基于智能优化与RRT算法的无人机任务规划方法研究
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摘要
无人机系统向着智能化、自主化的方向发展。任务规划是无人机自主控制的重要组成部分。本文对无人机任务规划中的航迹规划和任务分配问题进行了研究。
针对三维静态威胁环境下的无人机航迹规划问题，提出了两种改进蚁群算法。多重启发蚁群算法综合考虑无人机当前位置、待选位置以及目标位置三者之间的距离和威胁分布，并将这些先验知识构造为蚂蚁的多重启发信息，指引蚂蚁的路径搜索，证明了该算法的全局收敛性。同时提出了一种将人工势场法与蚁群算法相结合的人工势场蚁群算法，能够按照节点位置的势场力分布，进行确定性选择和概率性选择相结合的蚂蚁状态转移。试验结果表明两种改进算法能够获得优于单一的人工势场法和蚁群算法的规划结果，有效地缩短航迹规划时间，提高规划精度，得到最优的飞行航迹。
提出了一种改进混合粒子群算法，在标准粒子群算法之中融入Boids鸟群模型的避撞机制，用以摆脱局部极小点的束缚，利用Powell算法对全局极值进行局部搜索。对改进混合粒子群算法的收敛性进行了证明。仿真结果表明该算法能够改善标准粒子群算法局部搜索能差和早熟收敛的不足。针对多无人机协同航迹规划问题，提出了一种威胁启发粒子群算法，将任务环境中的威胁信息，构造为粒子速度更新公式的一部分，用以指引粒子向着远离威胁区域的方向移动。建立了同时考虑时域协同和空域协同的多无人机协同航迹规划数学模型。采用两阶段的协同规划框架，首先利用k-均值聚类方法对粒子群进行聚类，得到每架无人机的多条待选航迹，然后再通过协同变量和协同函数处理协同约束。通过仿真试验验证了所提方法的有效性和先进性。
研究了快速扩展随机树算法（Rapid-exploring Random Tree, RRT）的参数设置对算法性能的影响，提出了RRT算法参数选取的指导原则。在此基础上，提出了利用混沌序列生成随机节点，利用模糊推理系统动态调整算法参数的改进RRT算法。针对突发威胁环境下的航迹重规划问题，提出了一种改进双边RRT算法。在出现突发威胁后，对原有随机树进行节点删减，保留未受到影响的残余随机树，再根据无人机的当前位置，进行双边RRT随机树生长，同时设计了一种航迹修剪方法，用于去除冗余航点，并采用贝塞尔曲线进行航迹平滑。针对未知环境中的航迹规划问题，提出了滚动RRT算法，每次只生成探测范围内的局部航迹，无人机在按照该局部航迹飞行的同时进行下一阶段的航迹滚动优化。设计了一种滚动窗口内的随机节点选择方法，能够引导随机树较快的向着滚动窗口的边界生长。试验结果说明所提方法有效地能够满足不确定环境下的在线航迹规划需求，得到满意的规划结果。
针对多无人机协同任务分配问题，提出了一种多组群蚁群算法，设计了综合考虑无人机攻击收益、生存概率、航程代价的代价收益指标。多组群蚁群算法按照综合剩余任务能力进行蚂蚁选择，根据各个待选任务目标的状态转移概率，确定被选中蚂蚁需要添加的任务。利用2-opt算法进行进一步的局部优化。同时给出了任务需求或无人机群发生变化时的任务重分配方法。试验结果表明，多组群蚁群算法能够很好地获得满足各项约束条件的无人机任务分配方案，各架无人机的任务较为均衡，任务重分配方法具有良好的实时性，能够满足新任务的要求。
设计了一款小型无人机的航迹规划系统，包括数据录入、航迹生成、飞行仿真与验证，以及航迹加载与校验等环节。以靶试需求为例，进行复杂高原地形环境下的航迹规划应用，试验结果表明该航迹规划系统能够满足实际任务需求，取得预期的效果。
The tendency of UAV system is aim to be intelligent and autonomous. Mission planningoccupies an important position in designing of the future UAV system, which is also one of the keytechnologies in the realization of the UAV’s autonomous control. UAV route planning and taskallocation are two major problems studied in this paper.
For the problem of UAV route planning under three-dimensional and static threat circumstance, akind of multiple heuristic ant colony algorithm is proposed. This algorithm analyzes the condition ofthe distance and the threat distribution among the current position, candidate position and targetposition of UAV. And these above conditions are utilized to be the multiple heuristic information inthe state transition of ants, which can help determine ants foraging behaviors. Moreover, theconvergence of this multiple heuristic ant colony algorithm is proved. In the meantime, an artificialpotential ant colony optimization algorithm (APACO) is proposed, which combines ant colonyoptimization algorithm with artificial potential field. According to the potential force distribution, thestate transition rules comprise deterministic selection and probability selection. The experimentalresults show that the proposed algorithm for the route planning is better than both of simplex artificialpotential field algorithm and ant colony optimization algorithm. Double improved algorithms caneffectively shorten the route planning time, improve planning accuracy, and ultimately achieve anoptimal path.
An improved hybrid particle swarm optimization (IHPSO) algorithm is proposed, whichintegrates the Boids model and the Powell algorithm. Collision avoidance mechanism is integratedinto PSO to get rid of the shackles of local minima, and then Powell algorithm is for further localsearch. The convergence of IHPSO algorithm is proved. The test results show that, compared with thestandard particle swarm algorithm, IHPSO has a better accuracy and success rate. In light of themultiple UAVs cooperative route planning problem, a threat heuristic particle swarm algorithm isbrought out. Threat information is added as part of the particle velocity updating formula, which cankeep particles far away from threat area. The two stages of collaborative planning framework areadopted. Firstly, using k-means clustering method to classify particle swarm and get multiplecandidate paths for each UAV. Then, through the coordination variable and coordination function,cooperative constraints can be tackled. According to results of the simulation, the validity of theproposed algorithm is proved.
The relation between parameters setting and the performance of RRT algorithm is studied.Meanwhile, the guiding principle for the selection of parameters in RRT algorithm is put forward. On this basis, an improved RRT algorithm is proposed. This algorithm uses chaotic sequence to generaterandom node, and uses the fuzzy inference system to adjust algorithm parameters dynamically. Animproved dual RRT algorithm for route replanning in popup threat environment is proposed. Thisalgorithm can make full use of the existing off-line route information, delete invalid nodes, andpreserve residual random tree uninfluenced by popup threats, then according to the UAV's currentlocation, execute improved dual RRT growth. A path pruning method for the removal of redundantwaypoints is designed.The Bessel curve is used for improving path smoothness. For unknownenvironment route planning problem, a rolling RRT algorithm is proposed. The execution process is akind of alternated rolling route planning of the UAV flight curve. A random node selection method isproposed, which can guide the tree faster approximate the rolling window boundary. The experimentresults show that the proposed method effectively meets the requirements of the unknownenvironment online route planning, and achieves the satisfied results with the rolling planning.
A multiple group ant colony algorithm (MGACO) is proposed for the condition of multipleUAVs attacking multiple ground targets. Design of comprehensive optimum index includes UAVattack benefits, survival probability, distance cost. According to the comprehensive remaining ability,choosing the ant to perform state transition, and then deciding which task arranged to this ant. Using2-opt algorithm for further local optimization. The experiment results show that the task allocationscheme generated by MGACO algorithm can satisfy various constraints and need less iteration. Thetask load of different UAVs is relatively balanced.
A route planning system is designed for a small UAV, including data entry, route generation,flight simulation and verification, and route loading and checking etc. This system sets taskspossession by software interfaces, and can realize the route planning, verification and upload. At last,an actual example is given to verify the route planning system.
