Attacking Strategy of Multiple Unmanned Surface Vehicles with Improved GWO Algorithm Under Control of Unmanned Aerial Vehicles

doi:10.1007/s12204-020-2159-2

Abstract

Abstract: Unmanned combat system is one of the important means to capture information superiority, carry out precision strike and accomplish special combat tasks in information war. Unmanned attack strategy plays a crucial role in unmanned combat system, which has to ensure the attack by unmanned surface vehicles (USVs) from failure. To meet the challenge, we propose a task allocation algorithm called distributed auction mechanism task allocation with grey wolf optimization (DAGWO). The traditional grey wolf optimization (GWO) algorithm is improved with a distributed auction mechanism (DAM) to constrain the initialization of wolves, which improves the optimization process according to the actual situation. In addition, one unmanned aerial vehicle (UAV) is employed as the central control system to establish task allocation model and construct fitness function for the multiple constraints of USV attack problem. The proposed DAGWO algorithm can not only ensure the diversity of wolves, but also avoid the local optimum problem. Simulation results show that the proposed DAGWO algorithm can effectively solve the problem of attack task allocation among multiple USVs.

Key words: unmanned surface vehicle (USV)| attack strategy| grey wolf optimization (GWO)| task allocation| unmanned aerial vehicle (UAV)

摘要： Unmanned combat system is one of the important means to capture information superiority, carry out precision strike and accomplish special combat tasks in information war. Unmanned attack strategy plays a crucial role in unmanned combat system, which has to ensure the attack by unmanned surface vehicles (USVs) from failure. To meet the challenge, we propose a task allocation algorithm called distributed auction mechanism task allocation with grey wolf optimization (DAGWO). The traditional grey wolf optimization (GWO) algorithm is improved with a distributed auction mechanism (DAM) to constrain the initialization of wolves, which improves the optimization process according to the actual situation. In addition, one unmanned aerial vehicle (UAV) is employed as the central control system to establish task allocation model and construct fitness function for the multiple constraints of USV attack problem. The proposed DAGWO algorithm can not only ensure the diversity of wolves, but also avoid the local optimum problem. Simulation results show that the proposed DAGWO algorithm can effectively solve the problem of attack task allocation among multiple USVs.

关键词: unmanned surface vehicle (USV)| attack strategy| grey wolf optimization (GWO)| task allocation| unmanned aerial vehicle (UAV)

CLC Number:

TP 399

WU Xin (武星), PU Juan (蒲娟), XIE Shaorong (谢少荣) . Attacking Strategy of Multiple Unmanned Surface Vehicles with Improved GWO Algorithm Under Control of Unmanned Aerial Vehicles[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(2): 201-207.

References 18

[1]	ZHAO X G. The impact of UAV system on future war [J]. National Defense Science & Technology, 2015,36(5): 55-58 (in Chinese).
[2]	SU J J, ZHENG X. Study on the influence of the development of unmanned combat system on army construction[J]. National Defense Science & Technology,2016, 37(5): 82-84 (in Chinese).
[3]	PU H Y, FENG D, LI X M, et al. Maritime autonomous obstacle avoidance in a dynamic environment based on collision cone of ellipse [J]. Chinese Journal of Scientific Instrument, 2017, 38(7): 1756-1762 (in Chinese).
[4]	ZHU J Y, WEI Z H, LI T Z. Research of wolf pack algorithm [C]//International Conference on Intelligent Transportation, Big Data & Smart City. Xiamen,China: IEEE, 2018: 695-698.
[5]	WANG Z C. Development and analysis of UAV equipment technology [J]. Ship Electronic Engineering,2016, 36(7): 27-31 (in Chinese).
[6]	CHENG X D, FENG J F, DU W H. Application research of submarine cooperative engagement support by multi-UAV system [J]. Ship Electronic Engineering,2013, 32(2): 4-6 (in Chinese).
[7]	YANG T H, HSIUNG S H, KUO C H, et al. Development of unmanned surface vehicle for water quality monitoring and measurement [C]//IEEE International Conference on Applied System Innovation.Chiba, Japan: IEEE, 2018: 566-569.
[8]	ZHANG B, KANG F J, SU B. Simulation of allied attack mission planning model of multi-unmanned surface vessels [J]. Computer Simulation, 2015, 32(4):349-353 (in Chinese).
[9]	XIAOX S, DUFEKJ, WOODBURYT, et al. UAV assisted USV visual navigation for marine mass casualty incident response [C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Vancouver,BC, Canada: IEEE, 2017: 6105-6110.
[10]	CAO L. Improved genetic algorithm for fast path planning of USV [J]. Proceedings of SPIE, 2015, 9815:981529-1.
[11]	EL-MENSHAWY M, BENTAHAR J, EL KHOLY W, et al. Verifying conformance of multi-agent commitment-based protocols [J]. Expert Systems with Applications, 2013, 40: 122-138.
[12]	EDISON E, SHIMA T. Genetic algorithm for cooperative UAV task assignment and path optimization[C]//AIAA Guidance, Navigation and Control Conference and Exhibit. Honolulu, Hawaii, USA: AIAA, 2008:6317.
[13]	DU K L, SWAMY M N S. Search and optimization by metaheuristics: Techniques and algorithms inspired by nature [M]. Switzerland: Springer International Publishing Switzerland, 2016: 153-173.
[14]	JIE L, SUN Y, ZHANG Z X. Research on cluster attack mission planning of Multi UAVs based on DAMBBOPSO algorithm [C]//IEEE International Conference on Mechatronics and Automation. Takamatsu,Japan: IEEE, 2013: 1641-1645.
[15]	MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer [J]. Advances in Engineering Software, 2014,69: 46-61.
[16]	ZHANG S, ZHOU Y Q, LI Z M, et al. Grey wolf optimizer for unmanned combat aerial vehicle path planning[J]. Advances in Engineering Software, 2016, 99:121-136.
[17]	WEI Z L, ZHAO H, HAN B J, et al. Research on cooperative attack decision of unmanned combat aerial vehicles using self-adaptive grey wolf optimization [J].Computer Engineering and Applications, 2016, 52(18):257-261 (in Chinese).
[18]	WEI Z L, ZHAO H, LI M D, et al. A grey wolf optimization algorithm based on nonlinear adjustment strategy of control parameter [J]. Journal of Air Force Engineering University (Natural Science Edition),2016, 17(3): 68-72 (in Chinese).