-398.
[16] Chandler P R, Pachter M. Hierarchical Control for Autonomous Teams[C]. In Proceedings of AIAA Guidance, Navigation, and Control Conference. Montreal, Canada, AIAA 2001-4149.
[17] Rathinam S, Zennaro M, Mak T, Sengupta R. An Architecture for UAV Team Control[C]. In Proceedings of the IFAC Conference on Intelligent Autonomous Vehicles in Portugal. 2004.
[18] Shamma J. Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments[EB/OL]. http://www.seas.ucla.edu/coopcontrol/. 200-08-14/2007-10-04.
[19] Butenko S, Murphey R, Pardalos P. Cooperative Control: Models, Applications and Algorithms[M]. Kluwer Press, 2006: 96-111.
[20] Campbell M, Andrea R D, Schneider D, Chaudhry A. RoboFLag Games using Systems Based, Hierarchical Control[C]. In Proceedings of the American Control Conference, 2003, Vol. 1:661-666.
[21] Ryan A, Zennaro M, Howell A, et al. An Overview of Emerging Results in Cooperative UAV Control[C]. In Proceedings of the IEEE Conference on Decision and Control. 2004: 602-607.
[22] Bryson M, Sukkarieh S. Decentralized Trajectory Control for Multi-UAV SLAM[C]. In Proceeding of the 4th International Symposium on Mechatronics and its Applications. Sharjah, U.A.E, 2007.
[23] Ramos F T. Recognizing, Representing and Mapping Natural Features in Unstructured Environments[D]. PhD. Thesis, The University of Sydney, 2007.
[24] Secrest B R. Traveling Salesman Problem for Surveillance Mission Using Particle Swarm Optimization[D]. Master’s thesis, Air Force Institute of Technology, Wright-patterson Air Force Base, Ohio, USA, March 2001.
[25] Brown D T. Routing Unmanned Aerial Vehicles while Considering General Restricted Operating Zones[D]. Master’s thesis, Air Force Institute of Technology, Wright-patterson Air Force Base, Ohio, USA, March 2001.
[26] Hutchison M G. A Method for Estimating Range Requirements of Tactical Reconnaissance UAVs[C]. In Proceedings of AIAA 1st Technical Conference and Workshop on Unmanned Aerospace Vehicles. Virginia, 2002.
[27] Kursat Y. Multi-objective Mission Route Planning using Particle Optimization [D]. Master’s thesis, Air Force Institute of Technology University, 2002.
[28] Eun Y, Bang H. Cooperative Control of Multiple UCAVs for Suppression of Enemy Air Defense[C]. In Proceedings of the 3rd AIAA“Unmanned Unlimited”Technical Conference. Chicago Illinois, 2004.
[29] Lua C A, Altenburg K, Nygard K E. Synchronized Mulit-point Attack by Autonomous Reactive Vehicles with Simple Local Communicaiton[C]. In Proceedings of the IEEE Swarm Intelligence Symposium. Indianapolis, Indiana, 2003.
[30] Schumacher C, Chandler P R, Rasmussen S R. Task Allocation for Wide Area Search Munitions Via Iterative Network Flow[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, California, 2002.
[31] Nygard K E, Chandler P R, Pachter M. Dynamic Network Flow Optimization Models for Air Vehicle Resource Allocation[C]. In Proceedings of the American Control Conference. Arlington, VA, 2001, 1853-1858.
[32] Schumacher C, Chandler P R, Pachter M., Pachter L S. UAV Task Assignment with Timing Constraints[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin, Texas, 2003.
[33] Schumacher C, Chandler P R, Pachter M, Pachter L S. Constrained Optimization for UAV Task Assignment[C]. In Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit. Providence, Rhode Island, 2004.
[34] Darrah M A, Niland W M, Stolarik B M. Multiple UAV Dynamic Task Allocation using Mixed Integer Linear Programming in a SEAD Mission[C]. In Proceedings of the AIAA Infotech & Aerospace Conference, AIAA 2005-7146,. Alexandria, VA, 2005.
[35] Shima T, Rasmussen S J, Sparks A G, Passino K M. Multiple Task Assignments for Cooperating Uninhaited Aerial Vehicles Using Genetic Alorithms[J]. Computer and Operation Research. 2006, 33(11): 3252-3269.
[36] Alighanbari M, How J P. Decentralized Task Assignment for Unmanned Aerial Vehicles[C]. In Proceedings of the 44th IEEE Conference on Decision and Control. Seville, Spain, 2005: 5668-5673.
[37] Platts J T, Howell S E. Increasing UAV Intelligence Through Learning[C]. In Proceedings of the AIAA 3rd“Unmanned Unlimited”Technical Conference, Workshop and Exhibit. Chicago, IL, USA, 2004:1-13.
[38] Yang Y, Minai A A, Polycarpou M M. Decentralized Cooperative Search in AV’s Using Opportunistic Learning[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference. Moterey, CA, 2002. AIAA-2002-4590.
[39] Cruz B J, Simaan A M, Gracic A, Jiang H. Game Theoretice Modeling and Control of Military Operations[J]. IEEE Transaction on Aerospace and Electronic Systems. 2001, 37(4): 1393-1405.
[40] Mceneaney M W, Fitzpatrick G B, Lauko G I. Stochastic Game Approach to Air Operations[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(4):1191-1216.
[41] Alighanbari M, Kuwata Y, How J P. Coordination and Control of Multiple UAVs with Timing Constraints and Loiterings[C]. In Proceedings of the America Control Conference. Denver, Colorado, 2003: 5311-5316.
[42] Resmussen S, Chandler P R. Optimal vs. Heuristic Assignment of Cooperative Autonomous Unmanned Aerial Vehicles[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin, Texas, 2003.
[43] Chen G, Cruz J B. Genetic Algorithm for Task Alloation in UAV Cooperative Control[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin, Texas, 2003.
[44] Cruz JB J, Chen Ge, et al. Particle Swarm Optimization for Resource Allocation in UAV Cooperative Control[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Chicago, Illinois, 2004.
[45] Hart D M. Reducing Swarming Theory to Practice for UAV Control[C]. In Proceedings of the IEEE Aerospace Conference. 2004, Vol. 5: 3050-3063.
[46] Jin Y, Liao Y, Minai A A, Polycarpou M M. Balancing Search and Target Response in Cooperative Unmanned Aerial Vehicle(UAV) Teams[J]. IEEE Transactions on Systems, Man and Cybernetics– Part B: Cybernetics. 2006, 36(3): 571-587.
[47] Pongpunwattana A. Real-time Planning for Teams of Autonomous Vehicles in Dynamic Uncertain Environment[D]. Ph.D. thesis, University of Washington, USA, 2004.
[48] Dolgov D, Durfee E H. Satisficing Strategies for Resource-limited Policy Search in Dynamic Environments[C]. The 1st International Joint Conference on Autonomous Agents and Multi-agent Systems: Part 3. Bologna, Italy, 2002: 1325-1332.
[49] Xu Y, Scerri P, Yu B, et al. An integrated token-based algorithm for scalable coordination[C]. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems. ACM Press, 2005: 407-414.
[50] Bordeaux J. Self-organized Air Tasking: Examining a Non-hierarchical Model for Joint Air Operations. SRA International[C]. In Proceedings of the 2004 International Command and Control Research and Technology Symposium. Copenhagen, Denmark, 2004.
