Multi-platform collaborative MRC-PSO algorithm for anti-ship missile path planning

doi:10.23919/JSEE.2025.000026

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (2): 494-509.doi: 10.23919/JSEE.2025.000026

• • 上一篇

收稿日期:2024-05-15 出版日期:2025-04-18 发布日期:2025-05-20

Multi-platform collaborative MRC-PSO algorithm for anti-ship missile path planning

Gang LIU¹(), Xinyuan GUO¹(), Dong HUANG¹(), Kezhong CHEN²(), Wu LI³^,*()

¹ School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, China
² China Ship Development and Design Center, Wuhan 430064, China
³ Hunan Vocational College for Nationalities, Yueyang 414000, China

Received:2024-05-15 Online:2025-04-18 Published:2025-05-20
Contact: Wu LI E-mail:4350594@qq.com;812211120166@vip.hnist.edu.cn;596034082@qq.com;chenkezhongwork@126.com;12009012@hnist.edu.cn
About author:
LIU Gang was born in 1983. He received his B.S. and M.S. degrees from Naval Arms Command Institute, Guangzhou, China, in 2005 and 2008, respectively. He received his Ph.D. degree from National University of Defense Technology, Changsha, China, 2013. His research interests include intelligent information processing and collaborative mission planning. E-mail: 4350594@qq.com

GUO Xinyuan was born in 2001. She received her B.S. degree in digital media technology from University of South China, Hengyang, China, in 2022. She is pursuing her M.S. degree in Hunan Institute of Science and Technology, School of Information Science and Engineering. Her research interest is path planning. E-mail: 812211120166@vip.hnist.edu.cn

HUANG Dong was born in 1990. He received his B.S. and Ph.D. degrees in control science and engineering from China University of Petroleum, Beijing, China, in 2013 and 2018, respectively. His research interests include modeling, control, and optimization of complex systems. E-mail: 596034082@qq.com

CHEN Kezhong was born in 1991. He received his B.S. and M.S. degrees in weapon system architecture design from Beijing Institute of Technology, Beijing, China, in 2014 and 2017, respectively. His research interest is overall integrated design of ship electronic information systems. E-mail: chenkezhongwork@126.com

LI Wu was born in 1977. He received his B.S. degree from Shenyang Ligong University, Shenyang, China, in 2000, and M.S. degree in power system and its automation from Xinan Jiaotong University, Chengdu, China, in 2003, and Ph.D. degree in control science and engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2009. His research interests include intelligent decision analysis and optimization. E-mail: 12009012@hnist.edu.cn
Supported by:
This research was supported by Hunan Provincial Natural Science Foundation (2024JJ5173;2023JJ50047), Hunan Provincial Department of Education Scientific Research Project (23A0494), and Hunan Provincial Innovation Foundation for Postgraduate (CX20231221).

摘要/Abstract

Abstract:

To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper proposed multi-operator real-time constraints particle swarm optimization (MRC-PSO) algorithm. MRC-PSO algorithm utilizes a semi-rasterization environment modeling technique and integrates the geometric gradient law of ASMs which distinguishes itself from other collaborative path planning algorithms by fully considering the coupling between collaborative paths. Then, MRC-PSO algorithm conducts chunked stepwise recursive evolution of particles while incorporating circumvent, coordination, and smoothing operators which facilitates local selection optimization of paths, gradually reducing algorithmic space, accelerating convergence, and enhances path cooperativity. Simulation experiments comparing the MRC-PSO algorithm with the PSO algorithm, genetic algorithm and operational area cluster real-time restriction (OACRR)-PSO algorithm, which demonstrate that the MRC-PSO algorithm has a faster convergence speed, and the average number of iterations is reduced by approximately 75%. It also proves that it is equally effective in resolving complex scenarios involving multiple obstacles. Moreover it effectively addresses the problem of path crossing and can better satisfy the requirements of multi-platform collaborative path planning. The experiments are conducted in three collaborative operation modes, namely, three-to-two, three-to-three, and four-to-two, and the outcomes demonstrate that the algorithm possesses strong universality.

