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

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

Abstract

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)

CLC Number:

TP391.9

Fang Haojie, Zhen Ziyang, Gong Huajun, Xie Xu, Luo Wei. 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.

Figures/Tables 18

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Table 2

Table 3

Table 4

Table 5

Table of task

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

Table 5

Table 6

Table 7

图5

图6

Fig. 7

Fig. 8

Table 8

Fig. 9

Fig. 10

References 32

[1]	王耀南, 安果维, 王传成, 等. 智能无人系统技术应用与发展趋势[J]. 中国舰船研究, 2022, 17(5): 9-26.
	Wang Yaonan, An Guowei, Wang Chuancheng, et al. Technology Application and Development Trend of Intelligent Unmanned System[J]. Chinese Journal of Ship Research, 2022, 17(5): 9-26.
[2]	邱志明, 孟祥尧, 马焱, 等. 海上无人系统发展及关键技术研究[J]. 中国工程科学, 2023, 25(3): 74-83.
	Qiu Zhiming, Meng Xiangyao, Ma Yan, et al. Development and Key Technologies of Maritime Unmanned Systems[J]. Strategic Study of CAE, 2023, 25(3): 74-83.
[3]	梁晓龙, 王宁, 王维佳, 等. 海上跨域无人集群研究进展综述[J]. 空军工程大学学报, 2023, 24(5): 1-15.
	Liang Xiaolong, Wang Ning, Wang Weijia, et al. Progress in Maritime Cross-domain Manned Swarms[J]. Journal of Air Force Engineering University, 2023, 24(5): 1-15.
[4]	军事科学院. 中国人民解放军军语[M]. 北京: 军事科学出版社, 2011.
	Academy of Military Sciences. Military Language of the People's Liberation Army of Chinese[M]. Beijing: Military Science Press, 2011.
[5]	张修社, 石静, 范文新. 协同作战系统工程导论[M]. 北京: 国防工业出版社, 2019.
	Zhang Xiushe, Shi Jing, Fan Wenxin. Introduction to Cooperative Engagement System Engineering[M]. Beijing: National Defense Industry Press, 2019.
[6]	李明哲, 马琼敏, 伍国华. 基于强化学习的无人机集群动态任务规划算法[J]. 系统仿真技术, 2023, 19(3): 193-204.
	Li Mingzhe, Ma Qiongmin, Wu Guohua. Dynamic Task Planning Algorithm for UAV Swarm Based on Reinforcement Learning[J]. System Simulation Technology, 2023, 19(3): 193-204.
[7]	张栋, 李林, 王孟阳, 等. 不确定环境下多无人机察打一体任务规划方法[J]. 北京理工大学学报, 2025, 45(2): 111-125.
	Zhang Dong, Li Lin, Wang Mengyang, et al. Methodology for Integrated Reconnaissance-strike Mission Planning of Multi-UAV Under Uncertain Environments[J]. Transactions of Beijing Institute of Technology, 2025, 45(2): 111-125.
[8]	陈超. 无人集群任务分配方法研究[D]. 长沙: 国防科技大学, 2021.
	Chen Chao. Research on Task Assignment Algorithms in Unmanned Swarm[D]. Changsha: National University of Defense Technology, 2021.
[9]	Xiong Tao, Liu Fang, Liu Haoting, et al. Multi-drone Optimal Mission Assignment and 3D Path Planning for Disaster Rescue[J]. Drones, 2023, 7(6): 394.
[10]	刘攀, 徐胜利, 张迪, 等. 基于粒子群优化的多导弹动态武器目标分配算法[J]. 南京航空航天大学学报, 2023, 55(1): 108-115.
	Liu Pan, Xu Shengli, Zhang Di, et al. Multi-missile Dynamic Weapon Target Assignment Algorithm Based on Particle Swarm Optimization[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2023, 55(1): 108-115.
[11]	王峰, 黄子路, 韩孟臣, 等. 基于KnCMPSO算法的异构无人机协同多任务分配[J]. 自动化学报, 2023, 49(2): 399-414.
	Wang Feng, Huang Zilu, Han Mengchen, et al. A Knee Point Based Coevolution Multi-objective Particle Swarm Optimization Algorithm for Heterogeneous UAV Cooperative Multi-task Allocation[J]. Acta Automatica Sinica, 2023, 49(2): 399-414.
[12]	张安, 杨咪, 毕文豪, 等. 基于多策略GWO算法的不确定环境下异构多无人机任务分配[J]. 航空学报, 2023, 44(8): 143-159.
	Zhang An, Yang Mi, Bi Wenhao, et al. Task Allocation of Heterogeneous Multi-UAVs in Uncertain Environment Based on Multi-strategy Integrated GWO[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(8): 143-159.
[13]	闫川, 甄子洋, 张继豪, 等. 突防飞行与多区域搜索一体化侦察航迹规划[J]. 飞行力学, 2023, 41(1): 20-26, 46.
	Yan Chuan, Zhen Ziyang, Zhang Jihao, et al. Integrated Reconnaissance Path Planning of Penetration Flight and Multi Area Search[J]. Flight Dynamics, 2023, 41(1): 20-26, 46.
[14]	Guo Cong, Huang Lei, Tian Kuo. Combinatorial Optimization for UAV Swarm Path Planning and Task Assignment in Multi-obstacle Battlefield Environment[J]. Applied Soft Computing, 2025, 171: 112773.