[51] Parunak H V, Purcell M, Connell P O. Digital Pheromones for Autonomous Coordination of Swarming UAVs[C]. In Proceedings of the 1st AIAA Unmanned Aerospace Vehicles, Systems, Technologies, and Operations Conference. AIAA-2002-3446, 2002.
[52] Price I C. Evolving Self-organized Behavior for Homogeneous and Heterogeneous UAV or UCAV Swarms. Master thesis, Air Force Institute of Technology, Wright-patterson Air Force Base, Ohio, USA, March 2006.
[53] Clough B T. UAV Swarming? So What are Those Swarms, What are the Implications, and How Do We Handle Them[C]. In Proceedings of the AUVSI Unmanned System Conference. Orlando, FL, USA. 2002.
[54] Dionne D, Rabbath C A. Multi-UAV Decentralized Task Allocation with Intermittent Communications: the DTC algorithm[C]. In Proceedings of the 2007 American Control Conference. New York City, USA. 2007:5406-5411.
[55] Sujit P B, Sinha A, Ghose D. Multi-UAV Task Allocation using Team Theory[C]. In Proceedings of the 44th IEEE Conference on Decision and Control. Seville, Spain. 2005:1497-1502.
[56] Shima T, Rasmussen S J, Chandler P R. UAV Team Decision and Control Using Efficient Collaborative Estimation[J]. Journal of Dynamic Systems, Measurement, and Control. 2007, 129: 609-619.
[57] Godwin M F, Spry S, Hedrick J K. Distributed Collaboration with Limited Communication using Mission State Estimates[C]. In Proceedings of the American Control Conference. Minneapolis, Minnesota, USA. 2006: 2040-2046.
[58] Liao Y, Jin Y, Minai A A, Polycarpo M M. Information Sharing in Cooperative Unmanned Aerial Vehicle Teams[C]. In Proceedings of the 44th IEEE Conference on Decision and Control. Seville, Spain. 2005:90-95.
[59] Frazzoli E. Robust Hybrid Control for Autonomous Vehicle Motion Planning[D]. Ph.D. thesis, Massachusetts Institute of Technology, USA, 2001.
[60] LaValle S M. Planning Algorithms[M]. Cambridge: Cambridge University Press, 2006.
[61] Frazzoli E, Dahleh M A, Feron E. Real-time Motion Planning for Agile Autonomous Vehicles[J]. AIAA Journal of Guidance, Control, and Dynamics, 2002, 25(1): 116-129.
[62] Bellingham J, Tillerson M, Richards A, How J P. Multi-task Allocation and Path Planning for Cooperative UAVs[M]. Cooperative Control: Models, Applications and Algorithms. Denver, CO, 2003, 23-41.
[63] Kuwata Y, How J P. Three Dimensional Receding Horizon Control for UAVs[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. 2004.
[64] Beard R W, McLain T W, Goodrish M, Andeson E P. Coordinated Target Assignment and Intercept for Unmanned Air Vehicles[J]. IEEE Transactions on Robotics and Automation. December 2002, 18(3): 911-922.
[65] McLain T W, Chandler P R. Cooperative Control of UAV Rendezvous[C]. In Proceedings of American Control Conference. 2001, 2309-2314.
[66] Pettersson P O, Doherty P. Probabilistic Roadmap Based Path Planning for an Autonomous Unmanned Aerial Vehicle[C]. In Proceedings of the Workshop on Connecting Planning and Theory with Practice. 2004.
[67] LaValle S M. Rapidly-Exploring Random Trees: A New Tool for Path Planning[R]. Computer Science Department, Iowa State University. 1998.
[68] Yershova A, Jaillet L, Simeon T, LaValle S M. Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain[C]. In Proceedings of the IEEE International Conference on Robotics and Automation. Barcelona, Spain. 2005: 3856-3861.
[69] Melchior N A, Simmons R. Particle RRT for Path Planning with Uncertainty[C]. In Proceedings of the IEEE International Conference on Robotics and Automation. Roma, Italy. 2007: 1617-1624.
[70] Anderson E P, Beard R W, McLain T W. Real Time Dynamic Trajectory Smoothing for Unihabited Aerial Vehicles[J]. IEEE Transaction on Control Systems Technology. 2005, 13(3): 471-477.
[71] Szczerba R J. Robust Algorithm for Real-time Route Planning[J]. IEEE Transaction on Aerospace and Electronic System. 2000, 36(3): 869-878.
[72] Gudaitis M S. Multi-criteria Mission Route Planning Using a Parallel A* Search[R]. Technical Report. AFIT/GCS/ENG/94D-05, AD-A289284, 1994.
[73] Jarle A, Leif H B. Flight Path Planning System for Autonomous Unmanned Aerial Vehicles[R]. Project Report. Norwegian University of Science and Technology, 2004.
[74] Rahtbun D, Kragelund S, Pongpunwattana A, et al. An Evolution Based Path Planning Algorithm for Autonomous Motion of a UAV Through Uncertain Environments[C]. In Proceedings of the 21st AIAA Digital Avionics Systems Conference. Irvine, CA, 2003.
[75] Nikolos I K. Evolutionary Algorithm Based Offline/Online Path Planner for UAV Navigation[J]. IEEE Transaction on Systems, Man, and Cybernetics– Part B: Cybernetics, 2003, 33(6):898-912.
[76] Foo J, Knutzon J, Oliver J, Winer E. Three-dimensional Path Planning of Unmanned Aerial Vehicles Using Particle Swarm Optimization[C]. AIAA 2006-6995.
[77] Tom S. Safe Trajectory Planning of Autonomous Vehicles[D]. Ph.D. thesis, Massachusetts Institute of Technology, USA, 2005.
[78] Gotoand Y, Stentz A. Mobile Robot Navigation: The CMU System[J]. IEEE Expert. 1987, 2(4): 44-55.
[79] Korf R. Real-time Heuristic Search: Research Issues[J]. Artificial Intelligence. 1990, 42(2): 189-211.
[80] Sjanic Zoran. On-line Mission Planning based on Model Predictive Control[D]. Master’s thesis, Division of Automatic Control Department of Electrical Engineering, Linkoing University, 2001.
[81] Richards A, How J P. Decentralized Model Predictive Control of CooperatingUAVs[C]. In Proceedings of the 43rd IEEE Conference on Control and Decision. 2004, 4: 4286-4291.
[82] Chandler P R, Rasmussen S, Pachter M. UAV Cooperative Path Planning[C]. In Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit. Denver, Colorado, 2000.
[83] McLain T W, Beard R W. Cooperative Path Planning for Timing-critical Missions[C]. In Proceedings of American Control Conference. Denver, Colorado, 2003: 296-301.
[84] McLain T W, Beard R W. Coordination Variables, Coordination Functions, and Cooperative Timing Missions[J]. Journal of Guidance, Control, and Dynamics. 2005, 28(1) : 150-161.
[85] Beard R W, McLain T W, Nelson D B. Decentralized Cooperative Aerial Surveillance Using Fixed-Wing Miniature UAVs[J]. Proceedings of the IEEE. 2006, 94(7): 1306-1324.
[86] Boivin E, Desbiens A, Gagnon E. UAV Collision Avoidance Using Cooperative Predictive Control[C]. In Proceedings of the 16th Mediteranean Conferece on Control and Automation Congress Centre. Ajaccio, France, 2008: 682-688.
