不确定环境下多UCAV实时任务规划方法研究
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摘要
由于无人作战飞机(Unmanned Combat Aerial Vehicle, UCAV)能够完成各种复杂的作战任务,在现代战争中发挥的作用越来越显著,因而受到各国军事高层的关注。在实际的战场环境中,存在着战场环境的多变性、情报获取的不确定性和武器作战效能的随机性等不确定因素,因此研究不确定环境下多UCAV任务规划方法问题已成为一个新的重要研究课题,而任务分配又是多UCAV任务规划系统的基础和保障,对于提高自身的生存能力、突防能力、作战效能具有重要的理论价值和现实意义。围绕以上问题,本文在不确定环境下多UCAV实时任务规划问题方面进行了相关的理论研究,主要内容概括如下:
首先,根据不确定环境下作战双方的态势,分析了决定任务分配结果的各项关键指标,如价值收益、毁伤代价、武器弹药代价、航程和燃油代价等,并充分考虑了这些指标中的不确定因素和不确定信息。其次,根据以上所分析的任务分配中的关键指标和指标中的不确定信息,考虑目标的动态特性,分别建立了随时间变化的系统数学优化模型和基于不确定信息的数学优化模型。最后,利用遗传算法、SMAA(Stochastic Multi-criteria Acceptability Analysis)方法及理想点法对数学优化模型进行动态求解,得到最优的任务分配结果。仿真结果验证了该模型和方法的有效性和适用性。
在论文的最后,总结了本论文的研究成果,并指出了本论文研究的局限和对后续工作研究的建议。
UCAV(Unmanned Combat Aerial Vehicle) can complete a variety of the complex combat tasks, and is playing more and more important role in morden war, so it attracts the attention of the national military. In the real battlefield environments, there are many uncertain factors such as the variability of the battlefield environment、the uncertainty of the acquired information、the randomness of the weapon’s damage effectiveness etc.Therefore, the problem of studying on the real-time task planning of multi-UCAV in uncertain environment is a new important topic. Moreover, task assignment is the guarantee and foundation of task planning system. Meanwhile, it has important theoretical value and realistic significance in improving the viability、penetration ability and combat effectiveness of UCAV. Around the above problems, the related theories about the problem of real-time task planning in uncertain environments are carried out in this paper, the main content can be summarized as follows:
First, the key indexes which dccide the task assignment result are analyzed according to the combat situation in uncertain environment. For example, the target rewad, the damage cost, weapon and ammunition cost, the distance and fuel oil cost and so on. The uncertain facors and uncertain information in these indicators are fully considered. Secondly, according to the key indicators in task assignment and the uncertain information analyzed above, the mathematical optimization model with time-varying and the mathematical optimization model based on uncertain information are established which take the dynamia characteristics of the target into account. Lastly, the mathematical optimization models are solved using genetic algorithm, SMAA and ideal point method, the optimal task assignment result is obtained. The simulation results validate the applicability and effectiveness of the model and method.
At last, the research fruits are summarized. Following that, the limitation of the research content and future research emphasis are pointed out.
首先,根据不确定环境下作战双方的态势,分析了决定任务分配结果的各项关键指标,如价值收益、毁伤代价、武器弹药代价、航程和燃油代价等,并充分考虑了这些指标中的不确定因素和不确定信息。其次,根据以上所分析的任务分配中的关键指标和指标中的不确定信息,考虑目标的动态特性,分别建立了随时间变化的系统数学优化模型和基于不确定信息的数学优化模型。最后,利用遗传算法、SMAA(Stochastic Multi-criteria Acceptability Analysis)方法及理想点法对数学优化模型进行动态求解,得到最优的任务分配结果。仿真结果验证了该模型和方法的有效性和适用性。
在论文的最后,总结了本论文的研究成果,并指出了本论文研究的局限和对后续工作研究的建议。
UCAV(Unmanned Combat Aerial Vehicle) can complete a variety of the complex combat tasks, and is playing more and more important role in morden war, so it attracts the attention of the national military. In the real battlefield environments, there are many uncertain factors such as the variability of the battlefield environment、the uncertainty of the acquired information、the randomness of the weapon’s damage effectiveness etc.Therefore, the problem of studying on the real-time task planning of multi-UCAV in uncertain environment is a new important topic. Moreover, task assignment is the guarantee and foundation of task planning system. Meanwhile, it has important theoretical value and realistic significance in improving the viability、penetration ability and combat effectiveness of UCAV. Around the above problems, the related theories about the problem of real-time task planning in uncertain environments are carried out in this paper, the main content can be summarized as follows:
First, the key indexes which dccide the task assignment result are analyzed according to the combat situation in uncertain environment. For example, the target rewad, the damage cost, weapon and ammunition cost, the distance and fuel oil cost and so on. The uncertain facors and uncertain information in these indicators are fully considered. Secondly, according to the key indicators in task assignment and the uncertain information analyzed above, the mathematical optimization model with time-varying and the mathematical optimization model based on uncertain information are established which take the dynamia characteristics of the target into account. Lastly, the mathematical optimization models are solved using genetic algorithm, SMAA and ideal point method, the optimal task assignment result is obtained. The simulation results validate the applicability and effectiveness of the model and method.
