多异构无人机任务规划的分布式一体化求解方法

doi:10.13229/j.cnki.jdxbgxb20170704

摘要/Abstract

摘要：

针对多异构固定翼无人机对已知目标群执行侦查、打击、评估任务的规划问题,提出了一种分布式一体化求解方法,该方法将任务执行耗时约束和协同攻击约束加入到协同任务规划模型(CMTAP)中,基于分布式规划架构,通过改进遗传算法的基因编码方式和相关遗传算子,完成任务分配和航迹生成两个子问题的一体化求解。将任务完成的总时间指标加入到代价函数中,保证了各无人机规划航迹的均匀性。建立固定翼无人机六自由度模型,采用矢量场航迹跟踪算法验证了该方法的可用性,并通过蒙特卡洛数学仿真,验证了该方法的快速性。

关键词: 飞行器设计, 任务分配, 航迹生成, 分布式遗传算法, 异构无人机, Dubins路径

Abstract:

This paper studies the planning problem of investigation, strike and evaluation task for known targets with multi-heterogeneous fixed wing Unmanned Aerial Vehicles (UAVs). Firstly, the task execution time-consuming constraints, collaborative attack constraints are added to the cooperative multiple task assignment problem (CMTAP). Then, based on the distributed planning architecture, the integrated solution of the two sub-problems is completed by improving the gene coding method. In order to optimize the uniformity of the UAVs' planning trajectories, the total time index of the task completion is added to the cost function. The feasibility of this method is verified by the six-degree-of-freedom model and the Vector-based path following algorithm. The simulation results show the rapidity of this method.

Key words: flight vehicle design, task assignment, trajectory generation, distributed genetic algorithm, heterogeneous unmanned aerial vehicle, Dubins trajectory

中图分类号:

TP29

吴蔚楠,崔乃刚,郭继峰,赵杨杨. 多异构无人机任务规划的分布式一体化求解方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1827-1837.

WU Wei-nan,CUI Nai-gang,GUO Ji-feng,ZHAO Yang-yang. Distributed integrated method for mission planning of heterogeneous unmanned aerial vehicles[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1827-1837.

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分布式遗传算法参数"

N_p	N_g	N_cp	N_e	N_cro	$P muta$
1000	500	10	100	800	0.03

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参考文献 21

[1]	Sujit P B, Kingston D, Beard R. Cooperative forest fire monitoring using multiple UAVs [C]//IEEE Conference on Decision and Control, New Orleans, Louisiana,USA, 2007: 4875-4880.
[2]	Rathinam S, Sengupta R, Darbha S . A resource allocation algorithm for multivehicle systems with nonholonomic constraints[J]. IEEE Trans Autom Sci Eng, 2007,4(1):98-104. doi: 10.1109/TASE.2006.872110
[3]	Ponda S S, Johnson L B, Geramifard A , et al. Cooperative Mission Planning for Multi-UAV Teams[M]. Netherlands:Springer, 2015: 1447-1490.
[4]	沈林成, 陈璟, 王楠 . 飞行器任务规划技术综述[J]. 航空学报, 2014,35(3):593-606. doi: 10.7527/S1000-6893.2013.0500
	Shen Lin-cheng, Chen Jing, Wang Nan . A survey on task planing technology for aircraft[J]. Acta Aeronautica et Astronautica Sinica, 2014,35(3):593-606. doi: 10.7527/S1000-6893.2013.0500
[5]	Chandler P R, Pachter M, Swaroop D, et al. Complexity in UAV cooperative control [C]//American Control Conference, IEEE, Anchorage,Alaska,USA, 2002: 1831-1836.
[6]	Woosley B, Dasgupta P. Multi-robot task allocation with real-time path planning [C]//Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference (FLAIRS-26 Conference), St. Pete Beach, FL, USA, 2013: 574579.
[7]	Arsie A, Savla K, Frazzoli E . Efficient routing algorithms for multiple vehicles with no explicit communications[J]. IEEE Transactions on Automatic Control, 2009,54(10):2302-2317. doi: 10.1109/TAC.2009.2028954
[8]	Moon S, Oh E, Shim D H . An integral framework of task assignment and path planning for multiple unmanned aerial vehicles in dynamic environments[J]. Journal of Intelligent & Robotic Systems, 2013,70(1-4):303-313. doi: 10.1007/s10846-012-9740-3
[9]	Rasmussen S J, Shima T . Tree search algorithm for assigning cooperating UAVs to multiple tasks[J]. International Journal of Robust and Nonlinear Control, 2008,18(2):135-153. doi: 10.1002/rnc.1257
[10]	Shima T, Rasmussen S J, Sparks A G , et al. Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms[J]. Computers & Operations Research, 2006,33(11):3252-3269. doi: 10.1016/j.cor.2005.02.039
[11]	Edison E, Shima T . Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms[J]. Computers & Operations Research, 2011,38(1):340-356. doi: 10.1016/j.cor.2010.06.001
[12]	Deng Q, Yu J, Wang N . Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes[J]. Chinese Journal of Aeronautics, 2013,26(5):1238-1250. doi: 10.1016/j.cja.2013.07.009
[13]	Zhang X, Chen J, Xin B , et al. A memetic algorithm for path planning of curvature-constrained UAVs per-forming surveillance of multiple ground targets[J]. Chinese Journal of Aeronautics, 2014,27(3):622-633. doi: 10.1016/j.cja.2014.04.024
[14]	Michalewicz Z, Fogel D B . How to Solve It: Modern Heuristics[M]. Berlin:Berlin Springer Science & Business Media, 2013.
[15]	Schaefer R. Foundations of Global Genetic Optimization[M]. Berlin: Springer Publishing Company, Incorporated, 2007.
[16]	楚拉多斯·怀特·尚穆加韦尔 . 无人机协同路径规划[M]. 北京: 国防工业出版社, 2011.
[17]	Dubins L E . On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents[J]. American Journal of Mathematics, 1957,79(3):497-516. doi: 10.2307/2372560
[18]	Yoav Gottlieb, Tal Shima . UAVs task and motion planning in the presence of obstacles and prioritized targets[J]. Sensors, 2015,15:29734-29764. doi: 10.3390/s151129734 pmid: 4701357
[19]	Beard R W, McLain T W . Small Unmanned Aircraft: Theory and Practice[M]. New Jersey: Princeton University Press, 2012.
[20]	Nelson D, Barber B, McLain T, et al. Vector field path following for small unmanned aerial vehicles [C]//Proceedings of the American Control Conference, Minneapolis, Minnesota, 2006: 5788-5794.
[21]	Interval Zero. RTX development guide[EB/OL].[2017-04-30]..

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UAV	巡航速度 /(m·s^-1)	初始位置和航向 /(m,m,(°))	保持的高度/m	最小转弯半径/m	极限探测距离/m
UAV-R	55	(0,0,0)	300	300	520
UAV-C	55	(0,0,0)	350	300	520
UAV-S	65	(0,0,0)	400	260	-
UAV-S	65	(0,0,0)	430	260	-

目标	位置/m	包括的任务	相应任务需要的执行时间/s
1	(600,5200)	{R,S,V}	5
2	(1120,5800)	{R,S,S,V}	5
3	(5600,5350)	{R,S,V}	5

时间	UAV1	UAV2	UAV3	UAV4
实际	297.1	304.3	100.2	236.5
规划	301.9	309.1	105.7	240.9