[87] Richards A, How J P. Aircraft Trajectory Planning with Collision Avoidance Using Mixed Integer Linear Programming[C]. In Proceedings of the American Control Conference. Anchorage, AK, USA. 2002: 1936-1941.
[88] Schouwenaars T. How J P, Feron E. Decentralized Cooperative Trajectory Planning of Multiple Aircraft with Hard Safety Guarantees[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Providence, Rhode Island. AIAA-2004-5141. 2004.
[89] Kuwata Y, How J P. Robust Cooperative Decentralized Trajectory Optimization using Receding Horizon MILP[C]. In Proceedings of the 2007 American Control Conference. New York City, USA, July 11-13, 2007.
[90] Fasano G, Accardo D, Moccia A, et al. Multi-sensor based Fully Autonomous Non-Cooperative Collision Avoidance System for UAVs[C]. In Proceedings of the AIAA Infotech@Aerospace Conference and Exhibit. Rohnert Park, California. AIAA-2007-2847. 2007.
[91]余周毅,陈宗基,周锐.基于遗传算法的动态资源调度问题研究[J].控制与决策. 2004, 19(11): 1308-1311.
[92]冯琦,周德云.应用单亲遗传算法进行大规模UCAV任务分配.火力与指挥控制[J]. 2006, 31(5):18-21.
[93]严平.无人飞行器航迹规划与任务分配方法研究[D].博士学位论文,华中科技大学武汉,武汉, 2006.
[94]余舟毅,陈宗基,周锐.基于遗传算法的动态资源调度问题研究[J].控制与决策. 2004, 19(11): 1308-1311.
[95]段海滨,丁全心,常俊杰,刘森琪.基于并行蚁群优化的多无人作战飞机任务分配仿真平台[J].航空学报. 2008, 5(s1): 192-197.
[96]曹攀峰,崔升.考虑信息延迟的无人机分布式协同搜索算法[J].电光与控制. 2010, 17(3): 27-29+34.
[97]廖沫,陈宗基.基于满意决策的多机协同目标分配算法[J].北京航空航天大学学报. 2007, 33(1): 81-85.
[98]宋敏,魏瑞轩,冯志明.基于差分进化算法的异构多无人机任务分配[J].系统仿真学报. 2010, 22(7): 150-154.
[99]潘峰,陈杰,任智平,王光辉.基于计算智能方法的无人机任务指派约束优化模型研究[J].兵工学报. 2009, 30(12): 140-147.
[100]刘毅,李为民,邢清华,徐小来.基于双层规划的攻击无人机协同目标分配优化[J].系统工程与电子技术. 2010, 32(3): 579-583.
[101]郭文强,高晓光,任佳,肖秦琨.基于图模型自主优化的多无人机多目标攻击[J].系统工程与电子技术. 2010, 32(3): 574-578.
[102]符小卫,高晓光.一种无人机路径规划算法研究[J].系统仿真学报. 2004, 14(1): 20-21+34.
[103]肖秦琨,高晓光.基于空间改进型Voronoi图的无人机路径规划研究[J].计算机工程与应用. 2005, 26:204-207.
[104]郑昌文,丁明跃,周成平等.多飞行器协调航迹规划方法.宇航学报. 2003, 24(2): 115-120.
[105]柳长安,王和平,李为吉.攻击无人机的协同航路规划.西北工业大学学报. 2003, 21(6): 707-710.
[106]高晓光,符小卫,宋绍梅.多UCAV航迹规划研究.系统工程理论与实践. 2004, 24(5): 140-143.
[107]严平,丁明跃,周成平,郑昌文.飞行器多任务在线实时航迹规划.航空学报. 2004, 25(9): 485-489.
[108]唐强,张新国,刘锡成.一种用于低空飞行的在线航迹重规划方法.西北工业大学学报. 2005, 23(2): 271-275.
[109]曾佳,申功璋,杨凌宇.无人机在线协同航迹规划时序问题.南京航空航天大学学报. 2009, 31(3): 52-56.
[110]严平,丁明跃,郑昌文.基于Nash均衡与进化计算的协调航迹规划.航空学报(英文版). 2006, 19(1): 20-25.
[111]丁晓东,刘毅,李为民.基于动态RCS的无人机航迹实时规划方法研究.系统工程与电子技术. 2008, 30(5): 90-93.
[112]唐上钦,黄长强,胡杰,吴文超.基于威胁等效和改进PSO算法的UCAV实时航路规划方法.系统工程与电子技术. 2010, 32(8): 1706-1710.
[113]叶媛媛.多UCAV协同任务规划方法研究.博士学位论文,国防科学技术大学,长沙, 2005.
[114]龙涛.多UCAV协同任务控制中分布式任务分配与任务协调技术研究.博士学位论文,国防科学技术大学,长沙, 2006.
[115]田菁.多无人机协同侦察任务问题建模与优化技术研究.博士学位论文,国防科学技术大学,长沙, 2007.
[116]霍霄华.多UCAV动态协同任务规划建模与滚动优化方法研究.博士学位论文,国防科学技术大学,长沙, 2008.
[117]彭辉.分布式多无人机协同区域搜索中的关键问题研究.博士学位论文,国防科学技术大学,长沙, 2009.
[118] Boyd J R. The Essence of Winning and Losing[EB/OL]. http://www. chetrichards.com/modern_business_strategy/boyd/essence/eowl_frameset.htm. 2006-08-12/2009-09-13.
[119] Grant T. Comparing OODA & Other Models as Operational View C2 Architecture [C]. In Proceedings of the 10th International Command and Control Research and Technology Symposium. Washington, DC, USA. 2005.
[120] Valavanis K P. Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy[M]. Netherlands: Springer Press. 2007: 537-540.
[121] Air Force Pamphlet 14-210. USAF Intelligence Targeting Guide[M]. DOD, 1998.
[122] JP-1-02. Department of Defense Dictionary of Military and Associated Terms. Director for Operational Plans and Joint Force Development (J-7)[M]. DOD, 2005.
[123] Chen C. R, Blankenship L G. Dynamic Programming Equations for Discounted Constrained Stochastic Control[J]. IEEE Transaction on Automatic Control, 2004, 49(5): 699-709.
[124]任德华,卢桂章.对队形控制的思考[J].控制与决策,2005,20(6):601-606.
[125] Lemay M, Michaud F, Letourneau D, et al. Autonomous Initialization of Robot Formations[C]. In Proceedings of IEEE International Conference on Robotics and Automation. 2004: 3018-3023.
[126]胡跃明.非线性控制系统理论与应用[M].北京:国防工业出版社. 2005.
[127] Tomonari F. Time-Optimal Coordinated Control of the Relative Formation of Multiple Vehicles[C]. In Proceedings of 2003 IEEE International symposium on computational intelligence in Robotics and Automation. Kobe, Japan: IEEE, 2003. 259-264.
[128]俞辉,王永骥,程磊.基于有向网络的智能群体群集运动控制[J].控制理论与应用, 2007, 24(1): 79-83.
[129]吴正平,关治洪,吴先用.基于一致性理论的多机器人系统队形控制[J].控制与决策, 2007, 22(11): 1241-1244.