针对三维静态威胁环境下的无人机航迹规划问题，提出了两种改进蚁群算法。多重启发蚁群算法综合考虑无人机当前位置、待选位置以及目标位置三者之间的距离和威胁分布，并将这些先验知识构造为蚂蚁的多重启发信息，指引蚂蚁的路径搜索，证明了该算法的全局收敛性。同时提出了一种将人工势场法与蚁群算法相结合的人工势场蚁群算法，能够按照节点位置的势场力分布，进行确定性选择和概率性选择相结合的蚂蚁状态转移。试验结果表明两种改进算法能够获得优于单一的人工势场法和蚁群算法的规划结果，有效地缩短航迹规划时间，提高规划精度，得到最优的飞行航迹。
提出了一种改进混合粒子群算法，在标准粒子群算法之中融入Boids鸟群模型的避撞机制，用以摆脱局部极小点的束缚，利用Powell算法对全局极值进行局部搜索。对改进混合粒子群算法的收敛性进行了证明。仿真结果表明该算法能够改善标准粒子群算法局部搜索能差和早熟收敛的不足。针对多无人机协同航迹规划问题，提出了一种威胁启发粒子群算法，将任务环境中的威胁信息，构造为粒子速度更新公式的一部分，用以指引粒子向着远离威胁区域的方向移动。建立了同时考虑时域协同和空域协同的多无人机协同航迹规划数学模型。采用两阶段的协同规划框架，首先利用k-均值聚类方法对粒子群进行聚类，得到每架无人机的多条待选航迹，然后再通过协同变量和协同函数处理协同约束。通过仿真试验验证了所提方法的有效性和先进性。
研究了快速扩展随机树算法（Rapid-exploring Random Tree, RRT）的参数设置对算法性能的影响，提出了RRT算法参数选取的指导原则。在此基础上，提出了利用混沌序列生成随机节点，利用模糊推理系统动态调整算法参数的改进RRT算法。针对突发威胁环境下的航迹重规划问题，提出了一种改进双边RRT算法。在出现突发威胁后，对原有随机树进行节点删减，保留未受到影响的残余随机树，再根据无人机的当前位置，进行双边RRT随机树生长，同时设计了一种航迹修剪方法，用于去除冗余航点，并采用贝塞尔曲线进行航迹平滑。针对未知环境中的航迹规划问题，提出了滚动RRT算法，每次只生成探测范围内的局部航迹，无人机在按照该局部航迹飞行的同时进行下一阶段的航迹滚动优化。设计了一种滚动窗口内的随机节点选择方法，能够引导随机树较快的向着滚动窗口的边界生长。试验结果说明所提方法有效地能够满足不确定环境下的在线航迹规划需求，得到满意的规划结果。
针对多无人机协同任务分配问题，提出了一种多组群蚁群算法，设计了综合考虑无人机攻击收益、生存概率、航程代价的代价收益指标。多组群蚁群算法按照综合剩余任务能力进行蚂蚁选择，根据各个待选任务目标的状态转移概率，确定被选中蚂蚁需要添加的任务。利用2-opt算法进行进一步的局部优化。同时给出了任务需求或无人机群发生变化时的任务重分配方法。试验结果表明，多组群蚁群算法能够很好地获得满足各项约束条件的无人机任务分配方案，各架无人机的任务较为均衡，任务重分配方法具有良好的实时性，能够满足新任务的要求。
设计了一款小型无人机的航迹规划系统，包括数据录入、航迹生成、飞行仿真与验证，以及航迹加载与校验等环节。以靶试需求为例，进行复杂高原地形环境下的航迹规划应用，试验结果表明该航迹规划系统能够满足实际任务需求，取得预期的效果。
The tendency of UAV system is aim to be intelligent and autonomous. Mission planningoccupies an important position in designing of the future UAV system, which is also one of the keytechnologies in the realization of the UAV’s autonomous control. UAV route planning and taskallocation are two major problems studied in this paper.
For the problem of UAV route planning under three-dimensional and static threat circumstance, akind of multiple heuristic ant colony algorithm is proposed. This algorithm analyzes the condition ofthe distance and the threat distribution among the current position, candidate position and targetposition of UAV. And these above conditions are utilized to be the multiple heuristic information inthe state transition of ants, which can help determine ants foraging behaviors. Moreover, theconvergence of this multiple heuristic ant colony algorithm is proved. In the meantime, an artificialpotential ant colony optimization algorithm (APACO) is proposed, which combines ant colonyoptimization algorithm with artificial potential field. According to the potential force distribution, thestate transition rules comprise deterministic selection and probability selection. The experimentalresults show that the proposed algorithm for the route planning is better than both of simplex artificialpotential field algorithm and ant colony optimization algorithm. Double improved algorithms caneffectively shorten the route planning time, improve planning accuracy, and ultimately achieve anoptimal path.
An improved hybrid particle swarm optimization (IHPSO) algorithm is proposed, whichintegrates the Boids model and the Powell algorithm. Collision avoidance mechanism is integratedinto PSO to get rid of the shackles of local minima, and then Powell algorithm is for further localsearch. The convergence of IHPSO algorithm is proved. The test results show that, compared with thestandard particle swarm algorithm, IHPSO has a better accuracy and success rate. In light of themultiple UAVs cooperative route planning problem, a threat heuristic particle swarm algorithm isbrought out. Threat information is added as part of the particle velocity updating formula, which cankeep particles far away from threat area. The two stages of collaborative planning framework areadopted. Firstly, using k-means clustering method to classify particle swarm and get multiplecandidate paths for each UAV. Then, through the coordination variable and coordination function,cooperative constraints can be tackled. According to results of the simulation, the validity of theproposed algorithm is proved.
The relation between parameters setting and the performance of RRT algorithm is studied.Meanwhile, the guiding principle for the selection of parameters in RRT algorithm is put forward. On this basis, an improved RRT algorithm is proposed. This algorithm uses chaotic sequence to generaterandom node, and uses the fuzzy inference system to adjust algorithm parameters dynamically. Animproved dual RRT algorithm for route replanning in popup threat environment is proposed. Thisalgorithm can make full use of the existing off-line route information, delete invalid nodes, andpreserve residual random tree uninfluenced by popup threats, then according to the UAV's currentlocation, execute improved dual RRT growth. A path pruning method for the removal of redundantwaypoints is designed.The Bessel curve is used for improving path smoothness. For unknownenvironment route planning problem, a rolling RRT algorithm is proposed. The execution process is akind of alternated rolling route planning of the UAV flight curve. A random node selection method isproposed, which can guide the tree faster approximate the rolling window boundary. The experimentresults show that the proposed method effectively meets the requirements of the unknownenvironment online route planning, and achieves the satisfied results with the rolling planning.
A multiple group ant colony algorithm (MGACO) is proposed for the condition of multipleUAVs attacking multiple ground targets. Design of comprehensive optimum index includes UAVattack benefits, survival probability, distance cost. According to the comprehensive remaining ability,choosing the ant to perform state transition, and then deciding which task arranged to this ant. Using2-opt algorithm for further local optimization. The experiment results show that the task allocationscheme generated by MGACO algorithm can satisfy various constraints and need less iteration. Thetask load of different UAVs is relatively balanced.
A route planning system is designed for a small UAV, including data entry, route generation,flight simulation and verification, and route loading and checking etc. This system sets taskspossession by software interfaces, and can realize the route planning, verification and upload. At last,an actual example is given to verify the route planning system.
引文
[1]魏瑞轩,李学仁.无人机系统及作战使用.北京:国防工业出版社,2009.
[2] Office of the Secretary of Defence. Unmanned Systems Integrated Roadmap2009-2034.Washington D C: Office of the Secretary of Defence,2009.
[3] Huang H M, Pavek K,Novak B,et al. A framework for autononmy levels for unmanned systems.Proceeding of AUVSI’s Unmanned System North America,Baltimore,2005:849-863
[4] Yang Y, Minai A A, Polycarpou M M. Decentralized cooperative search in UAV’s usingopportunistic learning. AIAA Guidance, Navigation and Control Conference, Monterey,2002:2002-4590
[5] Pachter M, Chandler P R.Challenges of autonomous control. IEEE Control Systems Magazine,1998,18(4):92-97.
[6]沈林成,徐昕.移动机器人自主控制理论与技术.北京:科学出版社,2011.
[7]朱华勇,牛轶峰,沈林成等.无人机系统自主控制技术研究现状与发展趋势.国防科技大学学报,2010,32(3):115-120.
[8]王英勋,蔡志浩.无人机的自主飞行控制.航空制造技术,2009,8(8):26-31.
[9] Young L A, Yetter J A, Guynn M D. System analysis applied autonomy: Application tohigh-altitude long endurance remotely operated aircraft. Proceedings of the AIAA InfotechAerospace Conference. Arlington2005:22-31.
[10] Naval S B. Autonomous Vehicles in Support o naval Operations. Washington D C: NationalResearch Council,2005.
[11] LTC Warren O’Donell. Future combat system review. Washington D C: Office of assistantSecretary of the navy,2003.
[12]陈宗基，魏金钟，王英勋等.无人机自主控制等级及其系统结构研究.航空学报,2011,32(6):1075-1083.
[13] Suresh M, Ghose D. Role of information and communication in redefining unmanned aerialvehicle autonomous control levels, Proceedings of the Institution of Mechanical Engineers, PartG: Journal of Aerospace Engineering,2010,224(2):171-197.
[14] Johnson T L, SutherlandH A, Bush S F, et al. The TRAC Mission Manager AutonomousControl Executive.Proceeding of IEEE Aerospace Conference. Big Sky:IEEE,2001:2639-2646.
[15] Peng K M, Cai G W, Chen B M. Design and implementation of an autonomous flight controllaw for a UAV helicopter. Automatica,2009,45(10):2333-2338.
[16] Reichard K M, Banks J, Eddie C C, et al. Intelligent Self Situational Awareness for IncreasedAutonomy, Reduced Operational Risk, and Improved Capability.1st Space ExplorationConference. Orlando,2005:946-953.
[17] Cheng Z Q. Dynamic Inversion and Model Predictive Control for Unmanned Aerial Vehicles.Doctoral dissertation, University of Ottawa,Canada,2004.
[18] Tisdale J, Kim Z W, Hedrick J K. Autonomous UAV path planning and estimation: An onlinepath planning framework for cooperative search and localization. IEEE Robotics andAutomation Magazine,2009,16(2):35-42.
