复杂约束条件下无人系统跨域协同作战任务规划方法

doi:10.16182/j.issn1004731x.joss.25-0577

系统仿真学报 ›› 2026, Vol. 38 ›› Issue (3): 785-799.doi: 10.16182/j.issn1004731x.joss.25-0577

• 论文 • 上一篇

复杂约束条件下无人系统跨域协同作战任务规划方法

方浩杰¹, 甄子洋¹, 龚华军¹, 谢序²^,³, 罗伟¹^,⁴

^1.南京航空航天大学自动化学院，江苏南京 211106
^2.空基信息感知与融合全国重点实验室，河南洛阳 471009
^3.航空工业洛阳电光设备研究所，河南洛阳 471009
^4.中航金城无人系统有限公司，江苏南京 210002

收稿日期:2025-06-19 修回日期:2025-09-07 出版日期:2026-03-18 发布日期:2026-03-27
通讯作者: 甄子洋
第一作者简介:方浩杰(2001-)，男，硕士生，研究方向为无人系统任务规划。
基金资助:
中央高校基本科研业务费专项资金(56XCA2402810)

Task Planning Method for Cross-domain Cooperative Combat Operations of Unmanned Systems Under Complex Constraints

Fang Haojie¹, Zhen Ziyang¹, Gong Huajun¹, Xie Xu²^,³, Luo Wei¹^,⁴

^1.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
^2.National Key Laboratory of Air-based Information Perception and Fusion, Luoyang 471009, China
^3.Luoyang Institute of Electro-optical Equipment, AVIC, Luoyang 471009, China
^4.AVIC Jincheng Unmanned Systems Co. , Ltd. , Nanjing 210002, China

Received:2025-06-19 Revised:2025-09-07 Online:2026-03-18 Published:2026-03-27
Contact: Zhen Ziyang

摘要/Abstract

摘要：

在跨域协同作战战前任务规划中，针对无人系统性能差异与作战任务协同要求提升所带来的约束条件多样复杂、规划模型求解困难的问题，提出了一种多策略增强灰狼优化算法(multi-strategy enhanced GWO，MSEGWO)。考虑各型无人系统的性能、弹药使用、任务时序、任务时间窗、航路等多种复杂约束条件，建立了以最小化综合代价为目标的任务规划数学模型；设计了收敛因子非线性调整、备选解空间映射、任务时序修复、增强搜索等改进策略，以增强算法的寻优能力。仿真验证结果表明：所提算法能够有效求解复杂约束条件下无人系统跨域协同作战任务规划问题，且能够适应不同规模和不同复杂度的问题场景。

关键词: 无人系统, 跨域协同, 复杂约束条件, 任务规划, 灰狼优化算法

Abstract:

In pre-combat task planning for cross-domain cooperative combat operations, to solve the problems of diverse and complex constraints and difficulties in solving planning models caused by performance differences of unmanned systems and increased requirements for cooperative combat operations, a multi-strategy enhanced grey wolf optimization (MSEGWO) algorithm was proposed. By considering various complex constraints such as performance of each type of unmanned systems, munition usage, task timing, task time window, and flight path, a task planning mathematical model with minimizing the comprehensive cost as the objective was established. Improvement strategies such as nonlinear adjustment of convergence factor, alternative solution space mapping, task sequencing repair, and enhanced search were designed to enhance the optimization ability of the algorithm. Simulation results demonstrate that the proposed algorithm can effectively solve the cross-domain cooperative combat operation task planning problem of unmanned systems under complex constraints and can adapt to problem scenarios of different scales and complexities.

Key words: unmanned system, cross-domain cooperation, complex constraint, task planning, grey wolf optimization algorithm(GWO)

中图分类号:

TP391.9

方浩杰,甄子洋,龚华军等 . 复杂约束条件下无人系统跨域协同作战任务规划方法[J]. 系统仿真学报, 2026, 38(3): 785-799.

Fang Haojie,Zhen Ziyang,Gong Huajun,et al . Task Planning Method for Cross-domain Cooperative Combat Operations of Unmanned Systems Under Complex Constraints[J]. Journal of System Simulation, 2026, 38(3): 785-799.

图/表 18

图1

图2

图3

图4

表1

表2

表3

表4

表5

任务列表

任务ID	目标ID	时间窗/s	协同打击批次
R1	T1	$2 008, + ∞$	0
R2	T2	(0, 3 474]	0
A1	T3	[3 491, 4 687]	0
E1	T3	[3 491, 4 687]	0
A2	T4	$0, + ∞$	0
E2	T4	$0, + ∞$	0
A3	T5	[2 448, 3 940]	1
A4	T6	[2 448, 3 940]	1
A5	T7	[2 448, 3 940]	1
A6	T8	[2 448, 3 940]	1
A7	T9	[2 448, 3 940]	1
E3	T5~T9	[2 448, 3 940]	0
A8	T10	(0, 3 805]	2
E4	T10	(0, 3 805]	0
A9	T11	(0, 3 550]	2
E5	T11	(0, 3 550]	0
A10	T12	(0, 3 729]	2
E6	T12	(0, 3 729]	0