[130]王振国,陈小前,罗文彩,张为华.飞行器多学科设计优化理论与应用研究[M].北京:国防工业出版社. 2006:50-56.
[131] Camponogara E, Jia D, Krogh B H, et al. Distributed Model Predictive Control[J]. IEEE Control Systems Magazine. 2002, 22(1): 44–52.
[132] Camponogara E. Controlling Networks with Collaborative Nets[D]. PhD. Thesis, Carnegie Mellon University, 2000.
[133] Boche H, Stanczak S. Convexity of Some Feasible QoS Regions and Asymptotic Behavior of the Minimu Total Power in CDMA Systems[J]. IEEE Transactions on Communications, 2004, 53(12): 2190-2197.
[134] Palomar D P, Chiang M. A Tutorial on Decomposition Methods for Network Utility Maximization[J]. IEEE Journal on Selected Areas in Communication. 2006, 24(8): 1439-1451.
[135] Boyd S, Vandenberghe L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004.
[136] Bertsekas D. P., Nedic A., Ozdaglar A. Convex Analysis and Optimization[M]. Belmont: Athena Scientific. 2003.
[137] Nedic A, Bertsekas D P. Incremental Subgradient Methods for Nondifferentiable Optimization[J]. SIAM Journal on Optimization. 2001, 12(1): 109-138.
[138] Ram S S, Nedic A, Veeravalli V V. Incremental Stochastic Subgradient Algorithms for Convex Optimization[J]. SIAM Journal on Optimization. 2009, 20(2): 691-717.
[139] Nedic A, Ozdaglar A. On the Rate of Convergence of Distributed Subgradient Method for Multi-agent Optimization[C]. In Proceedings of the 46th IEEE Conference on Decision and Control. New Orleans, USA. 2007: 4711-4716.
[140] Nedic A, Ozdaglar A, Parrilo P A. Constrained Consensus and Optimization in Multi-Agent Networks[J]. IEEE Transactions on Automatic Control. 2010, 55(4): 922-938.
[141] Mathews G, Durrant-Whyte H, Prokopenk M. Asynchronous Gradient-Based Optimisation for Team Decision Making[C]. In Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, 2007: 3145-3150.
[142] Nedic A, Ozdaglar A. Convergence Rate for Consensus with Delays[J]. Journal of Global Optimization. 2010: 47(3): 437-456.
[143] Adelman D, Mersereau J A. Relaxations of Weakly Coupled Stochastic Dynamic Programs[J]. Operations Research. 2008, 56(3): 712-727.
[144] Hawkins J. A Lagrangian Decomposition Approach to Weakly Coupled DynamicOptimization Problems and its Applications[D]. Ph.D. thesis, Massachusetts Institute of Technology, USA, 2003.
[145] Castanon A D. Stochastic Control Bounds on Sensor Network Performance[C]. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference. Seville, Spain, 2005: 4939-4944.
[146] Wang J, Luh P B, Zhao X, Wang J. An Optimization-based Algorithm for Job Shop Scheduling[J]. SADHANA, 1997, 22(2): 241-256.
[147] Adelman D. A Price-directed Approach to Stochastic Inventory/Routing[J]. Operations Research. 2004, 52(4): 499-514.
[148] Bertsimas D, Mersereau A. A Learning Approach for Interactive Marketing to a Customer Segment[R]. Working Paper. University of Chicago GSB, 2006.
[149] Growe-Kuska N, Kiwiel K C. Short-term hydropower production planning by stochastic programming[J]. Computers and Operations Research. 2008, 35(8): 2656-2671.
[150] Sudipto G, Kamesh M. Approximation Algorithms for Partial-information based Stochastic Control with Markovian Rewards[C]. In Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science. IEEE Computer Society, Washington DC, USA, 2007: 483-493.
[151]刘克.实用马尔可夫决策过程[M].北京:清华大学出版社. 2004.
[152] Cassandra R A. Exact and Approximate Algorithms for Partially Observable Markov Decision Processes. Ph. D. thesis, Brown University, Providence, Rhode Island, 1998.
[153] Lovejoy S W. A Survey of Algorithmic Methods for Partially Observable Markov Decision Processes[J]. Annals of Operations Research. 1991, 28(1): 47-65.
[154] Striebel T V. Sufficient Staticstics in the Optimal Control of Stochastic Systems[J]. Journal of Mathematical Analysis and Applications. 1965, 12:576-592.
[155]陈华东,王树宗,王航宇.基于混合粒子群算法的多平台多武器火力分配研究[J].系统工程与电子技术. 2008, 30(5): 880 - 883.
[156] David G G. Game theoretic target assignment strategies in competitive multi-team systems[D]. PhD. thesis,University of Pittsburgh, Pittsburgh, PA, 2004.
[157] Rosenberger J M, Yucel A. The Generalized Weapon Target Assignment Problemp[C]. In Proceedings of the 10th International Command and Control Research and Technology Symposium, 2005.
[158]吴玲,卢兴发,贾培发.动态武器目标分配问题中改进遗传算法的元级控制[J].清华大学学报. 2008, 48(S2): 1762-1765.
[159] Wacholder E. A neural network-based optimization algorithm for the static weapon assignment problem[J]. ORSA Journal on Computing. 1989, 4: 232– 246.
[160] Cheng H. Algorithms for Partially Observable Markov Decision Processes[D]. Ph. D. thesis, University of British Columbia, 1988.
[161] Sondik J. Edward. The Optimal Control of Partially Observable Markov Processes[D]. Ph.D. thesis, Stanford University, Palo Alto, CA, 1971.
[162] Kamal W A, Gu D W, Postlethwaite I. Real Time Trajectory Planning for UAVs Using MILP[C]. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference. Seville, Spain, 2005: 3381-3386.
[163]黄红选,韩继业.数学规划[M].北京:清华大学出版社. 2006: 237-240.
[164]《现代应用数学手册》编委会.运筹学与最优化理论卷[M].北京:清华大学出版社. 1998: 182-185.
[165] Cowling I. Toward Autonomy of Quadrotor UAV[D]. Ph.D. thesis, Cranfield University, 2008.
[2] Flach J M, Eggleston R G, Kuperman G G.无人作战飞机的未来作战用途[J].国际航空. 2003, (7).
[3] Office of the Secretary of Defense. Unmanned Aircraft Systems Roadmap 2005-2030[R]. Department of Defense, Washington DC, 2005.
[4] Banda S, Doyle J, Murray R. Research Needs in Dynamics and Control for Uninhabited Aerial Vehicles (UAVs) [EB/OL]. www.cds.caltech. edu/ ~murray/notes/uav-nov97.pdf.
[5] Murphey R, Pardalos P M. Cooperative Control and Optimization (Applied Optimization Vol. 66) [M]. Springer, 2002.
[6]人民网.作战技术超前数十年:美空军六大基础研究课题揭秘[EB/OL]. http:// www.people.com.cn/GIG5/junshi/62/20020624/759999.html.
[7] Christopher J L. Inverting the Control Ratio: Human Control of Large Autonomous Teams[C]. In Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems. Melbourne, Australia, 2003.
[8] Ownby M. Mixed Initiative Control of Automa-teams (MICA)– A Progress Report[C]. In Proceedings of AIAA 3rd“Unmanned Unlimited”Technical Conference. Chicago, Illinois, 2004.
[9] Autonomous Negotiating Teams Program (ANT)[EB/OL]. http://www.if.afrl.af. mil/div/iftb/ants.html/.