Key words: anti-ship missiles, multi-platform collaborative, path planning, particle swarm optimization (PSO) algorithm

. [J]. Journal of Systems Engineering and Electronics, 2025, 36(2): 494-509.

Gang LIU, Xinyuan GUO, Dong HUANG, Kezhong CHEN, Wu LI. Multi-platform collaborative MRC-PSO algorithm for anti-ship missile path planning[J]. Journal of Systems Engineering and Electronics, 2025, 36(2): 494-509.

图/表 19

参考文献 44

1	LIU S Y, LI S, LIU Y J, et al. Research on anti-ship missile cooperative trajectory planning based on gauss pseudospectral method. Proc. of the 5th Chinese Conference on Swarm Intelligence and Cooperative Control, 2022: 1163−1176.
2	LIU S Y, RUAN W H, QIU L K, et al. On-line distribution of coordinated attack targets for multi-anti-ship missiles based on genetic simulated annealing algorithm. Proc. of the Chinese Control and Decision Conference, 2019: 1228−1233.
3	LIU G, AN Z B, LAO S Y, et al Firepower distribution method of anti-ship missile based on coupled path planning. Journal of Systems Engineering and Electronics, 2022, 33 (4): 1010- 1024.
4	ZHANG H, XIN B, DOU L H, et al A review of cooperative path planning of an unmanned aerial vehicle group. Frontiers of Information Technology & Electronic Engineering, 2020, 21 (12): 1671- 1694.
5	LIU G, LAO S Y, TANG J, et al Progress on intelligent collaborative path planning for anti-ship missile. Journal of Command and Control, 2021, 7 (4): 342- 349.
6	LIU G, LAO S Y, TAN D F, et al Research status and progress on anti-ship missile path planning. Acta Automatica Sinica, 2013, 39 (4): 347- 359.
7	PHARPATARA P, HERISSE B, PEPY R, et al Sampling-based path planning: a new tool for missile guidance. IFAC Proceedings Volumes, 2013, 46 (19): 131- 136. doi: 10.3182/20130902-5-DE-2040.00091
8	KIM M J, KANG T Y, RYOO C K Real-time path planning for unmanned aerial vehicles based on compensated voronoi diagram. International Journal of Aeronautical and Space Sciences, 2025, 26 (1): 235- 244.
9	GUO J, XIA W, HU X X, et al Feedback RRT* algorithm for UAV path planning in a hostile environment. Computers & Industrial Engineering, 2022, 174, 108771.
10	HAO G Q, LV Q, HUANG Z, et al UAV path planning based on improved artificial potential field method. Aerospace, 2023, 10 (6): 562. doi: 10.3390/aerospace10060562
11	RAO J J, XIANG C Y, XI J Y, et al Path planning for dual UAVs cooperative suspension transport based on artificial potential field-A* algorithm. Knowledge-Based Systems, 2023, 277, 110797. doi: 10.1016/j.knosys.2023.110797
12	YUAN M S, ZHOU T L, CHEN M Improved lazy theta* algorithm based on octree map for path planning of UAV. Defence Technology, 2023, 23, 8- 18. doi: 10.1016/j.dt.2022.01.006
13	CUI J G, WU L, HUANG X D, et al Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning. Knowledge-Based Systems, 2024, 288, 111459. doi: 10.1016/j.knosys.2024.111459
14	LUO J, LIANG Q C, LI H UAV penetration mission path planning based on improved holonic particle swarm optimization. Journal of Systems Engineering and Electronics, 2023, 34 (1): 197- 213. doi: 10.23919/JSEE.2022.000132
15	LU F X, DAI Q Y, XU J F, et al Improved APF missile route planning based on circulation repulsion potential field. Journal of Aerospace Power, 2023, 38 (9): 2288- 2298.
16	ZHANG J D, GUO Y K, ZHENG L H, et al Real-time UAV path planning based on LSTM network. Journal of Systems Engineering and Electronics, 2024, 35 (2): 374- 385. doi: 10.23919/JSEE.2023.000157
17	WEN N F, ZHAO L L, SU X H, et al UAV online path planning algorithm in a low altitude dangerous environment. IEEE/CAA Journal of Autom, 2015, 2 (2): 173- 185.
18	WU J F, WANG H L, WANG Y X, et al UAV reactive interfered fluid path planning. Acta Automatica Sinica, 2023, 49 (2): 272- 287.
19	WANG Q J, SHENG P, PENG J Geometric principle based route planning of anti-ship missile in coordinated attacking. Electronics Optics and Control, 2019, 26 (9): 19- 25.
20	WANG Y J, LU M H, GONG C Research on anti-ship missile by submarine salvo route planning algorithms. Tactical Missile Technology, 2017, 6, 44- 49.
21	BABEL L Coordinated target assignment and UAV path planning with timing constraints. Journal of Intelligent Robotic Systems, 2019, 94 (3): 857- 869.
22	ZHANG C G, DING Y, SHEN X P Cooperative path planning for anti-ship missiles on multi-platform based on quantum bidirectional RRT algorithm. Fire Control & Command Control, 2017, 42 (4): 36- 41.
23	ZHANG J, DU X, DONG Q C, et al Distributed collaborative complete coverage path planning based on hybrid strategy. Journal of Systems Engineering and Electronics, 2023, 35 (2): 463- 472.
24	ALI Z A, HAN Z G, WANG B H Cooperative path planning of multiple UAVs by using max-min ant colony optimization along with cauchy mutant operator. Fluctuation Noise Letters, 2021, 20 (1): 2150002. doi: 10.1142/S0219477521500024
25	HUANG L W, QU H, JI P, et al A novel coordinated path planning method using k-degree smoothing for multi-UAVs. Applied Soft Computing, 2016, 48, 182- 192. doi: 10.1016/j.asoc.2016.06.046
26	NIU Y B, YAN X F, WANG Y Z, et al Three-dimensional collaborative path planning for multiple UCAVs based on improved artificial ecosystem optimizer and reinforcement learning. Knowledge-Based Systems, 2023, 276, 110782. doi: 10.1016/j.knosys.2023.110782
27	JAIN G, YADAV G, PRAKASH D, et al MVO-based path planning scheme with coordination of UAVs in 3-D environment. Journal of Computational Science, 2019, 37, 101016. doi: 10.1016/j.jocs.2019.07.003
28	SHI Y, ZHANG L H, DONG S Q, et al Path planning algorithm for antiship missile based on regional division. Systems Engineering and Electronics, 2019, 41 (3): 571- 578.
29	HUANG C, MA H J, ZHOU X B, et al. Cooperative path planning of multiple unmanned aerial vehicles using cylinder vector particle swarm optimization with gene targeting. IEEE Sensors Journal, 2025, 25(5): 8470−8480.
30	CAO Y, WEI W Y, BAI Y, et al Multi-base multi-UAV cooperative reconnaissance path planning with genetic algorithm. Cluster Computing, 2019, 22, 5175- 5184. doi: 10.1007/s10586-017-1132-9
31	LIU G, LAO S Y, TAN D F Converse path planning for anti-ship missiles based on operational area. Systems Engineering and Electronics, 2011, 33 (4): 799- 805.
32	SHI Y, ZHANG L H, DONG S Q. Path planning of anti-ship missile based on voronoi diagram and binary tree algorithm, 2019, 69(4):369−377.
33	CHENG L, LU H, LEI T, et al. Path planning for anti-ship missile using tangent based dubins path. Proc. of the 2nd International Conference on Intelligent Autonomous Systems, 2019: 175−180.
34	HAN Z L, CHEN M, ZHU H J, et al Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm. Defence Technology, 2024, 32, 1- 22.
35	TANG G, TANG C, CLARAMUNT C, et al Geometric A-star algorithm: An improved A-star algorithm for AGV path planning in a port environment. IEEE Access, 2021, 9, 59196- 59210.
36	LIU Y, ZHANG X J, ZHANG Y, et al Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach. Chinese Journal of Aeronautics, 2019, 32 (6): 1504- 1519.
37	ASLAN S, ERKIN T A multi-population immune plasma algorithm for path planning of unmanned combat aerial vehicle. Advanced Engineering Informatics, 2023, 55, 101829.
38	LIU G, LAO S Y, HOU L L, et al OARPER-MAFO algorithm for anti-ship missile path planning. Aerospace Science and Technology, 2015, 47, 135- 145.
39	SHEN J F, LIU X M, WU L H, et al Cooperative route planning of anti-ship missiles from multiple platforms. Tactical Missile Technology, 2009, 30 (2): 62- 66.
40	LIU G, LAO S Y, YUAN C, et al OACRR-PSO Algorithm for anti-ship missile path planning. Acta Automatica Sinica, 2012, 38 (9): 1528- 1537. doi: 10.3724/SP.J.1004.2012.01528
41	WU X L, WU S T, XING Z H. A guidance law with impact angle constraint for anti-ship missiles cooperative attack. Proc. of the 33rd Chinese Control Conference, 2014: 954−959.
42	TAVOOSI V, MARZBANRAD J, GOLNAVAZ M Optimized path planning of an unmanned vehicle in an unknown environment using the PSO algorithm. Proc. of the IOP Conference Series: Materials Science and Engineering, 2020, 671, 012009. doi: 10.1088/1757-899X/671/1/012009
43	SONG B Y, WANG Z D, ZOU L An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied soft computing, 2021, 100, 106960. doi: 10.1016/j.asoc.2020.106960
44	MASEHIAN E, SEDIGHIZADEH D. A multi-objective PSO-based algorithm for robot path planning. Proc. of the IEEE International Conference on Industrial Technology, 2010: 465−470.