[19] Sharma R K., Ghose D. Collision avoidance between UAV clusters using swarm intelligencetechniques. International Journal of Systems Science,2009,40(5):521-538.
[20] Remenyte P R, Andrews, J D,Chung P W H. An efficient phased mission reliability analysis forautonomous vehicles. Reliability Engineering and System Safety,2010,95(3):226-235.
[21] Fabiani P, Fuertes V, Piquereau A. Autonomous flight and navigation of VTOL UAVs: fromautonomy demonstrations to out-of-sight flights. Aerospace Science and Technology,2007,11(2-3):183-193.
[22]周锐,李惠峰,陈宗基.无人战术飞行器的自主控制.控制与决策,2001,16(3):344-346.
[23]池沛,陈宗基,周锐.自主飞行器扩展态势评估方法研究.系统仿真学报,2007,19(10):2200-2205.
[24]牛轶峰,沈林成,龙涛.攻击型无人飞行器自主控制技术研究综述.系统工程与电子技术，2007,29(3):391-395.
[25]谭雁英,赵荣椿,祝小平等.基于智能Agent的小型无人机自主飞行任务管理器的设计.西北工业大学学报,2006,24(6):754-758.
[26]周锐,吴雯漫,罗广文.自主多无人机的分散化协同控制.航空学报,2008,29(S):26-32.
[27]吴立珍,沈林成,牛轶峰.无人机战场环境感知与理解技术研究.系统仿真学报,2010,22(S):79-84.
[28]王宏伦,王英勋.无人机飞行控制与管理.航空学报.2008,29(S1):1-7.
[29]宗令蓓,谢凡,秦世引.基于MAS的无人机编队飞行智能优化控制.航空学报,2008,29(5):1326-1332.
[30]段海滨,刘森琪.空中/地面机器人异构协同技术研究现状和展望.中国科学:技术科学,2010,40(9):1029-1036.
[31]叶文.无人飞行器任务规划.北京:国防工业出版社,2011.
[32] Vachtsevanos G, Tang L, Drozeski G. From mission planning to flight control of unmannedaerial vehicles: Strategies and implementation tools. Annual Reviews in Control,2009,29(1):101-115
[33]郑昌文,严平,丁明跃.飞行器航迹规划研究现状与趋势.宇航学报,2007,28(6):1441-1446.
[34]杜萍,杨春.飞行器航迹规划算法综述.飞行力学,2005,23(2):10-14.
[35]郑昌文,严平,丁明跃等.飞行器航迹规划.北京:国防工业出版社,2008
[36] Chaudhry A, Misovec K, D’Andrea R. Low overvability path planning for an unmanned airvehicle using mixed integer linear programming.43th IEEE Conference on Decision andControl, Bahamas:IEEE,2004:3823-3829.
[37] Karaman S,Frazzoli E.Linear temporal logic vehicle routing with applications to multi-UAVmission planning. International Journal of Robust and Nonlinear Control.2011,21(12):1372-1395.
[38] Khatib O. Real time obstacle avoidance for manipulators and mobiles robots. InternationalJournal of Robotics Research.1986,5(1):90-99.
[39]张建英,刘暾.基于人工势场法的移动机器人最优路径规划.航空学报,2007,28(S):183-188.
[40] Sven K, Maxim L. Real time adaptive A*. Proceeding of the International Conference onAutonomous Agents. New York: ACM,2006:281-288.
[41] Yang X, Ding M Y, Zhou C P. Fast Marine Route Planning for UAV Using Improved Sparse A*Algorithm. International Conference on Genetic and Evolutionary Computing. Piscataway:IEEE,2010:190-193.
[42] Szczerba R J. Threat Netting for real time,intelligent route planning. The Proceedings of theInformation, Decision and Control Symposium. Adelaide, Australia,1999:377-382.
[43] Szczerba R J, Galkowski P, Glickstein I S, et al. Robust algorithm for real time route planning.IEEE Transactions on Aerospce and Electronic System,2000,36(3):869-878.
[44]李春华,郑昌文,周成平.一种三维航迹快速索算法.宇航学报,2002,23(3):13-17.
[45] Stentz A.Optimal and efficient route planning for partially-know environments.The Proceedingsof IEEE Conference on Robotics and Automation,1994,3310-3317.
[46] Besada P E, Luis D L T, Cruz D L,et al.Evolutionary trajectory planner for multiple UAVs inrealistic scenarios. IEEE Transactions on Robotics,2010,26(4):619-634.
[47]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].国防科技大学,2006.
[48] Pzchter M, Chandler P R. Challenges of autonomous control. IEEE Control SystemsMagazine,1998,18(4):91-97.
[49] Rabbaty C A, Gagnon E, Lauzon M. On the cooperative control of multiple unmanned aerialvehicles, IEEE Canadian Review, Spring,2004:15-18.
[50] Chandler P, Rasumussen S, Patchter M. UAV cooperative path planning. Prcceedings of AIAAGuidance,Navigation, and Control Conference, Denver:AIAA,2000:2000-4370.
[51]倪谣,周德云,马云红等.基于MILP模型的多无人机对地攻击任务分配.火力指挥与控制,2008,33(11):62-65.
[52] Schumacher C, Chandler P R, Rasmussen S R. Task allocation for wide area search munitions.Proceedings of American Control Conference, Anchorage,2002:1917-1922.
[53] Nygard K E, Chandler P R, Pachter M. Dynamic network flow optimization models for airvehicle resource allocation. Proceedings of American Control Conference, Arlinton,2001:1853-1858.
[54] Dias M B, Stentz A. Opportunistic optimization for market-based multirobot control.Proceedings of IEEE International Conference on Intelligent Robots and Systems, Switzerland,2002:2714-2720.
[55] Elad K, Kelly C, Manish K. Market-Based Solution to the Allocation of Tasks to Agents.Procedia Computer Science,2011,1(6):28–33.
[56] Chen Y L, Brian G, Matt E. Market-Based Collaborations for Autonomous Operations ofUnmanned Air Vehicle. IEEE Workshop on Distributed Intelligent Systems. Prague,2006:273-278.
[57]龙涛,朱华勇,沈林成.多UCAV协同中基于协商的分布式任务分配研究.宇航学报.2006,27(3):457-462.
[58]龙涛,沈林成,朱华勇等.面向协同任务的多UCAV分布式任务分配与协调技术.自动化学报,2007,33(7):731-737.
[59]廖沫,陈宗基.基于多Agent分布协同拍卖的动态目标分配算法.北京航空航天大学,2007,33(2):180-183.
[60] Shima T, Steven J. R, Andrew G. S.Multiple task assignments for cooperating uninhabited aerialvehicles using genetic algorithms. Computers&Operations Research.2006,33(11):3252-3269.
[61] Edison E, Shima T. Integrated task assignment and path optimization for cooperatinguninhabited aerial vehicles using genetic algorithms. Computers&Operations Research.2011,38(1):340-356.
[62] Gao N, Qu Z H, Li X J. On Modeling and Discrete Particle Swarm Optimization for TaskAssignment of Cooperating UAVs. Chinese Control and Decision Conference. Mianyang,2011:1044-1049.
[63]叶文,朱爱红,欧阳中辉.基于混合离散粒子群算法的多无人作战飞机协同目标分配.兵工学报.2010,31(3):331-336.
[64]苏菲,陈岩,沈林成.基于蚁群算法的无人机协同多任务分配.航空学报,2008,29(S):184-191.
[65]段海滨,丁全心,常俊杰.基于并行蚁群优化的多UCAV任务分配仿真平台.航空学报,2008,29(S):192-197.
[66]邢文训,谢金星.现代优化计算方法.北京:清华大学出版社,2005.
[67]汪定伟,王俊伟.智能优化算法.北京:高等教育出版社,2007.
[68] Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents.IEEE Transaction on Systems, Man and Cybernetics-Part B,1996,26(1):29-41.
[69] Kennedy J, Eberhart R. Particle swarm optimization.Proc of the IEEE Internationa1Conferenceon Neura1Networks. Piscataway: IEEE,1995:1942-1948.
[70] Eberhart R C,Kennedy J. A new optimizer using particle swarm theory. Proceedings of theSixth International Symposium on Micro Machine and Human Scienc. Nagoya Japan1995:39-43.
[71] Sabat S L,Udgata S K,Abraham A. Artificial bee colony algorithm for small signal modelparameter extraction of MESFET.Engineering Applications of Artificial Intelligence,2010,23(5):689-694．
[72] Kunz H, Hemelrijk C K. Artificial fish schools: collective effects of school size, body size, andbody form. Artificial Life,2003,9(3):237-253.
[73] Bonabeau E, Dorigo M, Theraulaz G. Inspiration for optimization from social insect behavior.Nature,2000,406(6):39-42.
[74] Michael J B K, Jean B B, Laurent K. Ant-like task recruitment in cooperative robots.Nature,2000,406(31):992-995
[75] Jackson D E, Holcombe M, Ratnieks F L W. Trail geometry gives polarity to ant foragingnetworks. Nature,2004,432(7019):907-909.
