Air-to-ground reconnaissance-attack task allocation for heterogeneous UAV swarm

doi:10.23919/JSEE.2025.000012

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (1): 155-175.doi: 10.23919/JSEE.2025.000012

• SYSTEMS ENGINEERING • Previous Articles

Air-to-ground reconnaissance-attack task allocation for heterogeneous UAV swarm

Yuelong LUO¹(), Xiuqiang JIANG¹^,²^,³^,*(), Suchuan ZHONG¹(), Yuandong JI¹^,²^,³()

¹ School of Aeronautics and Astronautics, Sichuan University, Chengdu 610207, China
² Robotic Satellite Key Laboratory of Sichuan Province, Sichuan University, Chengdu 610207, China
³ Space Advanced Mechanism and Intelligent Flight Vehicle Key Laboratory of the Ministry of Education, Sichuan University, Chengdu 610207, China

Received:2023-04-06 Online:2025-02-18 Published:2025-03-18
Contact: Xiuqiang JIANG E-mail:yuelong@stu.scu.edu.cn;jiangxiuqiang@scu.edu.cn;sczhong@scu.edu.cn;jyd1127@scu.edu.cn
About author:
LUO Yuelong was born in 1997. He received his M.S. degree from Sichuan University, Chengdu, China, in 2023. He is pursuing his Ph.D. degree in National University of Defense Technology. His research interests are multi-agent system task planning and space orbital game. E-mail: yuelong@stu.scu.edu.cn

JIANG Xiuqiang was born in 1987. He received his Ph.D. degree from the College of Astronautics, Nanjing University of Aeronautics and Astronautics, China, in 2019. He is an associate professor in Sichuan University. His research interest is flight vehicle confrontation and game. E-mail: jiangxiuqiang@scu.edu.cn

ZHONG Suchuan was born in 1986. She received her Ph.D. degree from Sichuan University, Chengdu, China, in 2013. She is an associate professor in Sichuan University. Her research interests are stochastic dynamical system, and stochastic signal processing. E-mail: sczhong@scu.edu.cn

JI Yuandong was born in 1985. He received his Ph.D. degree from Sichuan University, Chengdu, China, in 2014. He is an associate professor in Sichuan University. His research interests are multi-agent swarm collaborative control, and rigid flexible coupling dynamics and control of flexible spacecraft. E-mail: jyd1127@scu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (12202293), and the Sichuan Science and Technology Program (2023NSFSC0393; 2022NSFSC1952).

Abstract

Abstract:

A task allocation problem for the heterogeneous unmanned aerial vehicle (UAV) swarm in unknown environments is studied in this paper. Considering that the actual mission environment information may be unknown, the UAV swarm needs to detect the environment first and then attack the detected targets. The heterogeneity of UAVs, multiple types of tasks, and the dynamic nature of task environment lead to uneven load and time sequence problems. This paper proposes an improved contract net protocol (CNP) based task allocation scheme, which effectively balances the load of UAVs and improves the task efficiency. Firstly, two types of task models are established, including regional reconnaissance tasks and target attack tasks. Secondly, for regional reconnaissance tasks, an improved CNP algorithm using the uncertain contract is developed. Through uncertain contracts, the area size of the regional reconnaissance task is determined adaptively after this task assignment, which can improve reconnaissance efficiency and resource utilization. Thirdly, for target attack tasks, an improved CNP algorithm using the fuzzy integrated evaluation and the double-layer negotiation is presented to enhance collaborative attack efficiency through adjusting the assignment sequence adaptively and multi-layer allocation. Finally, the effectiveness and advantages of the improved method are verified through comparison simulations.

Key words: unmanned aerial vehicle (UAV) swarm, reconnaissance-attack coupled task allocation, contract net protocol (CNP), fuzzy integrated evaluation, double-layer negotiation

Yuelong LUO, Xiuqiang JIANG, Suchuan ZHONG, Yuandong JI. Air-to-ground reconnaissance-attack task allocation for heterogeneous UAV swarm[J]. Journal of Systems Engineering and Electronics, 2025, 36(1): 155-175.

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Evaluation set	Definition
Urgency evaluation set	$ {v^{{\mathrm{urg}}}} $={Urgent, Normal, Not urgent}
Importance evaluation set	$ {v^{{\mathrm{imp}}}} $={Important, Normal, Not important}
Execution time evaluation set	$ {v^{{\mathrm{exe}}}} $={Short, Normal, Long}
Complexity evaluation set	$ {v^{{\mathrm{com}}}} $={Complex, Normal, Simple}

Target	Location/km	Missile defense capability	Laser defense capability	EMP defense capability	Attack revenue	Latest time/h	Number of UAVs required
Target 1	(185,153)	65	22	81	10	1.2	5
Target 2	(293,151)	43	20	57	20	2.7	4
Target 3	(289,209)	95	76	29	30	2	5
Target 4	(260,189)	97	18	25	40	2.8	4
Target 5	(86,166)	31	77	73	50	1	6
Target 6	(288,188)	73	47	24	60	2.6	5
Target 7	(28,151)	33	52	48	70	2.8	6
Target 8	(194,160)	92	68	42	80	1.2	7
Target 9	(18,295)	60	57	41	90	1.9	7
Target 10	(226,272)	42	70	88	100	2.7	7

