基于多准则交互膜进化算法的UAV三维航迹规划

doi:10.3969/j.issn.1001-506X.2021.01.17

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (1): 138-146.doi: 10.3969/j.issn.1001-506X.2021.01.17

基于多准则交互膜进化算法的UAV三维航迹规划

来磊(), 吴德伟(), 邹鲲*(), 韩昆(), 李海林()

空军工程大学信息与导航学院, 陕西西安 710077

收稿日期:2020-01-16 出版日期:2020-12-25 发布日期:2020-12-30
通讯作者: 邹鲲 E-mail:lailei0731@126.com;wudewei74609@126.com;wyyxzk@163.com;hk199009@126.com;hailinli@126.com
作者简介:来磊(1983-),男,讲师,博士,主要研究方向为无人自主系统、智能导航等。E-mail:lailei0731@126.com|吴德伟(1964-),男,教授,博士,主要研究方向为无人系统、智能导航、量子导航技术。E-mail:wudewei74609@126.com|韩昆(1976-),男,博士研究生,主要研究方向为智能导航技术。E-mail:hk199009@126.com|李海林(1982-),男,副教授,博士,主要研究方向为惯性导航技术。E-mail:hailinli@126.com
基金资助:
国家自然科学基金(61603409);国家自然科学基金(61571456);中国博士后科学基金(2017M623352);中国博士后科学基金(2018T111148);陕西省自然科学基金(2020JM-352);陕西省自然科学基金(2020JM-343)

Three dimensional route planning of UAV based on the multi-criterion interactive membrane evolutionary algorithm

Lei LAI(), Dewei WU(), Kun ZOU*(), Kun HAN(), Hailin LI()

Information and Navigation College, Air Force Engineering University, Xi'an 710077, China

Received:2020-01-16 Online:2020-12-25 Published:2020-12-30
Contact: Kun ZOU E-mail:lailei0731@126.com;wudewei74609@126.com;wyyxzk@163.com;hk199009@126.com;hailinli@126.com

摘要/Abstract

摘要：

针对智能优化算法在无人机(unmanned aerial vehicle, UAV)三维航迹优化中搜索复杂度较高、容易陷入局部最优的问题,提出一种基于嵌套式细胞膜结构的多准则交互式多目标进化算法。以建立的多目标航迹评价模型来克服航迹评价加权求和的不足;同时在应用降维离散缩减寻优空间的基础上,采用萤火虫算法和人工蜂群算法作为不同膜内优化准则,利用膜系统计算的并行性和膜内信息交互优势提高算法性能;并对膜内进化规则进行非支配排序、搜索加权等改进,实现了UAV三维多目标航迹寻优。仿真实验表明,所提方法在有无威胁两种环境下均能快速搜索到不同侧重目标的相对最优航迹,证明了该方法的有效性。

关键词: 无人机航迹规划, 膜系统, 多目标优化, 萤火虫算法, 人工蜂群算法

Abstract:

Aiming at the problem of the high complexity and easy to fall into the local optimal for the intelligent optimization algorithms in solving unmanned aerial vehicle(UAV) three dimensional route planning, a multi-criterion interactive multi-objective evolutionary algorithm based on the nested membrane structure is proposed. The multi-objective evaluation model is established to overcome the deficiency of weighted sum of route planning evaluation. Meanwhile, based on the application of the dimensionality reduction discrete to reduce the optimal space, firefly algorithm and artificial bee colony algorithm are used as intra-membrane optimization rules, takes advantage of the parallelism of membranes structure and the information interaction within the membrane to improve the performance of the algorithm. And the evolution rules within the membrane are improved by the method of non-dominated sorting and search weighting to realize the three dimensional multi-target route planning optimization. The simulation experiments show that the proposed algorithm can quickly find the relative optimal trajectory with different focuses under both threat and non-threat environments, which demonstrate the efficiently of the proposed algorithm.