[76] DorigoM, Gambardella L M. Ant colony system: a cooperative learning approach to thetraveling salesman problem. IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[77] Stutzle T, Hoos H H. Max-min ant system.Future Generation Computer Systems,2000,16(8):889-914.
[78] Dorigo M, Maniezzo V, Colomi A. The ant system: Optimization by a colony of cooperatingagents.IEEE Transactions on Systems, Man, and Cybemetics,1996,26(l):29-41.
[79] Dorigo M, Gambardella L M. A study of some properties of ant-Q. Proceedings of the4thInternational Conference on Parallel Problem Solving from Nature, Berlin:Springer-Verlag,1996:656-665.
[80] Blum C, Dorigo M. The hyper-cube framework for ant colony optimization. IEEE Transactionson Systems, Man and Cybernetics, Part B,2004,34(2):1161-1172.
[81]丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法的融合.计算机研究与发展,2003,40(9):1351-1356.
[82] Blum C, Socha K. Training feed-forward neural networks with ant colony optimization: Anapplication to pattern classification. The Fifth International Conference on Hybrid IntelligentSystems, Rio de Janeiro:IEEE,2005:233-238.
[83]刘超华,张英杰,张兢.蚁群算法与免疫算法的融合及其在TSP中的应用.控制与决策,2010,25(5):695-700.
[84]詹士昌,徐婕,吴俊.蚁群算法中有关算法参数的最优选择.科技通报,2003,19(5):381-386.
[85] Gaertner D, Clark K. On Optimal Parameters for Ant Colony Optimization Algorithms. TheInternational Conference on Artificial Intelligence,LasVegas: CSREA Press,2005:83-89.
[86] Duan H B, Ma G J, Liu S Q. Experimental study of the adjustable parameters in basic antcolony optimization algorithm.IEEE Congress on Evolutionary Computation. Piscataway:IEEE,2008:149-156.
[87] GutjahrW J. A graph-based ant system and its convergence. Future Generation ComputerSystems,2000,16(8):873-888.
[88] Stutzle T,DorigoM. A short convergence proof for a class of ant colony optimization algorithms.IEEE Transactions on Evolutionary Computation,2002,6(4):358-365.
[89] GutjahrW J. ACO algorithms with guaranteed convergence to the optimal solution. InformationProcessing Letters,2002,82(3):145-153.
[90]段海滨,王道波.蚁群算法的全局收敛性研究及改进.系统工程与电子技术,2004,26(10):1506-1509.
[91] Huang R H, Yang C L. Ant colony system for job shop scheduling with time windows.International Journal of Advanced Manufacturing Technology,2008,39(1-2):151-157.
[92] Wang K J, Chen M J. Cooperative capacity planning and resource allocation by mutualoutsourcing using ant algorithm in a decentralized supply chain. Expert Systems withApplications,2009,36(2):2831-2842.
[93] See P C, Wong K Y. Application of ant colony optimisation algorithms in solving facility layoutproblems formulated as quadratic assignment problems: A review. International Journal ofIndustrial and Systems Engineering,2008,3(6):644-672
[94] Zhao D M, Luo L, Zhang K. An improved ant colony optimization for the communicationnetwork routing problem. Mathematical and Computer Modelling,2010,52(11-12):1976-1981.
[95]马卫,朱庆宝.求解函数优化问题的快速连续蚁群算法.电子学报,2008,38(11):2120-2124.
[96] Boubertakh H, Tadjine M, Glorennec P Y, Labiod S. Tuning fuzzy PID controllers using antcolony optimization.17th Mediterranean Conference on Control and Automation, Piscataway:IEEE,2009:13-18.
[97] Juang C F, Lu C M. Ant colony optimization incorporated with fuzzy Q-learning forreinforcement fuzzy control. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,2009,39(3):597-608.
[98] Reynolds C W. Flocks, Herds and Schools：A distributed behavioral mode1. ComputerGraphics,1987,21(4):25-43.
[99] Gallad A,Hawary M,Sallam A. Enhancing the particle swarm optimizer via proper parametersselection. Canadian Conference on Electrical and Computer Engineering. Piscataway: IEEE,2002,2:792-797.
[100] Shi Y, Eberhart R. A. modified particle swarm optimizer. IEEE International Conference ofEvolutionary Computation, Piscataway: IEEE,1998:69-73.
[101] Ratnaweera A, Halgamuge S K, Watson H C. Self-organizing hierarchical particle swarmoptimizer with time-varying acceleration coefficients. IEEE Transactions on EvolutionaryComputation,8(3),240–255,2004.
[102] Van den Bergh F, Engelbrecht A P. A study of particle swarm optimization particle trajectories.Information Sciences,2006,176(8):937-971.
[103] Clerc M, Kennedy J. The particle swarm:explosion, stability and convergence in a multi-dimensional complex space. Transactions on Evolutionary Computation,2002,6(1):58-73.
[104] Trelea I C. The particle swarm optimization algorithm: convergence analysis and parameterselection. Information Processing Letters,2003,85(6):317-325.
[105] Kadirkamanathan V, Selvarajah K, Fleming P J. Stability analysis of the particle dynamics inparticle swarm optimizer. Transactions on Evolutionary Computation,2006,10(3):245-255.
[106] Jiang M, Luo Y P, Yang S Y. Stochastic convergence analysis and parameter selection of thestandard particle swarm optimization algorithm. Information Processing Letters,2007,102(1):8-16.
[107] Jin X L, Ma L H, Wu T J, Qian J X. Convergence analysis of the particle swarm optimizationbased on stochastic processes. Acta Automatica Sinica,2007,33(12):1263-1268
[108]潘峰,陈杰,辛斌等.粒子群优化方法若干特性分析.自动化学报,2009,35(7):1010-1015
[109] LaValle S M. Rapidly-exploring random trees:A new tool for path planning. Computer ScienceDept, Iowa State University, October1998.
[110] LaValle S M, Kuffner J J. Donald B R, et al. Rapidly-exploring random trees: Progress andprospects. Algorithmic and Computational Robotics: New Directions, Wellesley,2001:293-308.
[111] Joshua R, Jayesh N A, Jovan D B. Real-time Obstacle Detection and Reactive Path PlanningSystem for Autonomous Small-Scale Helicopters，AIAA Guidance, Navigation and ControlConference and Exhibit，Hilton Head,2007:1-22.
[112] Martin S R, Wright S E, Sheppard J W. Offline and Online Evolutionary Bi-Directional RRTAlgorithms for Efficient Re-Planning in Dynamic Environments. Proceedings of the3rd AnnualIEEE Conference on Automation Science and Engineering,Scottsdale,2007:1131-1136.
[113] Ian G and Jonathan P.H. Improving the Efficiency of Rapidly-exploring Random Trees Using aPotential Function Planner. Proceedings of the44th IEEE Conference on Decision andControl,Seville,2005:7965-7970.
[114] Armando A N, Douglas G. M, Mario F M C. On the Generation of Trajectories for MultipleUAVs in Environments with Obstacles. Journal of Intelligent and Robotic Systems: Theory andApplications,2010,57(1-4):123-141.
[115] David B. Comparison of A*and RRT-connect motion planning techniques for self-reconfiguration planning. International Conference on Intelligent Robots and Systems, Beijing,2006:892-897.
[116]彭辉,王林,沈林成.区域目标搜索中基于改进RRT的UAV实时航迹规划.国防科技大学学报,2009,31(5):86-91.
[117] Rodríguez S, Tang X Y, Lien J M. An Obstacle-Based Rapidly-Exploring Random Tree. IEEEInternational Conference on Robotics and Automation, Orlando,2006:895-900.
[118] Peng H, Su F, Bu Y L,et al. Cooperative area search for multiple UAVs based RRT anddecentralized receding horizon optimization.Proceedings of the7th Asian Control Conference,Hong Kong,2009:298-303.
[119] Song P, Li J F. Hybrid Multi-robot Path Planning strategy based on RRT and Fuzzy control inWireless Sensor Network. Proceedings of IEEE International Conference on Robotics andBiomimetics, Guilin,2009:877-881.
[120]刘萍.基于某型导弹的航迹规划与地形匹配技术研究[硕士学位论文].南京航空航天大学，2007.
[121]李清.综合低空飞行与突防系统研究[博士学位论文].南京航空航天大学，1998.
[122] Coss S, Aron S, Deneubourg J L,et al. Self organized shortcuts in the argentine ant.Naturwissenchaften.1989(76):579-581
[123]陈谋,肖健,姜长生.基于改进蚁群算法的无人机三维航路规划.吉林大学学报（工学版）,2008,38(4):991-995.
[124] Duan H B, Yu Y A, Zhang X Y. Three-dimension path planning for UCAV using hybridmeta-heuristic ACO-DE algorithm.Simulation Modeling Practice and Theory,2010,18(8):1104-1115.
[125]罗德林,吴顺祥.基于势场蚁群算法的机器人路径规划.系统工程与电子技术,2010,32(6):1277-1280
[126] Ying K C, Lin S W. Multi-heuristic desirability ant colony system heuristic for non-permutationflowshop scheduling problems. International Journal of Advanced Manufacturing Technology,2007,33(7-8):793-802.