[10] Hirschberg M. Joint Unmanned Combat Aerial System: J-UCAS Overview [EB/OL]. http//:www.darpar.mil/j-ucas. 2004-03-16/2007-10-04.
[11] Schumacher C, Chandler P R, Rasmussen S R. Task Allocation for Wide Area Search Munitions Via Iterative Network Flow[C]. In Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, California, 2002.
[12] Nygard K E, Chandler P R, Pachter M. Dynamic Network Flow Optimization Models for Air Vehicle Resource Allocation[C]. In Proceedings of American Control Conference. Arlington, VA, 2001: 1853-1858.
[13] Ollero A, Lacroix S, Merino L, et al. Multiple Eyes in the Skies: Architecture and Perception Issues in the COMETS Unmanned Air Vehicles Project[J]. IEEE Robotics & Automation Magazine. 2005:46-57.
[14] Rasmussen S J, Chandler P R. MultiUAV: A Multiple UAV Simulation for Investigation of Cooperative Control[C]. In Proceedings of the IEEE Conferenceon Decision and Control. 2004: 602-607.
[15] Chandler P R, Pachter M. Reaserch Issues in Autonomous Control of Tactical UAVs[C]. In Proceedings of American Control Conference. 1998:394-398.
[16] Chandler P R, Pachter M. Hierarchical Control for Autonomous Teams[C]. In Proceedings of AIAA Guidance, Navigation, and Control Conference. Montreal, Canada, AIAA 2001-4149.
[17] Rathinam S, Zennaro M, Mak T, Sengupta R. An Architecture for UAV Team Control[C]. In Proceedings of the IFAC Conference on Intelligent Autonomous Vehicles in Portugal. 2004.
[18] Shamma J. Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments[EB/OL]. http://www.seas.ucla.edu/coopcontrol/. 200-08-14/2007-10-04.
[19] Butenko S, Murphey R, Pardalos P. Cooperative Control: Models, Applications and Algorithms[M]. Kluwer Press, 2006: 96-111.
[20] Campbell M, Andrea R D, Schneider D, Chaudhry A. RoboFLag Games using Systems Based, Hierarchical Control[C]. In Proceedings of the American Control Conference, 2003, Vol. 1:661-666.
[21] Ryan A, Zennaro M, Howell A, et al. An Overview of Emerging Results in Cooperative UAV Control[C]. In Proceedings of the IEEE Conference on Decision and Control. 2004: 602-607.
[22] Bryson M, Sukkarieh S. Decentralized Trajectory Control for Multi-UAV SLAM[C]. In Proceeding of the 4th International Symposium on Mechatronics and its Applications. Sharjah, U.A.E, 2007.
[23] Ramos F T. Recognizing, Representing and Mapping Natural Features in Unstructured Environments[D]. PhD. Thesis, The University of Sydney, 2007.
[24] Secrest B R. Traveling Salesman Problem for Surveillance Mission Using Particle Swarm Optimization[D]. Master’s thesis, Air Force Institute of Technology, Wright-patterson Air Force Base, Ohio, USA, March 2001.
[25] Brown D T. Routing Unmanned Aerial Vehicles while Considering General Restricted Operating Zones[D]. Master’s thesis, Air Force Institute of Technology, Wright-patterson Air Force Base, Ohio, USA, March 2001.
[26] Hutchison M G. A Method for Estimating Range Requirements of Tactical Reconnaissance UAVs[C]. In Proceedings of AIAA 1st Technical Conference and Workshop on Unmanned Aerospace Vehicles. Virginia, 2002.
[27] Kursat Y. Multi-objective Mission Route Planning using Particle Optimization [D]. Master’s thesis, Air Force Institute of Technology University, 2002.
[28] Eun Y, Bang H. Cooperative Control of Multiple UCAVs for Suppression of Enemy Air Defense[C]. In Proceedings of the 3rd AIAA“Unmanned Unlimited”Technical Conference. Chicago Illinois, 2004.
[29] Lua C A, Altenburg K, Nygard K E. Synchronized Mulit-point Attack by Autonomous Reactive Vehicles with Simple Local Communicaiton[C]. In Proceedings of the IEEE Swarm Intelligence Symposium. Indianapolis, Indiana, 2003.
[30] Schumacher C, Chandler P R, Rasmussen S R. Task Allocation for Wide Area Search Munitions Via Iterative Network Flow[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, California, 2002.
[31] Nygard K E, Chandler P R, Pachter M. Dynamic Network Flow Optimization Models for Air Vehicle Resource Allocation[C]. In Proceedings of the American Control Conference. Arlington, VA, 2001, 1853-1858.
[32] Schumacher C, Chandler P R, Pachter M., Pachter L S. UAV Task Assignment with Timing Constraints[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin, Texas, 2003.
[33] Schumacher C, Chandler P R, Pachter M, Pachter L S. Constrained Optimization for UAV Task Assignment[C]. In Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit. Providence, Rhode Island, 2004.
[34] Darrah M A, Niland W M, Stolarik B M. Multiple UAV Dynamic Task Allocation using Mixed Integer Linear Programming in a SEAD Mission[C]. In Proceedings of the AIAA Infotech & Aerospace Conference, AIAA 2005-7146,. Alexandria, VA, 2005.
[35] Shima T, Rasmussen S J, Sparks A G, Passino K M. Multiple Task Assignments for Cooperating Uninhaited Aerial Vehicles Using Genetic Alorithms[J]. Computer and Operation Research. 2006, 33(11): 3252-3269.
[36] Alighanbari M, How J P. Decentralized Task Assignment for Unmanned Aerial Vehicles[C]. In Proceedings of the 44th IEEE Conference on Decision and Control. Seville, Spain, 2005: 5668-5673.
[37] Platts J T, Howell S E. Increasing UAV Intelligence Through Learning[C]. In Proceedings of the AIAA 3rd“Unmanned Unlimited”Technical Conference, Workshop and Exhibit. Chicago, IL, USA, 2004:1-13.
[38] Yang Y, Minai A A, Polycarpou M M. Decentralized Cooperative Search in AV’s Using Opportunistic Learning[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference. Moterey, CA, 2002. AIAA-2002-4590.
[39] Cruz B J, Simaan A M, Gracic A, Jiang H. Game Theoretice Modeling and Control of Military Operations[J]. IEEE Transaction on Aerospace and Electronic Systems. 2001, 37(4): 1393-1405.
[40] Mceneaney M W, Fitzpatrick G B, Lauko G I. Stochastic Game Approach to Air Operations[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(4):1191-1216.
[41] Alighanbari M, Kuwata Y, How J P. Coordination and Control of Multiple UAVs with Timing Constraints and Loiterings[C]. In Proceedings of the America Control Conference. Denver, Colorado, 2003: 5311-5316.
[42] Resmussen S, Chandler P R. Optimal vs. Heuristic Assignment of Cooperative Autonomous Unmanned Aerial Vehicles[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin, Texas, 2003.
[43] Chen G, Cruz J B. Genetic Algorithm for Task Alloation in UAV Cooperative Control[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin, Texas, 2003.
[44] Cruz JB J, Chen Ge, et al. Particle Swarm Optimization for Resource Allocation in UAV Cooperative Control[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Chicago, Illinois, 2004.