Key words: unmanned aerial vehicle (UAV) route planning, membrane system, multi-objective optimization, firefly algorithm, artificial bee colony algorithm

中图分类号:

V249

来磊, 吴德伟, 邹鲲, 韩昆, 李海林. 基于多准则交互膜进化算法的UAV三维航迹规划[J]. 系统工程与电子技术, 2021, 43(1): 138-146.

Lei LAI, Dewei WU, Kun ZOU, Kun HAN, Hailin LI. Three dimensional route planning of UAV based on the multi-criterion interactive membrane evolutionary algorithm[J]. Systems Engineering and Electronics, 2021, 43(1): 138-146.

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参考文献 31

1	LIANG X , MENG G L , XU Y M , et al. A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment[J]. Intelligent Service Robotics, 2018, 11 (3): 301- 312.
2	DONG Y Q , EFE C , ERDAL K . Faster RRT-based nonholonomic path planning in 2D building environments using skeleton-constrained path biasin[J]. Journal of Intelligent & Robotic Systems, 2018, 89 (3): 387- 401.
3	YANG Q , YANG Z , HU G X , et al. A new fusion chemical reaction optimization algorithm based on random molecules for multi-rotor UAV path planning in transmission line inspection[J]. Journal of Shanghai Jiao tong University (Science), 2018, 23 (5): 671- 677.
4	JON V S, GRAVAN O H, BJERKNES J D, et al. Multi-UAV cooperative path planning for sensor placement using cooperative coevolving genetic strategy[C]//Proc.of the International Conference in Swarm Intelligence, 2017: 433-444.
5	ZHANG X Y , LU X Y , JIA S M , et al. A novel phase angleen-coded fruit fly optimization algorithm with mutation adaptation mechanism applied to UAV path planning[J]. Applied Soft Computing, 2018, 70, 371- 388.
6	MOHAMMADREZA R , MANISH K , MOHAMMAD S . Grey wolf optimization based sense and avoid algorithm in a Bayesian framework for multiple UAV path planning in an uncertain environment[J]. Aerospace Science and Technology, 2018, 77, 168- 179.
7	CHEN Y B , YU J Q , MEI Y S . Modified central force optimization algorithm for 3D UAV path planning[J]. Neurocomputing, 2016, 171 (C): 878- 888.
8	MILAD N , ESMAEEL K , SAMIRA D . Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm[J]. Expert Systems with Applications, 2019, 115, 106- 120.
9	ZHANG D F , DUAN H B , et al. Social-class pigeon-inspired optimization and time stamp segmentation for multi-UAV cooperative path planning[J]. Neurocomputing, 2018, 313, 229- 246.
10	WU X D , BAI W B , XIE Y E , et al. A hybrid algorithm of particle swarm optimization, metropolis criterion and RTS smoother for path planning of UAVs[J]. Applied Soft Computing Journal, 2018, 73, 735- 747.
11	MARCO A C , VICTOR A R , URIEL H B . Mobile robot path planning using artificial bee colony and evolutionary programming[J]. Applied Soft Computing, 2015, 30, 319- 328.
12	李军华, 刘群芳. 基于稀疏A*算法与文化算法的无人机动态航迹规划[J]. 应用科学学报, 2017, 35 (1): 128- 138.
	LI J H , LIU Q F . Dynamic path planning of unmanned aerial vehicle based on sparse A* algorithm and cultural algorithm[J]. Journal of Applied Sciences, 2017, 35 (1): 128- 138.
13	柳长安, 王晓鹏. 基于改进灰狼优化算法的无人机三维航迹规划[J]. 华中科技大学学报(自然科学版), 2017, 45 (10): 38- 42.
	LIU C A , WANG X P . Three dimensional route planning for unmanned aerial vehicle based on improved grey wolf optimizer algorithm[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2017, 45 (10): 38- 42.
14	ZHAO Y J , ZHENG Z , YANG L . Survey on computational-intelligence-based UAV path planning[J]. Knowledge-Based Systems, 2018, 158, 54- 64.
15	PAUN G. Computing with Membranes[D]. Turku: Turku Centre for Computer Science, 1998.
16	DONG W B , ZHOU K , QI H Q , et al. A tissue P system based evolutionary algorithm for multi-objective VRPTW[J]. Swarm and Evolutionary Computation, 2018, 39, 310- 322.
17	PENG H , WANG J . A hybrid approach based on tissue P systems and artificial bee colony for ⅡR system identification[J]. Neural Computing and Applications, 2017, 28 (9): 2675- 2685.
18	SONG B S , PAN L Q , et al. An efficient time-free solution to QSAT problem using P systems with proteins on membranes[J]. Information and Computation, 2017, 256, 287- 299.
19	黄长强, 赵克新. 基于改进蚁狮算法的无人机三维航迹规划[J]. 电子与信息学报, 2018, 40 (7): 1532- 1537.
	HUANG C Q , ZHAO K X . Three dimensional path planning of UAV with improved ant lion optimizer[J]. Journal of Electronics Information Technology, 2018, 40 (7): 1532- 1537.
20	LYU L , FAN T H , LI Q . Object tracking with improved firefly algorithm[J]. International Journal of Computing Science and Mathematics, 2018, 9 (3): 219- 231.
21	ANTONIO B , PATRICIA R R , FRANCISCO T R . A hyperbolastic type-I diffusion process: parameter estimation by means of the firefly algorithm[J]. Biosystems, 2018, 163, 11- 22.
22	LI P W , ZHAO J , XIE Z F , et al. General central firefly algorithm based on different learning time[J]. International Journal of Computing Science and Mathematics, 2017, 8 (5): 447- 456.
23	NGAAM J C , DING X M . A non-homogeneous firefly algorithm and its convergence analysis[J]. Journal of Optimization Theory and Applications, 2016, 170 (2): 616- 628.
24	ZHANG X . A modified artificial bee colony algorithm for image denoising using parametric wavelet thresholding method[J]. Pattern Recognition and Image Analysis, 2018, 28 (3): 557- 568.
25	ZHANG X , ZHANG X , WANG L . Antenna design by an adaptive variable differential artificial bee colony algorithm[J]. IEEE Trans.on Magnetics, 2018, 54 (3): 7201704.
26	SARAVANAN S , GOKULRAJ P . Improving performance an artificial bee colony optimisation on CloudSim[J]. International Journal of Internet Technology and Secured Transactions, 2018, 8 (4): 573- 582.
27	SHIMPI S J , JAGDISH C B . Artificial bee colony algorithm with global and local neighborhoods[J]. International Journal of System Assurance Engineering and Management, 2018, 9 (3): 589- 601.
28	XIE Y , SAVVARI A , ANTONIOS T . Sizing of hybrid electric propulsion system for retrofitting a mid-scale aircraft using non-dominated sorting genetic algorithm[J]. Aerospace Science and Technology, 2018, 82, 323- 333.
29	HUANG Y , FEI M R . Motion planning of robot manipulator based on improved NSGA-Ⅱ[J]. International Journal of Control Automation and System, 2018, 16 (4): 1878- 1886.
30	LYU L , ZHAO J , WANG J Y , et al. Multi-objective firefly algorithm based on compensation factor and elite learning[J]. Future Generation Computer Systems, 2019, 91, 37- 47.
31	GONG D W , HAN Y Y , SUN J Y . A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems[J]. Knowledge-Based Systems, 2018, 148, 115- 130.

代价	P₁	P₂	P₃	P₄	P₅	P₆
J_R	0.000	0.000	0.000	0.000	0.000	0.000
J_C	0.070	0.067	0.060	0.025	0.011	0.023
J_H	0.181	0.226	0.213	0.361	0.425	0.443

代价	P₁	P₂	P₃	P₄	P₅	P₆
J_R	0.130	0.214	0.000	0.014	0.103	0.147
J_C	0.037	0.055	0.159	0.169	0.014	0.013
J_H	0.241	0.219	0.323	0.337	0.395	0.456

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基于多准则交互膜进化算法的UAV三维航迹规划

Three dimensional route planning of UAV based on the multi-criterion interactive membrane evolutionary algorithm

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