Algorithm	Path information contained by particles	Path point generation method	Particle evolution method
PSO	One path	Random generation of horizontal and vertical coordinates in a rectangular area	Overall evolution
Genetic algorithm	One path	Random generation of horizontal and vertical coordinates in a rectangular area	Overall evolution
OACRR-PSO	One path	Random generation of horizontal and vertical coordinates within clusters of operatonal areas	Stepwise recursive evolution
MRC-PSO	2 or more paths	Randomized generation of horizontal and vertical coordinates within clusters of operatonal areas	Chunked stepwise recursive evolution, local selection optimization

Algorithm	Total range length	Total rotation angle	Total number of steering points	Incidence angle	Collaborative adaptation value
PSO	424.673	1.211	5	1.012	0.844
Genetic algorithm	427.701	1.194	5	1.17	0.830
OACRR-PSO	421.159	0.724	5	0.938	0.836
MRC-PSO	424.341	0.556	2	1.0476	0.954

Algorithm	Average number of iterations		Average collaborative fitness value	Times of path crossing or invalidation
PSO	Primary path	57.760	0.378	84
PSO	Secondary path	48.640	0.378	84
OACRR-PSO	Primary path	51.120	0.550	48
OACRR-PSO	Secondary path	39.750	0.550	48
Genetic algorithm	Primary path	54,729	0.640	32
Genetic algorithm	Secondary path	46.345	0.640	32
MRC-PSO	−	7.909	0.951	1

Item	x-axis coordinates	y-axis coordinates	Radius	Maximum effective range
Launch ship G	10	150	300	35
Launch ship H	50	10	280	30
Launch ship I	20	−100	320	35
Target ship 3	260	120	−	−
Target ship 4	220	10	−	−
Target ship 5	250	−120	−	−

Multi-platform collaborative MRC-PSO algorithm for anti-ship missile path planning

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参考文献 44

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Item	x-axis coordinates	y-axis coordinates	Radius	Maximum effective range	Minimum path distance
Launch ship A	0	0	−	320	30
Launch ship B	120	−70	−	200	35
Target ship C	0	260	−	−	−
Obstacles	75	−10	20	−	−
	130	−10	30	−	−
	190	−20	20	−	−
	180	−80	10	−	−

Item	x-axis coordinate	y-axis coordinate	Radius	Maximum effective range	Minimum path distance
Launch ship D	10	120	−	320	35
Launch ship E	120	0	−	200	35
Launch ship F	0	−100	−	280	35
Target ship 1	260	50	−	−	−
Target ship 2	220	−100	−	−	−
Obstacles	120	120	20	−	−
	100	120	10	−	−
	200	100	20	−	−
	170	5	25	−	−
	230	−60	10	−	−
	180	−80	10	−	−
	75	−100	20	−	−