[45] Hart D M. Reducing Swarming Theory to Practice for UAV Control[C]. In Proceedings of the IEEE Aerospace Conference. 2004, Vol. 5: 3050-3063.
[46] Jin Y, Liao Y, Minai A A, Polycarpou M M. Balancing Search and Target Response in Cooperative Unmanned Aerial Vehicle(UAV) Teams[J]. IEEE Transactions on Systems, Man and Cybernetics– Part B: Cybernetics. 2006, 36(3): 571-587.
[47] Pongpunwattana A. Real-time Planning for Teams of Autonomous Vehicles in Dynamic Uncertain Environment[D]. Ph.D. thesis, University of Washington, USA, 2004.
[48] Dolgov D, Durfee E H. Satisficing Strategies for Resource-limited Policy Search in Dynamic Environments[C]. The 1st International Joint Conference on Autonomous Agents and Multi-agent Systems: Part 3. Bologna, Italy, 2002: 1325-1332.
[49] Xu Y, Scerri P, Yu B, et al. An integrated token-based algorithm for scalable coordination[C]. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems. ACM Press, 2005: 407-414.
[50] Bordeaux J. Self-organized Air Tasking: Examining a Non-hierarchical Model for Joint Air Operations. SRA International[C]. In Proceedings of the 2004 International Command and Control Research and Technology Symposium. Copenhagen, Denmark, 2004.
[51] Parunak H V, Purcell M, Connell P O. Digital Pheromones for Autonomous Coordination of Swarming UAVs[C]. In Proceedings of the 1st AIAA Unmanned Aerospace Vehicles, Systems, Technologies, and Operations Conference. AIAA-2002-3446, 2002.
[52] Price I C. Evolving Self-organized Behavior for Homogeneous and Heterogeneous UAV or UCAV Swarms. Master thesis, Air Force Institute of Technology, Wright-patterson Air Force Base, Ohio, USA, March 2006.
[53] Clough B T. UAV Swarming? So What are Those Swarms, What are the Implications, and How Do We Handle Them[C]. In Proceedings of the AUVSI Unmanned System Conference. Orlando, FL, USA. 2002.
[54] Dionne D, Rabbath C A. Multi-UAV Decentralized Task Allocation with Intermittent Communications: the DTC algorithm[C]. In Proceedings of the 2007 American Control Conference. New York City, USA. 2007:5406-5411.
[55] Sujit P B, Sinha A, Ghose D. Multi-UAV Task Allocation using Team Theory[C]. In Proceedings of the 44th IEEE Conference on Decision and Control. Seville, Spain. 2005:1497-1502.
[56] Shima T, Rasmussen S J, Chandler P R. UAV Team Decision and Control Using Efficient Collaborative Estimation[J]. Journal of Dynamic Systems, Measurement, and Control. 2007, 129: 609-619.
[57] Godwin M F, Spry S, Hedrick J K. Distributed Collaboration with Limited Communication using Mission State Estimates[C]. In Proceedings of the American Control Conference. Minneapolis, Minnesota, USA. 2006: 2040-2046.
[58] Liao Y, Jin Y, Minai A A, Polycarpo M M. Information Sharing in Cooperative Unmanned Aerial Vehicle Teams[C]. In Proceedings of the 44th IEEE Conference on Decision and Control. Seville, Spain. 2005:90-95.
[59] Frazzoli E. Robust Hybrid Control for Autonomous Vehicle Motion Planning[D]. Ph.D. thesis, Massachusetts Institute of Technology, USA, 2001.
[60] LaValle S M. Planning Algorithms[M]. Cambridge: Cambridge University Press, 2006.
[61] Frazzoli E, Dahleh M A, Feron E. Real-time Motion Planning for Agile Autonomous Vehicles[J]. AIAA Journal of Guidance, Control, and Dynamics, 2002, 25(1): 116-129.
[62] Bellingham J, Tillerson M, Richards A, How J P. Multi-task Allocation and Path Planning for Cooperative UAVs[M]. Cooperative Control: Models, Applications and Algorithms. Denver, CO, 2003, 23-41.
[63] Kuwata Y, How J P. Three Dimensional Receding Horizon Control for UAVs[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. 2004.
[64] Beard R W, McLain T W, Goodrish M, Andeson E P. Coordinated Target Assignment and Intercept for Unmanned Air Vehicles[J]. IEEE Transactions on Robotics and Automation. December 2002, 18(3): 911-922.
[65] McLain T W, Chandler P R. Cooperative Control of UAV Rendezvous[C]. In Proceedings of American Control Conference. 2001, 2309-2314.
[66] Pettersson P O, Doherty P. Probabilistic Roadmap Based Path Planning for an Autonomous Unmanned Aerial Vehicle[C]. In Proceedings of the Workshop on Connecting Planning and Theory with Practice. 2004.
[67] LaValle S M. Rapidly-Exploring Random Trees: A New Tool for Path Planning[R]. Computer Science Department, Iowa State University. 1998.
[68] Yershova A, Jaillet L, Simeon T, LaValle S M. Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain[C]. In Proceedings of the IEEE International Conference on Robotics and Automation. Barcelona, Spain. 2005: 3856-3861.
[69] Melchior N A, Simmons R. Particle RRT for Path Planning with Uncertainty[C]. In Proceedings of the IEEE International Conference on Robotics and Automation. Roma, Italy. 2007: 1617-1624.
[70] Anderson E P, Beard R W, McLain T W. Real Time Dynamic Trajectory Smoothing for Unihabited Aerial Vehicles[J]. IEEE Transaction on Control Systems Technology. 2005, 13(3): 471-477.
[71] Szczerba R J. Robust Algorithm for Real-time Route Planning[J]. IEEE Transaction on Aerospace and Electronic System. 2000, 36(3): 869-878.
[72] Gudaitis M S. Multi-criteria Mission Route Planning Using a Parallel A* Search[R]. Technical Report. AFIT/GCS/ENG/94D-05, AD-A289284, 1994.
[73] Jarle A, Leif H B. Flight Path Planning System for Autonomous Unmanned Aerial Vehicles[R]. Project Report. Norwegian University of Science and Technology, 2004.
[74] Rahtbun D, Kragelund S, Pongpunwattana A, et al. An Evolution Based Path Planning Algorithm for Autonomous Motion of a UAV Through Uncertain Environments[C]. In Proceedings of the 21st AIAA Digital Avionics Systems Conference. Irvine, CA, 2003.
[75] Nikolos I K. Evolutionary Algorithm Based Offline/Online Path Planner for UAV Navigation[J]. IEEE Transaction on Systems, Man, and Cybernetics– Part B: Cybernetics, 2003, 33(6):898-912.
[76] Foo J, Knutzon J, Oliver J, Winer E. Three-dimensional Path Planning of Unmanned Aerial Vehicles Using Particle Swarm Optimization[C]. AIAA 2006-6995.
[77] Tom S. Safe Trajectory Planning of Autonomous Vehicles[D]. Ph.D. thesis, Massachusetts Institute of Technology, USA, 2005.
[78] Gotoand Y, Stentz A. Mobile Robot Navigation: The CMU System[J]. IEEE Expert. 1987, 2(4): 44-55.
[79] Korf R. Real-time Heuristic Search: Research Issues[J]. Artificial Intelligence. 1990, 42(2): 189-211.