At last, the research fruits are summarized. Following that, the limitation of the research content and future research emphasis are pointed out.
引文
[1]丁鹏辉,陈开生.无人作战飞机发展的技术问题探讨[A].尖兵之翼—2006中国无人机大会论文集[C].2006:108-111
[2]田菁.多无人机协同侦察任务规划问题建模与优化技术研究[D].国防科学技术大学博士学位论文,2007
[3]丁琳.多无人作战飞机协同攻击多目标的研究与应用[D].西北工业大学硕士学位论文,2005
[4]刘新艳,黄显林,吴强.对地攻击任务规划系统的功能体系框架设计[J].哈尔滨工业大学学报,2007,39 (7):1013-1016
[5]冯琦,周德云. UCAV任务规划系统的研究进展及发展趋势[J].飞行力学,2003,21(2):1-4
[6]张煜.战术飞机飞行任务规划中不确定性因素处理方法研究[D].国防科学技术大学硕士学位论文,2006
[7]刘新艳,黄显林,吴强.国外任务规划系统的发展.火力与指挥控制,2007,32 (6):5-9
[8] A Richards, J P How. Aircraft trajectory planning with collision avoidance using mixed integer linear programming[C]. Proceeding of American Control Conference, 2002
[9]金庭,陈璟.协同空对地攻击中的目标分配方法[J].计算机仿真,2008,25(11):40-43
[10] Genshe Chen. Genetic Algorithm for Task Allocation in UAV Cooperative Control[C]. AIAA Guidance, Navigation, and Control Conference and Exhibit,2003.
[11] Jun Tao and Yantao Tian.Cooperative Task Allocation for Unmanned Combat Aerial Vehicles Using Improved Ant Colony Algorithm[C] . IEEE conference on CIS,2008,1220-1225.
[12]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 (增刊):s184-s191
[13] Jose B, Cruz, Jr. Particle Swarm Optimization for Resource Allocation in UAV Cooperative Control[C]. AIAA Guidance, Navigation, and Control Conference and Exhibit,2004.
[14]李炜,张伟.基于粒子群算法的多无人机任务分配方法[J].控制与决策,2010,25 (9):1359-1363
[15]国博,王社伟,陶军.基于改进粒子群算法的多无人机任务分配研究[J].计算机仿真,2009, 26(7):62-64
[16]叶媛媛,闵春平,沈林成.基于满意决策的多UAV协同目标分配方法[J].国防科技大学学报,2005,27 (4):116-120
[17]刘波,覃征,邵利平.基于群集智能的协同多目标攻击空战决策[J].航空学报,2009,30 (9): 1727-1738
[18]叶媛媛,闵春平,沈林成.多UCAV任务分配的混合遗传算法与约束处理[J].控制与决策. 2006,21(7):781-786
[19]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[D] .长沙:国防科学技术大学博士学位论文, 2006.
[20] Aknine S, Pinson S, Shakun M F. An extended multi-agent negotiation protocol [J]. Autonomous Agents and Multi-Agent System, 2004, 8 (1):5-45.
[21] Bogdanowicz Z R, Coleman N P. Sensor-target and weapon-target pairings based on auction algorithm [C]. Proc. of the 11th WS EA S I nternational Conference on Applied Mathematics,2007:92-96.
[22] Steven J. Rasmussen and Tal Shima. Tree search algorithm for assigning cooperating UAVs to multiple tasks [J]. Robust Nonlinear Control , 2008, 18:135–153
[23] Joel G. Manathara·P. B. Sujit·Randal W. Beard. Multiple UAV Coalitions for a Search and Prosecute Mission [J]. J Intell Robot Syst,2010
[24] Anawat P, Rolf R. Real-Time Planning for Teams of Autonomous Vehicles in Dynamic Uncertain Environments [J] .University of Washington, 2004.
[25] Mehdi Alighanbari, Jonathan How. A robust approach to the UAV task assignment problem [J]. International Journal of Robust and Nonlinear Control. 2008, 18: 118-134.
[26] Mehdi Alighanbari, Jonathan How. Robust Decentralized Task Assignment for Cooperative UAVs[C]. AIAA Guidance,Navigation and Control Conference and Exhibit, Colorado, August 2006, AIAA2006-6454
[27] Luc Brunet, Han-Lim Choi, Jonathan P. How. Consensus-Based Auction Approaches for Decentralized Task Assignment[C]. AIAA Guidance, Navigation and Control Conference and Exhibit, Hawaii, August, 2008, AIAA-2008-6839.
[28]张莉,张安.不确定环境下编队对地攻防对抗决策方法研究[J].系统工程与电子技术,2009, 31(2):411-415
[29]张煜.战术飞机飞行任务规划中不确定性因素处理方法研究[D].国防科技大学硕士论文,2006
[30]张永富.基于遗传算法的网络可靠性优化设计[D].南京邮电大学硕士论文,2008
[31]史志富,刘海燕等.基于影响图的UCAV编队对地攻击战术决策研究[J].系统工程与电子技术,2009,31(1):130-133
[32]唐苏妍,朱一凡等.多Agent系统任务分配方法综述[J].系统工程与电子技术,2010,32(10):2155-2161.
[33]李玉吉,卢才武,刘冠.蚁群遗传算法在高校智能排课系统中的应用[J].现代电子技术,2010,14:121-123
[34] Rathbun R., Capozzi B. Evolutionary Approaches to Path Planning Through Uncertain Environments [C]. Proceedings of the AIAA Unmanned Unlimited Conference, Portsmouth, VA., May 2002
[35] Risto Lahdelma, Joonas Hokkanen, Pekka Salminen. SMAA-Stochastic multi-objective acceptability analysis. European Journal of Operational Research [J]. 1998, 106(1): 137-143
[36] Risto Lahdelma, Pekka Salminen. SMAA-2: Stochastic multi-criteria acceptability analysis for group decision making. European Journal of Operational Research [J]. 2001, 49(3): 444-454
[37] Risto Lahdelma, Kaisa Miettinen, Pekka Salminen. Ordinal criteria in stochastic multi-criteria acceptability analysis (SMAA). European Journal of Operational Research [J]. 2003, 47(1): 117-127
[38]陈凤,先晓兵.基于DFS的多Agent动态任务分配算法[J].计算机工程,2009,35 (14):230-233
[39]王迎超,尚岳全等.基于熵权-理想点法的岩爆烈度预测模型及其应用[J].煤炭学报,2010,35 (2):218-221
[40]兰蓉.一种基于区间数距离的理想点多属性决策方法[J].西安邮电学院学报,2009,14 (1):153-156
[41]管怀建,唐亮,邓志江.基于正负理想点法的火炮武器系统作战效能评估[J].兵工自动化,2010,29(3):44-46
[2]田菁.多无人机协同侦察任务规划问题建模与优化技术研究[D].国防科学技术大学博士学位论文,2007
[3]丁琳.多无人作战飞机协同攻击多目标的研究与应用[D].西北工业大学硕士学位论文,2005
[4]刘新艳,黄显林,吴强.对地攻击任务规划系统的功能体系框架设计[J].哈尔滨工业大学学报,2007,39 (7):1013-1016
[5]冯琦,周德云. UCAV任务规划系统的研究进展及发展趋势[J].飞行力学,2003,21(2):1-4
[6]张煜.战术飞机飞行任务规划中不确定性因素处理方法研究[D].国防科学技术大学硕士学位论文,2006
[7]刘新艳,黄显林,吴强.国外任务规划系统的发展.火力与指挥控制,2007,32 (6):5-9
[8] A Richards, J P How. Aircraft trajectory planning with collision avoidance using mixed integer linear programming[C]. Proceeding of American Control Conference, 2002
[9]金庭,陈璟.协同空对地攻击中的目标分配方法[J].计算机仿真,2008,25(11):40-43
[10] Genshe Chen. Genetic Algorithm for Task Allocation in UAV Cooperative Control[C]. AIAA Guidance, Navigation, and Control Conference and Exhibit,2003.
[11] Jun Tao and Yantao Tian.Cooperative Task Allocation for Unmanned Combat Aerial Vehicles Using Improved Ant Colony Algorithm[C] . IEEE conference on CIS,2008,1220-1225.
[12]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 (增刊):s184-s191
[13] Jose B, Cruz, Jr. Particle Swarm Optimization for Resource Allocation in UAV Cooperative Control[C]. AIAA Guidance, Navigation, and Control Conference and Exhibit,2004.
