多无人机协同作战关键技术研究
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摘要
本文从无人机发展的趋势出发,系统的研究了多无人机协同编队执行攻击任务时的几个关键性问题:1)多机协同攻击航迹规划问题;2)多机协同空战攻击决策算法;3)多机协同编队内数据通信结构;4)OCP在多UAV的分布式控制平台的应用等问题。
本文具体研究内容如下:
1)在研究多架无人机协同攻击时的航迹规划问题时,为了使得各个UAV联合到达目标区域,同时满足暴露在雷达下的概率最小。提出一种分级分解的方法,将优化分解成三步:(1)为单机规划代价最小(或次最小)的航迹,利用计算几何上的快速随机扩展数算法和图搜索法中的Dijkstra算法,首先获得最优航迹的近似几何航迹;(2)进行满足时间约束的协同,确定团队ETA,选择满足协同要求的各架飞机的航迹和速度;(3)解决更细节的航迹产生问题,通过满足飞机机动性能限制使初始航迹细化成为可飞航迹。
2)在研究多机协同攻击问题时,将威胁指数、攻击优势和目标价值融合在一起作为多机协同多目标攻击逻辑的优化决策准则,同时引入蚁群算法,利用蚁群算法对问题的数学模型进行求解,实现了多机对多个目标攻击的最优目标分配和攻击排序。
3)探讨了无人机群局域组网的网络体系,设计了多无人机移动骨干网络,并深入研究了稳定链路与不稳定链路状态下的路由算法,为多机协同编队的通信以及各项作战任务的顺利完成提供了保障。
4)考虑到无人机编队是一组复杂的动态系统以及开放式控制平台的特点,我们研究了开放式控制平台在多无人机系统中的应用。探讨了DCOM中间件在多无人机协同控制系统分布式仿真的实现,在此基础上给出了多层的结构智能控制器和硬件在环仿真与飞行测试系统的描述,并基于Matlab/Simulink平台建立了多机协同系统仿真平台。
In this paper,we mainly research the trajectory problem on UAVs’task for coordinated search and combat, and also the problem of how to assign and combat the targets when they face lots of targets, then we discuss the application of open control system based on DCOM in the field of flight control of UAVs.
When researching the problem of trajectory plan of multi-UAV, in order to make every UAV arrive the target in the same time, and also take the smallest risk of exposure in the rador, we proposed a multi-levels structure, and divided the cooperated trajectory plan method into three levels. First, we use the Rapidly-exploring Random Tree (RRT) mathods and Dijkstra optimize mathods to make the optimum and suboptimum trajectory. Second, in order to fill the time constraint, make the Estimated Time of Arrival, choose the fulfilled trajectories and velocities of every UAV. At last, we smooth the sharp trajectories in order to make UAVs can fly.
Then, when researching the problem of coordinated combate for multi-UAV,we combined the threat factor, air combat situation and the target value together as the rule of how to optimize the attack logic for mutli-targets, first, we model the attack logic problem, then introduced the ACO into the optimize algorithm to assign the targets and design the attack logic.
After that, we discuss the communication networks system of multi-UAV, design a mobile backbone networks based on the Ad Hoc, and then go into the study of routing algorithm in the state of stable link and unstable link.
At last, with a view of the formation of UAV is a complicated dynamic system and the advantage of Open Control Platform (OCP), we research the use of OCP in the field of flight control system of UAV, discuss the multi-level application of DCOM midware which is the core part of OCP in the use of multi-UAV control platform, then we design the multi-level intelligent controller and hardware in the loop simulation and filght testing system, and then build a multi-UAV Coordinated simulation system based on the Matlab/Simulink platform..
本文具体研究内容如下:
1)在研究多架无人机协同攻击时的航迹规划问题时,为了使得各个UAV联合到达目标区域,同时满足暴露在雷达下的概率最小。提出一种分级分解的方法,将优化分解成三步:(1)为单机规划代价最小(或次最小)的航迹,利用计算几何上的快速随机扩展数算法和图搜索法中的Dijkstra算法,首先获得最优航迹的近似几何航迹;(2)进行满足时间约束的协同,确定团队ETA,选择满足协同要求的各架飞机的航迹和速度;(3)解决更细节的航迹产生问题,通过满足飞机机动性能限制使初始航迹细化成为可飞航迹。
2)在研究多机协同攻击问题时,将威胁指数、攻击优势和目标价值融合在一起作为多机协同多目标攻击逻辑的优化决策准则,同时引入蚁群算法,利用蚁群算法对问题的数学模型进行求解,实现了多机对多个目标攻击的最优目标分配和攻击排序。
3)探讨了无人机群局域组网的网络体系,设计了多无人机移动骨干网络,并深入研究了稳定链路与不稳定链路状态下的路由算法,为多机协同编队的通信以及各项作战任务的顺利完成提供了保障。
4)考虑到无人机编队是一组复杂的动态系统以及开放式控制平台的特点,我们研究了开放式控制平台在多无人机系统中的应用。探讨了DCOM中间件在多无人机协同控制系统分布式仿真的实现,在此基础上给出了多层的结构智能控制器和硬件在环仿真与飞行测试系统的描述,并基于Matlab/Simulink平台建立了多机协同系统仿真平台。
In this paper,we mainly research the trajectory problem on UAVs’task for coordinated search and combat, and also the problem of how to assign and combat the targets when they face lots of targets, then we discuss the application of open control system based on DCOM in the field of flight control of UAVs.