[80] Sjanic Zoran. On-line Mission Planning based on Model Predictive Control[D]. Master’s thesis, Division of Automatic Control Department of Electrical Engineering, Linkoing University, 2001.
[81] Richards A, How J P. Decentralized Model Predictive Control of CooperatingUAVs[C]. In Proceedings of the 43rd IEEE Conference on Control and Decision. 2004, 4: 4286-4291.
[82] Chandler P R, Rasmussen S, Pachter M. UAV Cooperative Path Planning[C]. In Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit. Denver, Colorado, 2000.
[83] McLain T W, Beard R W. Cooperative Path Planning for Timing-critical Missions[C]. In Proceedings of American Control Conference. Denver, Colorado, 2003: 296-301.
[84] McLain T W, Beard R W. Coordination Variables, Coordination Functions, and Cooperative Timing Missions[J]. Journal of Guidance, Control, and Dynamics. 2005, 28(1) : 150-161.
[85] Beard R W, McLain T W, Nelson D B. Decentralized Cooperative Aerial Surveillance Using Fixed-Wing Miniature UAVs[J]. Proceedings of the IEEE. 2006, 94(7): 1306-1324.
[86] Boivin E, Desbiens A, Gagnon E. UAV Collision Avoidance Using Cooperative Predictive Control[C]. In Proceedings of the 16th Mediteranean Conferece on Control and Automation Congress Centre. Ajaccio, France, 2008: 682-688.
[87] Richards A, How J P. Aircraft Trajectory Planning with Collision Avoidance Using Mixed Integer Linear Programming[C]. In Proceedings of the American Control Conference. Anchorage, AK, USA. 2002: 1936-1941.
[88] Schouwenaars T. How J P, Feron E. Decentralized Cooperative Trajectory Planning of Multiple Aircraft with Hard Safety Guarantees[C]. In Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Providence, Rhode Island. AIAA-2004-5141. 2004.
[89] Kuwata Y, How J P. Robust Cooperative Decentralized Trajectory Optimization using Receding Horizon MILP[C]. In Proceedings of the 2007 American Control Conference. New York City, USA, July 11-13, 2007.
[90] Fasano G, Accardo D, Moccia A, et al. Multi-sensor based Fully Autonomous Non-Cooperative Collision Avoidance System for UAVs[C]. In Proceedings of the AIAA Infotech@Aerospace Conference and Exhibit. Rohnert Park, California. AIAA-2007-2847. 2007.
[91]余周毅,陈宗基,周锐.基于遗传算法的动态资源调度问题研究[J].控制与决策. 2004, 19(11): 1308-1311.
[92]冯琦,周德云.应用单亲遗传算法进行大规模UCAV任务分配.火力与指挥控制[J]. 2006, 31(5):18-21.
[93]严平.无人飞行器航迹规划与任务分配方法研究[D].博士学位论文,华中科技大学武汉,武汉, 2006.
[94]余舟毅,陈宗基,周锐.基于遗传算法的动态资源调度问题研究[J].控制与决策. 2004, 19(11): 1308-1311.
[95]段海滨,丁全心,常俊杰,刘森琪.基于并行蚁群优化的多无人作战飞机任务分配仿真平台[J].航空学报. 2008, 5(s1): 192-197.
[96]曹攀峰,崔升.考虑信息延迟的无人机分布式协同搜索算法[J].电光与控制. 2010, 17(3): 27-29+34.
[97]廖沫,陈宗基.基于满意决策的多机协同目标分配算法[J].北京航空航天大学学报. 2007, 33(1): 81-85.
[98]宋敏,魏瑞轩,冯志明.基于差分进化算法的异构多无人机任务分配[J].系统仿真学报. 2010, 22(7): 150-154.
[99]潘峰,陈杰,任智平,王光辉.基于计算智能方法的无人机任务指派约束优化模型研究[J].兵工学报. 2009, 30(12): 140-147.
[100]刘毅,李为民,邢清华,徐小来.基于双层规划的攻击无人机协同目标分配优化[J].系统工程与电子技术. 2010, 32(3): 579-583.
[101]郭文强,高晓光,任佳,肖秦琨.基于图模型自主优化的多无人机多目标攻击[J].系统工程与电子技术. 2010, 32(3): 574-578.
[102]符小卫,高晓光.一种无人机路径规划算法研究[J].系统仿真学报. 2004, 14(1): 20-21+34.
[103]肖秦琨,高晓光.基于空间改进型Voronoi图的无人机路径规划研究[J].计算机工程与应用. 2005, 26:204-207.
[104]郑昌文,丁明跃,周成平等.多飞行器协调航迹规划方法.宇航学报. 2003, 24(2): 115-120.
[105]柳长安,王和平,李为吉.攻击无人机的协同航路规划.西北工业大学学报. 2003, 21(6): 707-710.
[106]高晓光,符小卫,宋绍梅.多UCAV航迹规划研究.系统工程理论与实践. 2004, 24(5): 140-143.
[107]严平,丁明跃,周成平,郑昌文.飞行器多任务在线实时航迹规划.航空学报. 2004, 25(9): 485-489.
[108]唐强,张新国,刘锡成.一种用于低空飞行的在线航迹重规划方法.西北工业大学学报. 2005, 23(2): 271-275.
[109]曾佳,申功璋,杨凌宇.无人机在线协同航迹规划时序问题.南京航空航天大学学报. 2009, 31(3): 52-56.
[110]严平,丁明跃,郑昌文.基于Nash均衡与进化计算的协调航迹规划.航空学报(英文版). 2006, 19(1): 20-25.
[111]丁晓东,刘毅,李为民.基于动态RCS的无人机航迹实时规划方法研究.系统工程与电子技术. 2008, 30(5): 90-93.
[112]唐上钦,黄长强,胡杰,吴文超.基于威胁等效和改进PSO算法的UCAV实时航路规划方法.系统工程与电子技术. 2010, 32(8): 1706-1710.
[113]叶媛媛.多UCAV协同任务规划方法研究.博士学位论文,国防科学技术大学,长沙, 2005.
[114]龙涛.多UCAV协同任务控制中分布式任务分配与任务协调技术研究.博士学位论文,国防科学技术大学,长沙, 2006.
[115]田菁.多无人机协同侦察任务问题建模与优化技术研究.博士学位论文,国防科学技术大学,长沙, 2007.
[116]霍霄华.多UCAV动态协同任务规划建模与滚动优化方法研究.博士学位论文,国防科学技术大学,长沙, 2008.
[117]彭辉.分布式多无人机协同区域搜索中的关键问题研究.博士学位论文,国防科学技术大学,长沙, 2009.
[118] Boyd J R. The Essence of Winning and Losing[EB/OL]. http://www. chetrichards.com/modern_business_strategy/boyd/essence/eowl_frameset.htm. 2006-08-12/2009-09-13.
[119] Grant T. Comparing OODA & Other Models as Operational View C2 Architecture [C]. In Proceedings of the 10th International Command and Control Research and Technology Symposium. Washington, DC, USA. 2005.
[120] Valavanis K P. Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy[M]. Netherlands: Springer Press. 2007: 537-540.
[121] Air Force Pamphlet 14-210. USAF Intelligence Targeting Guide[M]. DOD, 1998.
[122] JP-1-02. Department of Defense Dictionary of Military and Associated Terms. Director for Operational Plans and Joint Force Development (J-7)[M]. DOD, 2005.