Area	Center location/km	Radius/km
Area 1	（165, 39）	18
Area 2	（270, 60）	15
Area 3	（240, 210）	45
Area 4	（105, 105）	21
Area 5	（186, 171）	42
Area 6	（69, 195）	50

ID	Detection width/km	Flight velocity/(km/h)	Current location/km	Current task execution time/h
UAV_r 1	20	3600	(150,1)	0
UAV_r 2	20	2400	(150,1)	0
UAV_r 3	20	1200	(150,1)	0
UAV_r 4	20	2400	(150,1)	0
UAV_r 5	20	3600	(150,1)	0

ID	Sub-swarm	Task volume	Survival capability (0-100)	Flight velocity (km/h)	Attack capability (0-100)
UAV 1	Missile	3	28	1200	84
UAV 2	Missile	3	85	1200	89
UAV 3	Missile	3	76	1200	100
UAV 4	Missile	3	2	1200	77
UAV 5	Missile	3	77	1200	91
UAV 6	Missile	3	44	1200	80
UAV 7	Missile	3	61	1200	91
UAV 8	Missile	3	14	1200	96
UAV 9	Missile	3	55	1200	80
UAV 10	Missile	3	17	1200	85
UAV 11	Laser	3	67	1200	77
UAV 12	Laser	3	7	1200	98
UAV 13	Laser	3	3	1200	79
UAV 14	Laser	3	73	1200	88
UAV 15	Laser	3	3	1200	99
UAV 16	Laser	3	12	1200	94
UAV 17	Laser	3	57	1200	82
UAV 18	Laser	3	5	1200	91
UAV 19	Laser	3	4	1200	83
UAV 20	Laser	3	73	1200	87
UAV 21	EMP	3	99	1200	80
UAV 22	EMP	3	70	1200	88
UAV 23	EMP	3	70	1200	95
UAV 24	EMP	3	29	1200	96
UAV 25	EMP	3	80	1200	76
UAV 26	EMP	3	56	1200	91
UAV 27	EMP	3	45	1200	91
UAV 28	EMP	3	89	1200	98
UAV 29	EMP	3	82	1200	85
UAV 30	EMP	3	58	1200	81

Air-to-ground reconnaissance-attack task allocation for heterogeneous UAV swarm

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Task number	Task priority
No. 7	95
No. 8	92
No. 6	87
No. 4	84
No. 10	79
No. 5	79
No. 2	77
No. 1	72
No. 9	70
No. 3	68

Attack UAV sub-swarm	Number of tasks
Missile sub-swarm	7, 10, 5
Laser sub-swarm	4, 2, 1
EMP sub-swarm	8, 6, 9, 3

Sub-swarm	UAV number	Task number
Missile sub-swarm	UAV1	10, 5
	UAV2	7, 10
	UAV3	7, 10
	UAV4	10
	UAV5	7, 10
	UAV6	7, 5
	UAV7	7, 5
	UAV8	10, 5
	UAV9	7, 5
	UAV10	10, 5
Laser sub-swarm	UAV11	4, 1
	UAV12	2
	UAV13	1
	UAV14	4, 1
	UAV15	1
	UAV16	2
	UAV17	4
	UAV18	2
	UAV19	2
	UAV20	4, 1
EMP sub-swarm	UAV21	8, 6, 3
	UAV22	8, 9
	UAV23	8, 9
	UAV24	6, 3
	UAV25	8, 6, 3
	UAV26	6, 9
	UAV27	6, 9
	UAV28	8, 9, 3
	UAV29	8, 9, 3
	UAV30	8, 9

Evaluation metrics	Improved CNP algorithm	Basic CNP algorithm
Average attack effectiveness	55.61	35.68
Allocation time/s	0.341	0.627

Parameter	Location/km	Missile defense capability	Laser defense capability	EMP defense capability	Attack revenue	Latest time/h	Number of UAVs required
Task 11	(100,10)	36	21	61	100	1.2	5
Task 12	(140,30)	65	19	49	100	1.2	4
Task 13	(170,40)	85	67	24	100	1.2	5

Formation	UAV number	Task number
Missile sub-swarm	UAV1	12, 10, 5
	UAV2	7, 10
	UAV3	7, 10
	UAV4	12, 10
	UAV5	7, 10
	UAV6	7, 5
	UAV7	7, 5
	UAV8	12, 10, 5
	UAV9	7, 5
	UAV10	12, 10, 5
Laser sub-swarm	UAV11	4, 1
	UAV12	11, 2
	UAV13	2, 2
	UAV14	4, 1
	UAV15	11, 1
	UAV16	11, 2
	UAV17	4
	UAV18	11, 1
	UAV19	11, 2
	UAV20	4, 1
EMP sub-swarm	UAV21	8, 6, 9
	UAV22	8, 13, 3
	UAV23	8, 13, 3
	UAV24	6, 9
	UAV25	8, 6, 9
	UAV26	6, 9, 3
	UAV27	6, 9, 3
	UAV28	8, 13, 9
	UAV29	8, 13, 9
	UAV30	8, 13, 3

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