表5

表6

表7

Fig. 5

Fig. 6

图7

图8

表8

图9

图10

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类型	最小速度/(m/s)	巡航速度/(m/s)	最大速度/(m/s)	巡航高度/m	最小转弯半径/m	最大航程/km	出动成本	有效探测半径/m	弹药配置
UAV_A	30	75	100	5 000	600	500	5 000	3 711.5	WP_A×4, WP_B×2
UAV_B	25	63	90	5 000	500	300	4 000	3 711.5	WP_B×6
USV_L	3	15.4	22	0	100	600	3 000	10 456	WP_C×4, WP_D×4
USV_S	3	19.6	22	0	50	400	1 500	10 456	WP_D×6

类型	平均速度/(m/s)	最大射程/km	最小射程/km	毁伤能力	使用成本
WP_A	374	8	2	120	1×10⁵
WP_B	205	8	2	80	3×10⁴
WP_C	374	25	2	150	1.5×10⁵
WP_D	274	15	3	120	1×10⁵

序号	类型	初始位置/km
1	UAV_A	(5,3,5)
2	UAV_A	(5,2.5,5)
3	UAV_B	(5,35,5)
4	UAV_B	(5,2,5)
5	USV_L	(30,35,0)
6	USV_L	(30,25,0)
7	USV_S	(17.5,20,0)
8	USV_S	(17.5,40,0)

无人系统	执行序列	出发时间/s	总用时/s	总航程/m
U3	A5→A7→A4→A10→E3→A1	1 545.5	4 646.7	166 584.2
U4	A8→E4→E2	1 789.3	3 823.5	128 155.6
U5	R2→A9→E5→E6→E1→R1	0	6 409.8	90 349.8
U6	A2	0	2 449.0	37 713.9
U7	A6→A3	0	5 078.6	89 383.8

无人系统	任务ID	执行点位置/m	执行点航向/(°)	弹药	执行时间/s	打击命中时间/s	等待时间/s	累计航程/m
U3	A5	(677 21.9, 31 506.7, 5 000.0)	356.8	WP_B	2 542.6	2 588.5	0	62 819.1
	A7	(67 847.7, 31 499.7, 5 000.0)	356.8	WP_B	2 544.6	2 590.0	0	62 945.1
	A4	(67 973.5, 31 492.7, 5 000.0)	356.8	WP_B	2 546.6	2 591.5	0	63 071.1
	A10	(73 402.5, 35 335.6, 5 000.0)	34.0	WP_B	2 687.9	2 733.4	0	69 744.9
	E3	(73 946.5, 34 327.9, 5 000.0)	298.36	—	2 740.7	—	0	73 066.2
	A1	(76 956.9, 37 643.9, 5 000.0)	47.8	WP_B	3 491.0	3 536.9	569.2	93 772.5
U4	A8	(61 046.4, 12 043.7, 5 000.0)	351.9	WP_B	2 687.9	2 733.4	0	56 608.3
	E4	(65 292.3, 11 441.0, 5 000.0)	351.9	—	2 755.9	—	0	60 896.8
	E2	(64 166.0, 18 766.9, 5 000.0)	98.7	—	2 881.0	—	0	68 778.8
U5	R2	(71 500.0, 35 000.0, 0.0)	0.0	—	2 694.8	—	0	41 500.0
	A9	(71 500.0, 35 000.0, 0.0)	0.0	WP_D	2 694.8	2 733.4	0	41 500.0
	E5	(71 444.7, 34 884.4, 0.0)	244.4	—	2 740.0	—	0	42 196.4
	E6	(71 444.7, 34 884.4, 0.0)	244.4	—	2 740.0	—	0	42 196.4
	E1	(74 000.0, 37 500.0, 0.0)	45.7	—	3 536.9	—	0	46 106.5
	R1	(74 000.0, 37 500.0, 0.0)	45.7	—	3 536.9	—	0	46 106.5
U6	A2	(48 645.6, 23 576.9, 0.0)	355.6	WP_D	1 214.3	1 269.0	0	18 699.8
U7	A6	(60 709.6, 31 000.9, 0.0)	0.2	WP_D	2 533.8	2 588.5	0	44 594.3
U7	A3	(60 744.8, 31 001.0, 0.0)	0.2	WP_D	2 535.8	2 590.4	0	44 629.5

复杂约束条件下无人系统跨域协同作战任务规划方法

Task Planning Method for Cross-domain Cooperative Combat Operations of Unmanned Systems Under Complex Constraints

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参考文献 32

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目标ID	位置/m	类型	生命值
T1	(73 685.3, 29 748.3, 13.0)	1	0
T2	(79 723.4, 28 629.4, 90.9)	1	0
T3	(82 334.3, 43 567.0, 46.8)	2	50
T4	(63 602.1, 22 435.4, 13.2)	2	100
T5	(75 662.9, 30 985.4, 37.8)	3	90
T6	(75 697.5, 31 018.3, 37.9)	3	30
T7	(75 708.5, 31 064.1, 39.1)	3	50
T8	(75 721.8, 31 109.3, 40.4)	3	100
T9	(75 756.9, 31 132.0, 40.7)	3	50
T10	(68 967.0, 10 919.3, 171.9)	4	30
T11	(66 932.8, 25 451.9, 19.6)	4	30
T12	(80 036.3, 39 806.9, 204.2)	4	50

算法名称	适应度最小值	适应度最大值	适应度中位数	求解成功率
DBO	0.173	0.211	0.192	1
STBO	0.178	0.209	0.195	1
CEO	0.169	0.190	0.181	1
SOGWO	0.162	100	0.206	0.7
MELGWO	0.147	0.195	0.175	1
MSEGWO	0.146	0.181	0.158	1

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