[127] Duan H B, Ma G J, Liu S Q. Experimental study of the adjustable parameters in basic antcolony optimization algorithm.2007IEEE Congress on Evolutionary Computation.Singapore:IEEE,2008:149-156.
[128] Anderson E P. Real-time dynamic trajectory smoothing for unmanned air vehicles. IEEETransactions on control systems technology,2005,5(3):471-477.
[129]段海滨.蚁群算法原理及其应用.北京:科学出版社,2005.
[130] Tan G Z, He H, Sloman A. Ant colony system algorithm for real-time globally optimal pathplanning of mobile robots. Acta Automatica Sinica,2007,33(3):279-285.
[131] Tan X, Chen D F. A hybrid approach of path planning for mobile robots based on thecombination of ACO and APF algorithms.International Workshop on Intelligent Systems andApplications, Piscataway: IEEE Computer Society,2009:1-4.
[132] Wolpert, D.h. and M.acteady, W.G. No free lunch theorems for optimization. IEEE Transactionon Evolutionary Computation,1997,1(1):67-82.
[133] Solis F, Wets R. Minimization by Random Search Techniques.Mathematics of OperationsResearch,1981,6(1):19-30.
[134] Zhao Y, An X Y,Luo W X.Hybrid Particle Swarm Optimization Based on Parallel Collaboration.Proc. Int. Conf. Intell. Comput. Technol. Autom,65-70,2008.
[135] Li X, Liang X M,Yan G. Using co-line search technique to improve performance of PSO.Proceedings of the27th Chinese Control Conference,49-52,2008.
[136] Alves N A, MacHaret D G, Campos M F M. On the generation of trajectories for multipleUAVS in environments with obstacles. Journal of Intelligent and Robotic Systems: Theory andApplications,2010,57(1-4):123-141.
[137] Wang G S, Li Q, Guo L J. Multiple UAVs routes planning based on particle swarmoptimization algorithm. International Symposium on Information Engineering and ElectronicCommerce. Piscataway: IEEE,2010:150-154.
[138] Lin C L,Li Y H,Aouf N. Potential field based evolutionary route planner for the control ofmultiple unmanned aerial vehicles.Journal of Aerospace Engineering,2010,224(11):1229-1242.
[139] Alidaee B, Wang H B, Landram F. A note on integer programming formulations of the real-timeoptimal scheduling and flight path selection of UAVs. IEEE Transactions on Control SystemsTechnology,2009,17(4):839-843.
[140] Duan H B, Zhang X Y, Wu J, et al. Max-min adaptive ant colony optimization approach tomulti-UAVs coordinated trajectory replanning in dynamic and uncertain environment. Journalof Bionic Engineering,2009,6(2):161–173.
[141]唐强，王建元，朱志强.基于粒子群优化的三维突防航迹规划仿真研究.系统仿真学报，2004,16(9):2033-2035
[142] Oh S K, Kim C T, Lee J J. Balancing the Selection Pressures and Migration Schemes in ParallelGenetic Algorithms for Planning Multiple Paths. Proceeding of the2001IEEE InternationalConference on Robotics and Automation. Piscataway: IEEE2001:3314-3319.
[143] Cem H, Arthur C S. Planning Multiple Paths with Evolutionary Speciation. IEEE Transacion onEvolutionary Computation,2011,5(3):169-191.
[144]叶文,欧阳中辉,朱爱红等.求解多峰函数优化的小生境克隆选择算法.系统工程与电子技术,2010,32(5):1100-1104.
[145]于会,于忠,李伟华.基于小生境粒子群技术的多航迹规划研究.西北工业大学学报,2010,28(3):415-420.
[146]孙吉贵,刘杰,赵连宇.聚类算法研究[J].软件学报,2008,19(1):48-61.
[147] Gelbard R,Goldman O,Spiegler I. Investigating diversity of clustering methods: An empiricalcomparison. Data and Knowledge Engineering,2007,63(1):155-166.
[148]韦鹏程.混沌伪随机序列及其应用研究[博士学位论文].重庆大学，2008.
[149] Wang X Y, Luo C. Dynamic Analysis of the Coupled Logistic Map. Journal of Software,2006,17(4):729-739.
[150]李国勇.智能预测控制及其MATLAB实现.北京:电子工业出版社,2010.
[151]张雷.无人机作战任务规划策略研究[博士学位论文].南京航空航天大学,2008.
[152]张昉.无人机任务规划技术研究[硕士学位论文].南京航空航天大学，2010.
[153]高春华,王慧,李平.混合系统建模、分析与综合:研究进展与展望.系统工程理论与实践.2001(11):15-20.
[154]薛乐,魏晨,陈宗基.混合系统的建模设计与仿真综述.计算机仿真.2006,23(6):1-5.
[155] Alur T, Courcoubetis C, Henzinger T A,et al. Hybrid automata, An algorithmic approach to thespecification and verification of hybrid systems. Lecture Notes in Computer Science, Springer1993,736:209–229.
[156] Jolly K G, Kumar R S. Vijayakumar R. A Bezier curve based path planning in a multi-agentrobot soccer system without violating the acceleration limits. Robotics and AutonomousSystems,2009,57(1):23–33.
[157]朱东伟,毛晓波,陈铁军.基于改进粒子群三次Bezier曲线优化的路径规划.计算机应用研究,2012,29(5):1710-1712.
[158]刘春阳,金学奇,程文刚.基于Bezier曲线模型的移动机器人路径规划算法.华北电力大学学报,206,33(4):43-46.
[159] Yoshiaki K,Jonathan P. H. Cooperative distributed robust trajectory optimization using recedinghorizon MILP. IEEE Transactions on Control Systems Technology.2011,19(2):423-431.
[160]张胜祥.基于滚动时域MILP的小型无人机航迹规划[博士学位论文].华南理工大学,2009.
[161]张纯刚,席裕庚.动态未知环境中移动机器人的滚动路径规划.机器人,2002,24(1):71-75.
[162]张纯刚,席裕庚.动态未知环境中移动机器人的滚动路径规划及安全性分析.控制理论与应用,2003,20(1):37-44.
[163] Karaman S, Frazzoli E. Linear temporal logic vehicle routing with applications to multi-UAVmission planning.International Journal of Robust and Nonlinear Control,2011,21(12):1372-1395.
[164] Wang K J, Chen M J. Cooperative capacity planning and resource allocation by mutualoutsourcing using ant algorithm in a decentralized supply chain. Expert Systems withApplications2009,36(2):2831–2842
[165]冯琦,周德云.应用单亲遗传算法进行大规模UCAVs任务分配.火力与指挥控制.2006,31(5):18-21.
[166]杨遵,雷虎民.采用粒子群优化算法规划无人机侦察航路.电光与控制.2007,14(2):4-7,15.
[167] Wang J, Gao X G, Zhu Y W. A solving algorithm for target assignment optimization modelbased on ACO.2010Sixth International Conference on Natural Computation.
[168] Pan J J, Wang D W. An ant colony optimization algorithm for multiple travelling salesmanProblem. Proceeding of the First International Conference on Innovative Computing,Information and Control. Beijing,IEEE Press,2006,1:210-213.
[169] Liu W M, Li S J, Zhao F G. An ant colony optimization algorithm for the multiple travelingsalesman problem. The4th IEEE Conference on Industrial Electronics and Application.Xi’an:IEEE Press,2009:1533-1537.
[170] Russell R A. An effective heuristic for the m-tour traveling salesman problem with some sideconditions. Operation research,1977,25(3):517-524.
[2] Office of the Secretary of Defence. Unmanned Systems Integrated Roadmap2009-2034.Washington D C: Office of the Secretary of Defence,2009.
[3] Huang H M, Pavek K,Novak B,et al. A framework for autononmy levels for unmanned systems.Proceeding of AUVSI’s Unmanned System North America,Baltimore,2005:849-863
[4] Yang Y, Minai A A, Polycarpou M M. Decentralized cooperative search in UAV’s usingopportunistic learning. AIAA Guidance, Navigation and Control Conference, Monterey,2002:2002-4590
[5] Pachter M, Chandler P R.Challenges of autonomous control. IEEE Control Systems Magazine,1998,18(4):92-97.
[6]沈林成,徐昕.移动机器人自主控制理论与技术.北京:科学出版社,2011.
[7]朱华勇,牛轶峰,沈林成等.无人机系统自主控制技术研究现状与发展趋势.国防科技大学学报,2010,32(3):115-120.
[8]王英勋,蔡志浩.无人机的自主飞行控制.航空制造技术,2009,8(8):26-31.
[9] Young L A, Yetter J A, Guynn M D. System analysis applied autonomy: Application tohigh-altitude long endurance remotely operated aircraft. Proceedings of the AIAA InfotechAerospace Conference. Arlington2005:22-31.
[10] Naval S B. Autonomous Vehicles in Support o naval Operations. Washington D C: NationalResearch Council,2005.