[123] Chen C. R, Blankenship L G. Dynamic Programming Equations for Discounted Constrained Stochastic Control[J]. IEEE Transaction on Automatic Control, 2004, 49(5): 699-709.
[124]任德华,卢桂章.对队形控制的思考[J].控制与决策,2005,20(6):601-606.
[125] Lemay M, Michaud F, Letourneau D, et al. Autonomous Initialization of Robot Formations[C]. In Proceedings of IEEE International Conference on Robotics and Automation. 2004: 3018-3023.
[126]胡跃明.非线性控制系统理论与应用[M].北京:国防工业出版社. 2005.
[127] Tomonari F. Time-Optimal Coordinated Control of the Relative Formation of Multiple Vehicles[C]. In Proceedings of 2003 IEEE International symposium on computational intelligence in Robotics and Automation. Kobe, Japan: IEEE, 2003. 259-264.
[128]俞辉,王永骥,程磊.基于有向网络的智能群体群集运动控制[J].控制理论与应用, 2007, 24(1): 79-83.
[129]吴正平,关治洪,吴先用.基于一致性理论的多机器人系统队形控制[J].控制与决策, 2007, 22(11): 1241-1244.
[130]王振国,陈小前,罗文彩,张为华.飞行器多学科设计优化理论与应用研究[M].北京:国防工业出版社. 2006:50-56.
[131] Camponogara E, Jia D, Krogh B H, et al. Distributed Model Predictive Control[J]. IEEE Control Systems Magazine. 2002, 22(1): 44–52.
[132] Camponogara E. Controlling Networks with Collaborative Nets[D]. PhD. Thesis, Carnegie Mellon University, 2000.
[133] Boche H, Stanczak S. Convexity of Some Feasible QoS Regions and Asymptotic Behavior of the Minimu Total Power in CDMA Systems[J]. IEEE Transactions on Communications, 2004, 53(12): 2190-2197.
[134] Palomar D P, Chiang M. A Tutorial on Decomposition Methods for Network Utility Maximization[J]. IEEE Journal on Selected Areas in Communication. 2006, 24(8): 1439-1451.
[135] Boyd S, Vandenberghe L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004.
[136] Bertsekas D. P., Nedic A., Ozdaglar A. Convex Analysis and Optimization[M]. Belmont: Athena Scientific. 2003.
[137] Nedic A, Bertsekas D P. Incremental Subgradient Methods for Nondifferentiable Optimization[J]. SIAM Journal on Optimization. 2001, 12(1): 109-138.
[138] Ram S S, Nedic A, Veeravalli V V. Incremental Stochastic Subgradient Algorithms for Convex Optimization[J]. SIAM Journal on Optimization. 2009, 20(2): 691-717.
[139] Nedic A, Ozdaglar A. On the Rate of Convergence of Distributed Subgradient Method for Multi-agent Optimization[C]. In Proceedings of the 46th IEEE Conference on Decision and Control. New Orleans, USA. 2007: 4711-4716.
[140] Nedic A, Ozdaglar A, Parrilo P A. Constrained Consensus and Optimization in Multi-Agent Networks[J]. IEEE Transactions on Automatic Control. 2010, 55(4): 922-938.
[141] Mathews G, Durrant-Whyte H, Prokopenk M. Asynchronous Gradient-Based Optimisation for Team Decision Making[C]. In Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, 2007: 3145-3150.
[142] Nedic A, Ozdaglar A. Convergence Rate for Consensus with Delays[J]. Journal of Global Optimization. 2010: 47(3): 437-456.
[143] Adelman D, Mersereau J A. Relaxations of Weakly Coupled Stochastic Dynamic Programs[J]. Operations Research. 2008, 56(3): 712-727.
[144] Hawkins J. A Lagrangian Decomposition Approach to Weakly Coupled DynamicOptimization Problems and its Applications[D]. Ph.D. thesis, Massachusetts Institute of Technology, USA, 2003.
[145] Castanon A D. Stochastic Control Bounds on Sensor Network Performance[C]. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference. Seville, Spain, 2005: 4939-4944.
[146] Wang J, Luh P B, Zhao X, Wang J. An Optimization-based Algorithm for Job Shop Scheduling[J]. SADHANA, 1997, 22(2): 241-256.
[147] Adelman D. A Price-directed Approach to Stochastic Inventory/Routing[J]. Operations Research. 2004, 52(4): 499-514.
[148] Bertsimas D, Mersereau A. A Learning Approach for Interactive Marketing to a Customer Segment[R]. Working Paper. University of Chicago GSB, 2006.
[149] Growe-Kuska N, Kiwiel K C. Short-term hydropower production planning by stochastic programming[J]. Computers and Operations Research. 2008, 35(8): 2656-2671.
[150] Sudipto G, Kamesh M. Approximation Algorithms for Partial-information based Stochastic Control with Markovian Rewards[C]. In Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science. IEEE Computer Society, Washington DC, USA, 2007: 483-493.
[151]刘克.实用马尔可夫决策过程[M].北京:清华大学出版社. 2004.
[152] Cassandra R A. Exact and Approximate Algorithms for Partially Observable Markov Decision Processes. Ph. D. thesis, Brown University, Providence, Rhode Island, 1998.
[153] Lovejoy S W. A Survey of Algorithmic Methods for Partially Observable Markov Decision Processes[J]. Annals of Operations Research. 1991, 28(1): 47-65.
[154] Striebel T V. Sufficient Staticstics in the Optimal Control of Stochastic Systems[J]. Journal of Mathematical Analysis and Applications. 1965, 12:576-592.
[155]陈华东,王树宗,王航宇.基于混合粒子群算法的多平台多武器火力分配研究[J].系统工程与电子技术. 2008, 30(5): 880 - 883.
[156] David G G. Game theoretic target assignment strategies in competitive multi-team systems[D]. PhD. thesis,University of Pittsburgh, Pittsburgh, PA, 2004.
[157] Rosenberger J M, Yucel A. The Generalized Weapon Target Assignment Problemp[C]. In Proceedings of the 10th International Command and Control Research and Technology Symposium, 2005.
[158]吴玲,卢兴发,贾培发.动态武器目标分配问题中改进遗传算法的元级控制[J].清华大学学报. 2008, 48(S2): 1762-1765.
[159] Wacholder E. A neural network-based optimization algorithm for the static weapon assignment problem[J]. ORSA Journal on Computing. 1989, 4: 232– 246.
[160] Cheng H. Algorithms for Partially Observable Markov Decision Processes[D]. Ph. D. thesis, University of British Columbia, 1988.
[161] Sondik J. Edward. The Optimal Control of Partially Observable Markov Processes[D]. Ph.D. thesis, Stanford University, Palo Alto, CA, 1971.
[162] Kamal W A, Gu D W, Postlethwaite I. Real Time Trajectory Planning for UAVs Using MILP[C]. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference. Seville, Spain, 2005: 3381-3386.
[163]黄红选,韩继业.数学规划[M].北京:清华大学出版社. 2006: 237-240.
[164]《现代应用数学手册》编委会.运筹学与最优化理论卷[M].北京:清华大学出版社. 1998: 182-185.
[165] Cowling I. Toward Autonomy of Quadrotor UAV[D]. Ph.D. thesis, Cranfield University, 2008.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |