Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

doi:10.23919/JSEE.2021.000124

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (6): 1463-1476.doi: 10.23919/JSEE.2021.000124

• SYSTEMS ENGINEERING • Previous Articles Next Articles

Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

Jinqiang HU¹(), Husheng WU^1,*(), Renjun ZHAN¹(), Rafik MENASSEL²(), Xuanwu ZHOU³()

¹ School of Equipment Management and Support, Armed Police Force Engineering University, Xi’an 710086, China
² Department of Mathematics and Computer Science, Tebessa University, Tebessa 12002, Algeria
³ Foundation Department, Armed Police Command College, Tianjin 300250, China

Received:2020-11-29 Online:2022-01-05 Published:2022-01-05
Contact: Husheng WU E-mail:hujinqiang002@163.com;wuhusheng0421@163.com;zhanrenjun@aliyun.com;r.menassel@univ-tebessa.dz;schwoodchow@163.com
About author:|HU Jinqiang was born in 1990. He received his M.S. degree in vehicle operation engineering from Chang’an University, Xi’an, China, in 2017. He is currently a Ph.D. candidate with the School of Equipment Support and Management, Armed Police Force Engineering University. His current research interests include UAV swarm cooperative mission planning, swarm intelligent labor division approach, and swarm intelligence-based optimization algorithms. E-mail: hujinqiang002@163.com||WU Husheng was born in 1986. He received his Ph.D. degree from Air Force Engineering University in 2014. He is now an associate professor of the School of Equipment Management and Support, Armed Police Force Engineering University, Xi’an, China. He is now engaged in the study of wolf pack algorithm, anti-terrorist cluster operations, and intelligent equipment. E-mail: wuhusheng0421@163.com||ZHAN Renjun was born in 1963. He is a full professor and Ph.D. supervisor at the School of Equipment Management and Support, Armed Police Force Engineering University, Xi’an, China. He received his Ph.D. degree from Xi’an Jiaotong University in 1996. He is a Senior Member of China Society of Mechanical Engineering. His current research interests are evolutionary computing, equipment support, and task allocation of UAV swarm. E-mail: zhanrenjun@aliyun.com||MENASSEL Rafik was born in 1983. He currently works at the Department of Mathematics and Computer Science, Tebessa University. He does research in computer graphics, artificial neural network, and artificial intelligence. E-mail: r.menassel@univ-tebessa.dz||ZHOU Xuanwu was born in 1980. He is now a lecturer of Armed Police Command College. His current research interests are UAV swarm ad hoc network technology. E-mail: schwoodchow@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61502534), the Shaanxi Provincial Natural Science Foundation (2020JQ-493), the Integrative Equipment Research Project of Armed Police Force (WJ20211A030018), the Military Science Project of the National Social Science Fund (WJ2019-SKJJ-C-092), and the Theoretical Research Foundation of Armed Police Engineering University (WJY202148)

Abstract

Abstract:

Cooperative search-attack is an important application of unmanned aerial vehicle (UAV) swarm in military field. The coupling between path planning and task allocation, the heterogeneity of UAVs, and the dynamic nature of task environment greatly increase the complexity and difficulty of the UAV swarm cooperative search-attack mission planning problem. Inspired by the collaborative hunting behavior of wolf pack, a distributed self-organizing method for UAV swarm search-attack mission planning is proposed. First, to solve the multi-target search problem in unknown environments, a wolf scouting behavior-inspired cooperative search algorithm for UAV swarm is designed. Second, a distributed self-organizing task allocation algorithm for UAV swarm cooperative attacking of targets is proposed by analyzing the flexible labor division behavior of wolves. By abstracting the UAV as a simple artificial wolf agent, the flexible motion planning and group task coordinating for UAV swarm can be realized by self-organizing. The effectiveness of the proposed method is verified by a set of simulation experiments, the stability and scalability are evaluated, and the integrated solution for the coupled path planning and task allocation problems for the UAV swarm cooperative search-attack task can be well performed.

Key words: search-attack mission planning, unmanned aerial vehicle (UAV) swarm, wolf pack, hunting behavior, swarm intelligence, labor division

Jinqiang HU, Husheng WU, Renjun ZHAN, Rafik MENASSEL, Xuanwu ZHOU. Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior[J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1463-1476.

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Behavior characteristics	Wolf pack hunting	UAV swarm cooperative search-attack
Behavior actor	Wolf pack	UAV swarm
Behavior actor	Prey	Target
Behavior space	Hunting territory	Task environment
Behavior space	Scouting	Search target
Specific behavior	Labor division	Attack task allocation
Specific behavior	Besieging	Coordinated attack

UAV (U_i)	Type	Initial resource	Initial position (x_i, y_i)/m	Initial course angleo_i/(^o)
U₁	RS-UAV	(1,2,1)	(180,20)	45
U₂		(2,1,0)	(500,20)	90
U₃		(2,2,1)	(820,20)	135
U₄		(1,1,3)	(1 180,20)	45
U₅	R-UAV	(0,0,0)	(1 500,20)	90
U₆	R-UAV	(0,0,0)	(1 820,20)	135

Target (T_j)	Initial resource requirement	Position (x_j, y_j)/m	Value
T₁	(3,2,1)	(780,780)	3
T₂	(2,3,1)	(1 620,980)	5
T₃	(1,0,2)	(980,1 620)	4

Time/s	Target	Target resource requirement	UAV resource	Remaining resource
18.2	T₂	(2,3,1)	U₃(2,2,1) U₄(1,1,3)	(0,0,0) (1,0,3)
19.4	T₂	(0,1,0)	U₃(2,2,1) U₄(1,1,3)	(0,0,0) (1,0,3)
19.2	T₁	(3,2,1)	U₁(1,2,1) U₂(2,1,0)	(0,0,0) (0,1,0)
21.4	T₁	(2,0,0)	U₁(1,2,1) U₂(2,1,0)	(0,0,0) (0,1,0)
28.8	T₃	(1,0,2)	U₄(1,0,3)	(0,0,1)

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Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

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UAV swarm	Number of UAVs
R-UAVs	1	2	3	4
RS-UAVs	4	8	12	16