[11] LTC Warren O’Donell. Future combat system review. Washington D C: Office of assistantSecretary of the navy,2003.
[12]陈宗基，魏金钟，王英勋等.无人机自主控制等级及其系统结构研究.航空学报,2011,32(6):1075-1083.
[13] Suresh M, Ghose D. Role of information and communication in redefining unmanned aerialvehicle autonomous control levels, Proceedings of the Institution of Mechanical Engineers, PartG: Journal of Aerospace Engineering,2010,224(2):171-197.
[14] Johnson T L, SutherlandH A, Bush S F, et al. The TRAC Mission Manager AutonomousControl Executive.Proceeding of IEEE Aerospace Conference. Big Sky:IEEE,2001:2639-2646.
[15] Peng K M, Cai G W, Chen B M. Design and implementation of an autonomous flight controllaw for a UAV helicopter. Automatica,2009,45(10):2333-2338.
[16] Reichard K M, Banks J, Eddie C C, et al. Intelligent Self Situational Awareness for IncreasedAutonomy, Reduced Operational Risk, and Improved Capability.1st Space ExplorationConference. Orlando,2005:946-953.
[17] Cheng Z Q. Dynamic Inversion and Model Predictive Control for Unmanned Aerial Vehicles.Doctoral dissertation, University of Ottawa,Canada,2004.
[18] Tisdale J, Kim Z W, Hedrick J K. Autonomous UAV path planning and estimation: An onlinepath planning framework for cooperative search and localization. IEEE Robotics andAutomation Magazine,2009,16(2):35-42.
[19] Sharma R K., Ghose D. Collision avoidance between UAV clusters using swarm intelligencetechniques. International Journal of Systems Science,2009,40(5):521-538.
[20] Remenyte P R, Andrews, J D,Chung P W H. An efficient phased mission reliability analysis forautonomous vehicles. Reliability Engineering and System Safety,2010,95(3):226-235.
[21] Fabiani P, Fuertes V, Piquereau A. Autonomous flight and navigation of VTOL UAVs: fromautonomy demonstrations to out-of-sight flights. Aerospace Science and Technology,2007,11(2-3):183-193.
[22]周锐,李惠峰,陈宗基.无人战术飞行器的自主控制.控制与决策,2001,16(3):344-346.
[23]池沛,陈宗基,周锐.自主飞行器扩展态势评估方法研究.系统仿真学报,2007,19(10):2200-2205.
[24]牛轶峰,沈林成,龙涛.攻击型无人飞行器自主控制技术研究综述.系统工程与电子技术，2007,29(3):391-395.
[25]谭雁英,赵荣椿,祝小平等.基于智能Agent的小型无人机自主飞行任务管理器的设计.西北工业大学学报,2006,24(6):754-758.
[26]周锐,吴雯漫,罗广文.自主多无人机的分散化协同控制.航空学报,2008,29(S):26-32.
[27]吴立珍,沈林成,牛轶峰.无人机战场环境感知与理解技术研究.系统仿真学报,2010,22(S):79-84.
[28]王宏伦,王英勋.无人机飞行控制与管理.航空学报.2008,29(S1):1-7.
[29]宗令蓓,谢凡,秦世引.基于MAS的无人机编队飞行智能优化控制.航空学报,2008,29(5):1326-1332.
[30]段海滨,刘森琪.空中/地面机器人异构协同技术研究现状和展望.中国科学:技术科学,2010,40(9):1029-1036.
[31]叶文.无人飞行器任务规划.北京:国防工业出版社,2011.
[32] Vachtsevanos G, Tang L, Drozeski G. From mission planning to flight control of unmannedaerial vehicles: Strategies and implementation tools. Annual Reviews in Control,2009,29(1):101-115
[33]郑昌文,严平,丁明跃.飞行器航迹规划研究现状与趋势.宇航学报,2007,28(6):1441-1446.
[34]杜萍,杨春.飞行器航迹规划算法综述.飞行力学,2005,23(2):10-14.
[35]郑昌文,严平,丁明跃等.飞行器航迹规划.北京:国防工业出版社,2008
[36] Chaudhry A, Misovec K, D’Andrea R. Low overvability path planning for an unmanned airvehicle using mixed integer linear programming.43th IEEE Conference on Decision andControl, Bahamas:IEEE,2004:3823-3829.
[37] Karaman S,Frazzoli E.Linear temporal logic vehicle routing with applications to multi-UAVmission planning. International Journal of Robust and Nonlinear Control.2011,21(12):1372-1395.
[38] Khatib O. Real time obstacle avoidance for manipulators and mobiles robots. InternationalJournal of Robotics Research.1986,5(1):90-99.
[39]张建英,刘暾.基于人工势场法的移动机器人最优路径规划.航空学报,2007,28(S):183-188.
[40] Sven K, Maxim L. Real time adaptive A*. Proceeding of the International Conference onAutonomous Agents. New York: ACM,2006:281-288.
[41] Yang X, Ding M Y, Zhou C P. Fast Marine Route Planning for UAV Using Improved Sparse A*Algorithm. International Conference on Genetic and Evolutionary Computing. Piscataway:IEEE,2010:190-193.
[42] Szczerba R J. Threat Netting for real time,intelligent route planning. The Proceedings of theInformation, Decision and Control Symposium. Adelaide, Australia,1999:377-382.
[43] Szczerba R J, Galkowski P, Glickstein I S, et al. Robust algorithm for real time route planning.IEEE Transactions on Aerospce and Electronic System,2000,36(3):869-878.
[44]李春华,郑昌文,周成平.一种三维航迹快速索算法.宇航学报,2002,23(3):13-17.
[45] Stentz A.Optimal and efficient route planning for partially-know environments.The Proceedingsof IEEE Conference on Robotics and Automation,1994,3310-3317.
[46] Besada P E, Luis D L T, Cruz D L,et al.Evolutionary trajectory planner for multiple UAVs inrealistic scenarios. IEEE Transactions on Robotics,2010,26(4):619-634.
[47]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].国防科技大学,2006.
[48] Pzchter M, Chandler P R. Challenges of autonomous control. IEEE Control SystemsMagazine,1998,18(4):91-97.
[49] Rabbaty C A, Gagnon E, Lauzon M. On the cooperative control of multiple unmanned aerialvehicles, IEEE Canadian Review, Spring,2004:15-18.
[50] Chandler P, Rasumussen S, Patchter M. UAV cooperative path planning. Prcceedings of AIAAGuidance,Navigation, and Control Conference, Denver:AIAA,2000:2000-4370.
[51]倪谣,周德云,马云红等.基于MILP模型的多无人机对地攻击任务分配.火力指挥与控制,2008,33(11):62-65.
[52] Schumacher C, Chandler P R, Rasmussen S R. Task allocation for wide area search munitions.Proceedings of American Control Conference, Anchorage,2002:1917-1922.
[53] Nygard K E, Chandler P R, Pachter M. Dynamic network flow optimization models for airvehicle resource allocation. Proceedings of American Control Conference, Arlinton,2001:1853-1858.
[54] Dias M B, Stentz A. Opportunistic optimization for market-based multirobot control.Proceedings of IEEE International Conference on Intelligent Robots and Systems, Switzerland,2002:2714-2720.
[55] Elad K, Kelly C, Manish K. Market-Based Solution to the Allocation of Tasks to Agents.Procedia Computer Science,2011,1(6):28–33.
[56] Chen Y L, Brian G, Matt E. Market-Based Collaborations for Autonomous Operations ofUnmanned Air Vehicle. IEEE Workshop on Distributed Intelligent Systems. Prague,2006:273-278.
[57]龙涛,朱华勇,沈林成.多UCAV协同中基于协商的分布式任务分配研究.宇航学报.2006,27(3):457-462.
[58]龙涛,沈林成,朱华勇等.面向协同任务的多UCAV分布式任务分配与协调技术.自动化学报,2007,33(7):731-737.
[59]廖沫,陈宗基.基于多Agent分布协同拍卖的动态目标分配算法.北京航空航天大学,2007,33(2):180-183.
[60] Shima T, Steven J. R, Andrew G. S.Multiple task assignments for cooperating uninhabited aerialvehicles using genetic algorithms. Computers&Operations Research.2006,33(11):3252-3269.
[61] Edison E, Shima T. Integrated task assignment and path optimization for cooperatinguninhabited aerial vehicles using genetic algorithms. Computers&Operations Research.2011,38(1):340-356.
[62] Gao N, Qu Z H, Li X J. On Modeling and Discrete Particle Swarm Optimization for TaskAssignment of Cooperating UAVs. Chinese Control and Decision Conference. Mianyang,2011:1044-1049.
[63]叶文,朱爱红,欧阳中辉.基于混合离散粒子群算法的多无人作战飞机协同目标分配.兵工学报.2010,31(3):331-336.
[64]苏菲,陈岩,沈林成.基于蚁群算法的无人机协同多任务分配.航空学报,2008,29(S):184-191.
[65]段海滨,丁全心,常俊杰.基于并行蚁群优化的多UCAV任务分配仿真平台.航空学报,2008,29(S):192-197.