[15]	Liu Chenglou, Xie Fangfang, Ji Tingwei. Fixed-wing UAV Formation Path Planning Based on Formation Control: Theory and Application[J]. Aerospace, 2024, 11(1): 1.
[16]	Tong Xiaolong, Yu Shanen, Liu Guangyu, et al. A Hybrid Formation Path Planning Based on A* and Multi-target Improved Artificial Potential Field Algorithm in the 2D Random Environments[J]. Advanced Engineering Informatics, 2022, 54: 101755.
[17]	Zhang Jiadong, Ling Hongjie, Tang Zhihao, et al. Path Planning of USV in Confined Waters Based on Improved A* and DWA Fusion Algorithm[J]. Ocean Engineering, 2025, 322: 120475.
[18]	郑红星. 复杂任务/环境下的异构无人机集群全周期任务筹划与在线规划[D]. 哈尔滨: 哈尔滨工业大学, 2022.
	Zheng Hongxing. Full-cycle Mission Planning and Online Planning of Heterogeneous UAV Swarm Under Complex Missions/Environment[D]. Harbin: Harbin Institute of Technology, 2022.
[19]	徐杰, 吴蔚楠, 龚春林. 多无人机任务分配/航迹规划的一体化求解方法[J]. 宇航学报, 2023, 44(12): 1860-1870.
	Xu Jie, Wu Weinan, Gong Chunlin. Integrated Solution Method for Multi-UAV Task Assignment and Trajectory Planning[J]. Journal of Astronautics, 2023, 44(12): 1860-1870.
[20]	Dong Xili, Shi Chenguang, Wen Wen, et al. Multi-mission Oriented Joint Optimization of Task Assignment and Flight Path Planning for Heterogeneous UAV Cluster[J]. Remote Sensing, 2023, 15(22): 5315.
[21]	刘奇辉, 刘海颖, 黄魁华, 等. 基于CBBA的多域作战无人系统任务规划方法研究[J]. 火力与指挥控制, 2024, 49(2): 87-94.
	Liu Qihui, Liu Haiying, Huang Kuihua, et al. Research on Mission Planning Method of Multi-domain Operation Unmanned Systems Based on CBBA[J]. Fire Control & Command Control, 2024, 49(2): 87-94.
[22]	Harabor Daniel, Grastien Alban. Online Graph Pruning for Pathfinding on Grid Maps[C]//Proceedings of the Twenty-fifth AAAI Conference on Artificial Intelligence and the Twenty-third Conference on Innovative Applications of Artificial Intelligence and the Second Symposium on Educational Advances in Artificial Intelligence. Palo Alto: AAAI Press, 2011: 1114-1119.
[23]	Mirjalili Seyedali, Seyed Mohammad Mirjalili, Lewis Andrew. Grey Wolf Optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[24]	Chen Yongbo, Yang Di, Yu Jianqiao. Multi-UAV Task Assignment with Parameter and Time-Sensitive Uncertainties Using Modified Two-part Wolf Pack Search Algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(6): 2853-2872.
[25]	张瑞鹏, 冯彦翔, 杨宜康. 多无人机协同任务分配混合粒子群算法[J]. 航空学报, 2022, 43(12): 412-427.
	Zhang Ruipeng, Feng Yanxiang, Yang Yikang. Hybrid Particle Swarm Algorithm for Multi-UAV Cooperative Task Allocation[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(12): 412-427.
[26]	Zhou Xiaojun, Shi Peng, Lim C C, et al. A Dynamic State Transition Algorithm with Application to Sensor Network Localization[J]. Neurocomputing, 2018, 273: 237-250.
[27]	Tanabe Ryoji, Fukunaga Alex S. Improving the Search Performance of SHADE Using Linear Population Size Reduction[C]//2014 IEEE Congress on Evolutionary Computation (CEC). Piscataway: IEEE, 2014: 1658-1665.
[28]	Dong Yingchao, Zhang Shaohua, Zhang Hongli, et al. Chaotic Evolution Optimization: A Novel Metaheuristic Algorithm Inspired by Chaotic Dynamics[J]. Chaos Solitons & Fractals, 2025, 192: 116049.
[29]	Xue Jiankai, Shen Bo. Dung Beetle Optimizer: A New Meta-heuristic Algorithm for Global Optimization[J]. The Journal of Supercomputing, 2023, 79(7): 7305-7336.
[30]	Dehghani Mohammad, Trojovská Eva, Zuščák Tomáš. A New Human-inspired Metaheuristic Algorithm for Solving Optimization Problems Based on Mimicking Sewing Training[J]. Scientific Reports, 2022, 12(1): 17387.
[31]	Dhargupta Souvik, Ghosh Manosij, Mirjalili Seyedali, et al. Selective Opposition based Grey Wolf Optimization[J]. Expert Systems with Applications, 2020, 151: 113389.
[32]	Ahmed Rasel, Gade Pandu Rangaiah, Mahadzir Shuhaimi, et al. Memory, Evolutionary Operator, and Local Search Based Improved Grey Wolf Optimizer with Linear Population Size Reduction Technique[J]. Knowledge-based Systems, 2023, 264: 110297.

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