When researching the problem of trajectory plan of multi-UAV, in order to make every UAV arrive the target in the same time, and also take the smallest risk of exposure in the rador, we proposed a multi-levels structure, and divided the cooperated trajectory plan method into three levels. First, we use the Rapidly-exploring Random Tree (RRT) mathods and Dijkstra optimize mathods to make the optimum and suboptimum trajectory. Second, in order to fill the time constraint, make the Estimated Time of Arrival, choose the fulfilled trajectories and velocities of every UAV. At last, we smooth the sharp trajectories in order to make UAVs can fly.
Then, when researching the problem of coordinated combate for multi-UAV,we combined the threat factor, air combat situation and the target value together as the rule of how to optimize the attack logic for mutli-targets, first, we model the attack logic problem, then introduced the ACO into the optimize algorithm to assign the targets and design the attack logic.
After that, we discuss the communication networks system of multi-UAV, design a mobile backbone networks based on the Ad Hoc, and then go into the study of routing algorithm in the state of stable link and unstable link.
At last, with a view of the formation of UAV is a complicated dynamic system and the advantage of Open Control Platform (OCP), we research the use of OCP in the field of flight control system of UAV, discuss the multi-level application of DCOM midware which is the core part of OCP in the use of multi-UAV control platform, then we design the multi-level intelligent controller and hardware in the loop simulation and filght testing system, and then build a multi-UAV Coordinated simulation system based on the Matlab/Simulink platform..
引文
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[3] B.Freiberg. DARPA/USAF:Unmanned Combat Air Vehicle System Demonstration Program, 2002.5.21.
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[9]宋绍梅.无人机多机协同路径规划和航迹产生算法研究[D].西北工业大学,2003.
[10] J. D. Wolfe, D. F. Chichka, and J. L. Speyer. Decentralized controllers for unmanned aerial vehicle formation flight. AIAA Guidance Navigation and Control Conference, SanDiego, CA, July, 1996.
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[13] Lewis, A.M., Pizzi. S.V.,Quality of service for tactical data links: TDMA with dynamic scheduling, Digital Object Identifier 10.1109 MILCOM.2005.1606020,17-20 Oct. 2005 Page(s):2350 - 2359 Vol. 4
[14]阿瑟K塞伯斯基,刘笑妍.网络中心战的起源与未来[J].情报指挥控制系统与仿真技术1999年第01期.
[15] Stanley H. Pruett, Gary J. Slutz, Dr.James L.Paunicka, Eric Portilla. Hardware-In-The-Loop Simulation using Open Control Platform. AIAA Modeling & Simulation Technologies Conference. August 2003, Austin, Texas.pp.1-11
[16] ERIC NETTLETON, MATTHEW RIDLEY, SALAH SUKKARIEH, ALI Haydar Goktogan, HUGH DURRANT-WHYTE. Implementation of a Decentralised Sensing Network aboard Multiple UAVs. Telecommunication Systems. 2004, pp:253–284
[17] J. How, Y. Kuwata, E. King.Flight Demonstrations of Cooperative Control for UAV Teams. AIAA-2004-6490. AIAA 3rd "Unmanned Unlimited" Technical Conference, Workshop and Exhibit, Chicago, Illinois, Sep. 20-23, 2004.
[18]徐锦法.无人飞行器分布式控制系统集成新技术[J].系统仿真学报.Vol. 15 No. 3 March 2003.
[19] Wills L, Kannan S, Heck B, Vachtsevanos G, Restrepo C, Sander S,Schrage D, Prasad J V R. An open software infrastructure for reconfigurable control systems [C]. In Proc. 19th American Control Conference (ACC-2000), Chicago, IL, 2000, (6): 2799-2803.
[20]唐强,张翔伦,左玲.无人机航迹规划算法的初步研究[J].航空计算技术.Vol133 No11 Mar.2003.
[21] P. R. Chandler et al. Research issues in autonomous control of tactical UAVs [J] . Proceedings of the American Control Conference, pp. 394 - 398, 1998.
[22] Emilio Frazzoli et al. Real - time motion planning for agile autonomous vehicles [J]. Journal of Guidance, Control and Dynamics, Vol. 25, No. 1, pp. 116 - 129, 2002.
[23] G. Vachtevanos et al. Autonomous vehicles: fromflight control to mission planning using fuzzy logic techniques [J]. Proceedings of the 13th International Digital Signal Processing Conference, pp. 977 - 981, 1997.
[24] Robert J. Szczerba et al. Robust algorithmfor real - time route planning [J] . IEEE Transactions on Aerospace and Electronic Systems Vol. 36 No. 3, pp. 869 - 878, 2000.
[25] M. B. Pellazar. Vehicles route planning with constraints using genetic algorithms [J]. Proceedings of the IEEE 1994 National Aerospace and Electronic Conference, pp. 111-119, 1994.
[26] Timothy W. Mclain et al. Trajectory planning for coordinated rendezvous of unmanned air vehicles [J] . AIAA - 2000 -4369.
[27] Lydia E. Kavraki et al. Analysis of probabilistic roadmaps for path planning [J]. IEEE transactions on Robotics and Automation, Vol. 14, No. 1, pp. 166 - 171. 1998.
[28] Robert Bohlin and Lydia E. Kavraki. Path planning using lazy PRM [J]. Proceedings of the 2000 IEEE International Conference on Robotics and Automation, pp. 521 - 528, 2000.
[29] Marc W. McConley et al. Hybrid control for aggressive maneuvering of autonomous aerial vehicles [J]. Proceedings of the 19th Digital Avionics Systems Conference, 2000.
[30] Emilio Frazzoli. Robust hybrid control for autonomous vehicle motion planning [D]. Ph. D.Dissertation, Massachusetts Institute of Technology, 2001.
[31] M Andersson, J Wallander. Kin selection and reciprocity in flight formation Behavioral Ecology, 2004.15(1):158~162
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