[66]邢文训,谢金星.现代优化计算方法.北京:清华大学出版社,2005.
[67]汪定伟,王俊伟.智能优化算法.北京:高等教育出版社,2007.
[68] Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents.IEEE Transaction on Systems, Man and Cybernetics-Part B,1996,26(1):29-41.
[69] Kennedy J, Eberhart R. Particle swarm optimization.Proc of the IEEE Internationa1Conferenceon Neura1Networks. Piscataway: IEEE,1995:1942-1948.
[70] Eberhart R C,Kennedy J. A new optimizer using particle swarm theory. Proceedings of theSixth International Symposium on Micro Machine and Human Scienc. Nagoya Japan1995:39-43.
[71] Sabat S L,Udgata S K,Abraham A. Artificial bee colony algorithm for small signal modelparameter extraction of MESFET.Engineering Applications of Artificial Intelligence,2010,23(5):689-694．
[72] Kunz H, Hemelrijk C K. Artificial fish schools: collective effects of school size, body size, andbody form. Artificial Life,2003,9(3):237-253.
[73] Bonabeau E, Dorigo M, Theraulaz G. Inspiration for optimization from social insect behavior.Nature,2000,406(6):39-42.
[74] Michael J B K, Jean B B, Laurent K. Ant-like task recruitment in cooperative robots.Nature,2000,406(31):992-995
[75] Jackson D E, Holcombe M, Ratnieks F L W. Trail geometry gives polarity to ant foragingnetworks. Nature,2004,432(7019):907-909.
[76] DorigoM, Gambardella L M. Ant colony system: a cooperative learning approach to thetraveling salesman problem. IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[77] Stutzle T, Hoos H H. Max-min ant system.Future Generation Computer Systems,2000,16(8):889-914.
[78] Dorigo M, Maniezzo V, Colomi A. The ant system: Optimization by a colony of cooperatingagents.IEEE Transactions on Systems, Man, and Cybemetics,1996,26(l):29-41.
[79] Dorigo M, Gambardella L M. A study of some properties of ant-Q. Proceedings of the4thInternational Conference on Parallel Problem Solving from Nature, Berlin:Springer-Verlag,1996:656-665.
[80] Blum C, Dorigo M. The hyper-cube framework for ant colony optimization. IEEE Transactionson Systems, Man and Cybernetics, Part B,2004,34(2):1161-1172.
[81]丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法的融合.计算机研究与发展,2003,40(9):1351-1356.
[82] Blum C, Socha K. Training feed-forward neural networks with ant colony optimization: Anapplication to pattern classification. The Fifth International Conference on Hybrid IntelligentSystems, Rio de Janeiro:IEEE,2005:233-238.
[83]刘超华,张英杰,张兢.蚁群算法与免疫算法的融合及其在TSP中的应用.控制与决策,2010,25(5):695-700.
[84]詹士昌,徐婕,吴俊.蚁群算法中有关算法参数的最优选择.科技通报,2003,19(5):381-386.
[85] Gaertner D, Clark K. On Optimal Parameters for Ant Colony Optimization Algorithms. TheInternational Conference on Artificial Intelligence,LasVegas: CSREA Press,2005:83-89.
[86] Duan H B, Ma G J, Liu S Q. Experimental study of the adjustable parameters in basic antcolony optimization algorithm.IEEE Congress on Evolutionary Computation. Piscataway:IEEE,2008:149-156.
[87] GutjahrW J. A graph-based ant system and its convergence. Future Generation ComputerSystems,2000,16(8):873-888.
[88] Stutzle T,DorigoM. A short convergence proof for a class of ant colony optimization algorithms.IEEE Transactions on Evolutionary Computation,2002,6(4):358-365.
[89] GutjahrW J. ACO algorithms with guaranteed convergence to the optimal solution. InformationProcessing Letters,2002,82(3):145-153.
[90]段海滨,王道波.蚁群算法的全局收敛性研究及改进.系统工程与电子技术,2004,26(10):1506-1509.
[91] Huang R H, Yang C L. Ant colony system for job shop scheduling with time windows.International Journal of Advanced Manufacturing Technology,2008,39(1-2):151-157.
[92] Wang K J, Chen M J. Cooperative capacity planning and resource allocation by mutualoutsourcing using ant algorithm in a decentralized supply chain. Expert Systems withApplications,2009,36(2):2831-2842.
[93] See P C, Wong K Y. Application of ant colony optimisation algorithms in solving facility layoutproblems formulated as quadratic assignment problems: A review. International Journal ofIndustrial and Systems Engineering,2008,3(6):644-672
[94] Zhao D M, Luo L, Zhang K. An improved ant colony optimization for the communicationnetwork routing problem. Mathematical and Computer Modelling,2010,52(11-12):1976-1981.
[95]马卫,朱庆宝.求解函数优化问题的快速连续蚁群算法.电子学报,2008,38(11):2120-2124.
[96] Boubertakh H, Tadjine M, Glorennec P Y, Labiod S. Tuning fuzzy PID controllers using antcolony optimization.17th Mediterranean Conference on Control and Automation, Piscataway:IEEE,2009:13-18.
[97] Juang C F, Lu C M. Ant colony optimization incorporated with fuzzy Q-learning forreinforcement fuzzy control. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,2009,39(3):597-608.
[98] Reynolds C W. Flocks, Herds and Schools：A distributed behavioral mode1. ComputerGraphics,1987,21(4):25-43.
[99] Gallad A,Hawary M,Sallam A. Enhancing the particle swarm optimizer via proper parametersselection. Canadian Conference on Electrical and Computer Engineering. Piscataway: IEEE,2002,2:792-797.
[100] Shi Y, Eberhart R. A. modified particle swarm optimizer. IEEE International Conference ofEvolutionary Computation, Piscataway: IEEE,1998:69-73.
[101] Ratnaweera A, Halgamuge S K, Watson H C. Self-organizing hierarchical particle swarmoptimizer with time-varying acceleration coefficients. IEEE Transactions on EvolutionaryComputation,8(3),240–255,2004.
[102] Van den Bergh F, Engelbrecht A P. A study of particle swarm optimization particle trajectories.Information Sciences,2006,176(8):937-971.
[103] Clerc M, Kennedy J. The particle swarm:explosion, stability and convergence in a multi-dimensional complex space. Transactions on Evolutionary Computation,2002,6(1):58-73.
[104] Trelea I C. The particle swarm optimization algorithm: convergence analysis and parameterselection. Information Processing Letters,2003,85(6):317-325.
[105] Kadirkamanathan V, Selvarajah K, Fleming P J. Stability analysis of the particle dynamics inparticle swarm optimizer. Transactions on Evolutionary Computation,2006,10(3):245-255.
[106] Jiang M, Luo Y P, Yang S Y. Stochastic convergence analysis and parameter selection of thestandard particle swarm optimization algorithm. Information Processing Letters,2007,102(1):8-16.
[107] Jin X L, Ma L H, Wu T J, Qian J X. Convergence analysis of the particle swarm optimizationbased on stochastic processes. Acta Automatica Sinica,2007,33(12):1263-1268
[108]潘峰,陈杰,辛斌等.粒子群优化方法若干特性分析.自动化学报,2009,35(7):1010-1015
[109] LaValle S M. Rapidly-exploring random trees:A new tool for path planning. Computer ScienceDept, Iowa State University, October1998.
[110] LaValle S M, Kuffner J J. Donald B R, et al. Rapidly-exploring random trees: Progress andprospects. Algorithmic and Computational Robotics: New Directions, Wellesley,2001:293-308.
[111] Joshua R, Jayesh N A, Jovan D B. Real-time Obstacle Detection and Reactive Path PlanningSystem for Autonomous Small-Scale Helicopters，AIAA Guidance, Navigation and ControlConference and Exhibit，Hilton Head,2007:1-22.
[112] Martin S R, Wright S E, Sheppard J W. Offline and Online Evolutionary Bi-Directional RRTAlgorithms for Efficient Re-Planning in Dynamic Environments. Proceedings of the3rd AnnualIEEE Conference on Automation Science and Engineering,Scottsdale,2007:1131-1136.
[113] Ian G and Jonathan P.H. Improving the Efficiency of Rapidly-exploring Random Trees Using aPotential Function Planner. Proceedings of the44th IEEE Conference on Decision andControl,Seville,2005:7965-7970.
[114] Armando A N, Douglas G. M, Mario F M C. On the Generation of Trajectories for MultipleUAVs in Environments with Obstacles. Journal of Intelligent and Robotic Systems: Theory andApplications,2010,57(1-4):123-141.
[115] David B. Comparison of A*and RRT-connect motion planning techniques for self-reconfiguration planning. International Conference on Intelligent Robots and Systems, Beijing,2006:892-897.
[116]彭辉,王林,沈林成.区域目标搜索中基于改进RRT的UAV实时航迹规划.国防科技大学学报,2009,31(5):86-91.
[117] Rodríguez S, Tang X Y, Lien J M. An Obstacle-Based Rapidly-Exploring Random Tree. IEEEInternational Conference on Robotics and Automation, Orlando,2006:895-900.