[14]李炜,张伟.基于粒子群算法的多无人机任务分配方法[J].控制与决策,2010,25 (9):1359-1363
[15]国博,王社伟,陶军.基于改进粒子群算法的多无人机任务分配研究[J].计算机仿真,2009, 26(7):62-64
[16]叶媛媛,闵春平,沈林成.基于满意决策的多UAV协同目标分配方法[J].国防科技大学学报,2005,27 (4):116-120
[17]刘波,覃征,邵利平.基于群集智能的协同多目标攻击空战决策[J].航空学报,2009,30 (9): 1727-1738
[18]叶媛媛,闵春平,沈林成.多UCAV任务分配的混合遗传算法与约束处理[J].控制与决策. 2006,21(7):781-786
[19]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[D] .长沙:国防科学技术大学博士学位论文, 2006.
[20] Aknine S, Pinson S, Shakun M F. An extended multi-agent negotiation protocol [J]. Autonomous Agents and Multi-Agent System, 2004, 8 (1):5-45.
[21] Bogdanowicz Z R, Coleman N P. Sensor-target and weapon-target pairings based on auction algorithm [C]. Proc. of the 11th WS EA S I nternational Conference on Applied Mathematics,2007:92-96.
[22] Steven J. Rasmussen and Tal Shima. Tree search algorithm for assigning cooperating UAVs to multiple tasks [J]. Robust Nonlinear Control , 2008, 18:135–153
[23] Joel G. Manathara·P. B. Sujit·Randal W. Beard. Multiple UAV Coalitions for a Search and Prosecute Mission [J]. J Intell Robot Syst,2010
[24] Anawat P, Rolf R. Real-Time Planning for Teams of Autonomous Vehicles in Dynamic Uncertain Environments [J] .University of Washington, 2004.
[25] Mehdi Alighanbari, Jonathan How. A robust approach to the UAV task assignment problem [J]. International Journal of Robust and Nonlinear Control. 2008, 18: 118-134.
[26] Mehdi Alighanbari, Jonathan How. Robust Decentralized Task Assignment for Cooperative UAVs[C]. AIAA Guidance,Navigation and Control Conference and Exhibit, Colorado, August 2006, AIAA2006-6454
[27] Luc Brunet, Han-Lim Choi, Jonathan P. How. Consensus-Based Auction Approaches for Decentralized Task Assignment[C]. AIAA Guidance, Navigation and Control Conference and Exhibit, Hawaii, August, 2008, AIAA-2008-6839.
[28]张莉,张安.不确定环境下编队对地攻防对抗决策方法研究[J].系统工程与电子技术,2009, 31(2):411-415
[29]张煜.战术飞机飞行任务规划中不确定性因素处理方法研究[D].国防科技大学硕士论文,2006
[30]张永富.基于遗传算法的网络可靠性优化设计[D].南京邮电大学硕士论文,2008
[31]史志富,刘海燕等.基于影响图的UCAV编队对地攻击战术决策研究[J].系统工程与电子技术,2009,31(1):130-133
[32]唐苏妍,朱一凡等.多Agent系统任务分配方法综述[J].系统工程与电子技术,2010,32(10):2155-2161.
[33]李玉吉,卢才武,刘冠.蚁群遗传算法在高校智能排课系统中的应用[J].现代电子技术,2010,14:121-123
[34] Rathbun R., Capozzi B. Evolutionary Approaches to Path Planning Through Uncertain Environments [C]. Proceedings of the AIAA Unmanned Unlimited Conference, Portsmouth, VA., May 2002
[35] Risto Lahdelma, Joonas Hokkanen, Pekka Salminen. SMAA-Stochastic multi-objective acceptability analysis. European Journal of Operational Research [J]. 1998, 106(1): 137-143
[36] Risto Lahdelma, Pekka Salminen. SMAA-2: Stochastic multi-criteria acceptability analysis for group decision making. European Journal of Operational Research [J]. 2001, 49(3): 444-454
[37] Risto Lahdelma, Kaisa Miettinen, Pekka Salminen. Ordinal criteria in stochastic multi-criteria acceptability analysis (SMAA). European Journal of Operational Research [J]. 2003, 47(1): 117-127
[38]陈凤,先晓兵.基于DFS的多Agent动态任务分配算法[J].计算机工程,2009,35 (14):230-233
[39]王迎超,尚岳全等.基于熵权-理想点法的岩爆烈度预测模型及其应用[J].煤炭学报,2010,35 (2):218-221
[40]兰蓉.一种基于区间数距离的理想点多属性决策方法[J].西安邮电学院学报,2009,14 (1):153-156
[41]管怀建,唐亮,邓志江.基于正负理想点法的火炮武器系统作战效能评估[J].兵工自动化,2010,29(3):44-46