[118] Peng H, Su F, Bu Y L,et al. Cooperative area search for multiple UAVs based RRT anddecentralized receding horizon optimization.Proceedings of the7th Asian Control Conference,Hong Kong,2009:298-303.
[119] Song P, Li J F. Hybrid Multi-robot Path Planning strategy based on RRT and Fuzzy control inWireless Sensor Network. Proceedings of IEEE International Conference on Robotics andBiomimetics, Guilin,2009:877-881.
[120]刘萍.基于某型导弹的航迹规划与地形匹配技术研究[硕士学位论文].南京航空航天大学，2007.
[121]李清.综合低空飞行与突防系统研究[博士学位论文].南京航空航天大学，1998.
[122] Coss S, Aron S, Deneubourg J L,et al. Self organized shortcuts in the argentine ant.Naturwissenchaften.1989(76):579-581
[123]陈谋,肖健,姜长生.基于改进蚁群算法的无人机三维航路规划.吉林大学学报（工学版）,2008,38(4):991-995.
[124] Duan H B, Yu Y A, Zhang X Y. Three-dimension path planning for UCAV using hybridmeta-heuristic ACO-DE algorithm.Simulation Modeling Practice and Theory,2010,18(8):1104-1115.
[125]罗德林,吴顺祥.基于势场蚁群算法的机器人路径规划.系统工程与电子技术,2010,32(6):1277-1280
[126] Ying K C, Lin S W. Multi-heuristic desirability ant colony system heuristic for non-permutationflowshop scheduling problems. International Journal of Advanced Manufacturing Technology,2007,33(7-8):793-802.
[127] Duan H B, Ma G J, Liu S Q. Experimental study of the adjustable parameters in basic antcolony optimization algorithm.2007IEEE Congress on Evolutionary Computation.Singapore:IEEE,2008:149-156.
[128] Anderson E P. Real-time dynamic trajectory smoothing for unmanned air vehicles. IEEETransactions on control systems technology,2005,5(3):471-477.
[129]段海滨.蚁群算法原理及其应用.北京:科学出版社,2005.
[130] Tan G Z, He H, Sloman A. Ant colony system algorithm for real-time globally optimal pathplanning of mobile robots. Acta Automatica Sinica,2007,33(3):279-285.
[131] Tan X, Chen D F. A hybrid approach of path planning for mobile robots based on thecombination of ACO and APF algorithms.International Workshop on Intelligent Systems andApplications, Piscataway: IEEE Computer Society,2009:1-4.
[132] Wolpert, D.h. and M.acteady, W.G. No free lunch theorems for optimization. IEEE Transactionon Evolutionary Computation,1997,1(1):67-82.
[133] Solis F, Wets R. Minimization by Random Search Techniques.Mathematics of OperationsResearch,1981,6(1):19-30.
[134] Zhao Y, An X Y,Luo W X.Hybrid Particle Swarm Optimization Based on Parallel Collaboration.Proc. Int. Conf. Intell. Comput. Technol. Autom,65-70,2008.
[135] Li X, Liang X M,Yan G. Using co-line search technique to improve performance of PSO.Proceedings of the27th Chinese Control Conference,49-52,2008.
[136] Alves N A, MacHaret D G, Campos M F M. On the generation of trajectories for multipleUAVS in environments with obstacles. Journal of Intelligent and Robotic Systems: Theory andApplications,2010,57(1-4):123-141.
[137] Wang G S, Li Q, Guo L J. Multiple UAVs routes planning based on particle swarmoptimization algorithm. International Symposium on Information Engineering and ElectronicCommerce. Piscataway: IEEE,2010:150-154.
[138] Lin C L,Li Y H,Aouf N. Potential field based evolutionary route planner for the control ofmultiple unmanned aerial vehicles.Journal of Aerospace Engineering,2010,224(11):1229-1242.
[139] Alidaee B, Wang H B, Landram F. A note on integer programming formulations of the real-timeoptimal scheduling and flight path selection of UAVs. IEEE Transactions on Control SystemsTechnology,2009,17(4):839-843.
[140] Duan H B, Zhang X Y, Wu J, et al. Max-min adaptive ant colony optimization approach tomulti-UAVs coordinated trajectory replanning in dynamic and uncertain environment. Journalof Bionic Engineering,2009,6(2):161–173.
[141]唐强，王建元，朱志强.基于粒子群优化的三维突防航迹规划仿真研究.系统仿真学报，2004,16(9):2033-2035
[142] Oh S K, Kim C T, Lee J J. Balancing the Selection Pressures and Migration Schemes in ParallelGenetic Algorithms for Planning Multiple Paths. Proceeding of the2001IEEE InternationalConference on Robotics and Automation. Piscataway: IEEE2001:3314-3319.
[143] Cem H, Arthur C S. Planning Multiple Paths with Evolutionary Speciation. IEEE Transacion onEvolutionary Computation,2011,5(3):169-191.
[144]叶文,欧阳中辉,朱爱红等.求解多峰函数优化的小生境克隆选择算法.系统工程与电子技术,2010,32(5):1100-1104.
[145]于会,于忠,李伟华.基于小生境粒子群技术的多航迹规划研究.西北工业大学学报,2010,28(3):415-420.
[146]孙吉贵,刘杰,赵连宇.聚类算法研究[J].软件学报,2008,19(1):48-61.
[147] Gelbard R,Goldman O,Spiegler I. Investigating diversity of clustering methods: An empiricalcomparison. Data and Knowledge Engineering,2007,63(1):155-166.
[148]韦鹏程.混沌伪随机序列及其应用研究[博士学位论文].重庆大学，2008.
[149] Wang X Y, Luo C. Dynamic Analysis of the Coupled Logistic Map. Journal of Software,2006,17(4):729-739.
[150]李国勇.智能预测控制及其MATLAB实现.北京:电子工业出版社,2010.
[151]张雷.无人机作战任务规划策略研究[博士学位论文].南京航空航天大学,2008.
[152]张昉.无人机任务规划技术研究[硕士学位论文].南京航空航天大学，2010.
[153]高春华,王慧,李平.混合系统建模、分析与综合:研究进展与展望.系统工程理论与实践.2001(11):15-20.
[154]薛乐,魏晨,陈宗基.混合系统的建模设计与仿真综述.计算机仿真.2006,23(6):1-5.
[155] Alur T, Courcoubetis C, Henzinger T A,et al. Hybrid automata, An algorithmic approach to thespecification and verification of hybrid systems. Lecture Notes in Computer Science, Springer1993,736:209–229.
[156] Jolly K G, Kumar R S. Vijayakumar R. A Bezier curve based path planning in a multi-agentrobot soccer system without violating the acceleration limits. Robotics and AutonomousSystems,2009,57(1):23–33.
[157]朱东伟,毛晓波,陈铁军.基于改进粒子群三次Bezier曲线优化的路径规划.计算机应用研究,2012,29(5):1710-1712.
[158]刘春阳,金学奇,程文刚.基于Bezier曲线模型的移动机器人路径规划算法.华北电力大学学报,206,33(4):43-46.
[159] Yoshiaki K,Jonathan P. H. Cooperative distributed robust trajectory optimization using recedinghorizon MILP. IEEE Transactions on Control Systems Technology.2011,19(2):423-431.
[160]张胜祥.基于滚动时域MILP的小型无人机航迹规划[博士学位论文].华南理工大学,2009.
[161]张纯刚,席裕庚.动态未知环境中移动机器人的滚动路径规划.机器人,2002,24(1):71-75.
[162]张纯刚,席裕庚.动态未知环境中移动机器人的滚动路径规划及安全性分析.控制理论与应用,2003,20(1):37-44.
[163] Karaman S, Frazzoli E. Linear temporal logic vehicle routing with applications to multi-UAVmission planning.International Journal of Robust and Nonlinear Control,2011,21(12):1372-1395.
[164] Wang K J, Chen M J. Cooperative capacity planning and resource allocation by mutualoutsourcing using ant algorithm in a decentralized supply chain. Expert Systems withApplications2009,36(2):2831–2842
[165]冯琦,周德云.应用单亲遗传算法进行大规模UCAVs任务分配.火力与指挥控制.2006,31(5):18-21.
[166]杨遵,雷虎民.采用粒子群优化算法规划无人机侦察航路.电光与控制.2007,14(2):4-7,15.
[167] Wang J, Gao X G, Zhu Y W. A solving algorithm for target assignment optimization modelbased on ACO.2010Sixth International Conference on Natural Computation.
[168] Pan J J, Wang D W. An ant colony optimization algorithm for multiple travelling salesmanProblem. Proceeding of the First International Conference on Innovative Computing,Information and Control. Beijing,IEEE Press,2006,1:210-213.
[169] Liu W M, Li S J, Zhao F G. An ant colony optimization algorithm for the multiple travelingsalesman problem. The4th IEEE Conference on Industrial Electronics and Application.Xi’an:IEEE Press,2009:1533-1537.
[170] Russell R A. An effective heuristic for the m-tour traveling salesman problem with some sideconditions. Operation research,1977,25(3):517-524.
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