面向区域搜索的车载多无人机协同任务规划方法

doi:10.12305/j.issn.1001-506X.2026.01.14

Abstract

Abstract:

To better achieve full coverage scanning of large-area, a multi-truck and multi-drone collaborative area search mode is proposed. A multi-truck and multi-drone collaborative area search model is constructed considering the distance limit of truck travel and the total number limit of drones. A three-stage optimization algorithm integrating annealing mechanism, tabu strategy, and adaptive large neighborhood search (ALNSAWPT) is designed to solve the problem through area partitioning, truck path planning, and drone path planning. In the first stage, a grid based area partitioning method is designed to divide a large-area into multiple search tasks. In the second stage, the search task is assigned to the truck and a path planning scheme for the truck is generated. Thus, the original problem is simplified as a collaborative area search problem involving multiple groups of multi-truck and multi-drone. In the third stage, search tasks are assigned to each drone on the truck and generate a search path planning scheme. The experimental results show that ALNSAWPT is significantly better than the other four compared algorithms, and the efficiency of the multi-truck and multi-drone collaborative area search mode is significantly better than that of the single-truck and multi-drone area search mode, proving the effectiveness of the proposed algorithm.

Key words: large-scale area search, collaborative multi-truck and multi-drone, three stage optimization algorithm, task-planning method

CLC Number:

V 19

Fangyu HONG, Qing YE, Lining ZHANG, Guohua WU. Vehicle-based multi-UAV cooperative task planning method for area search[J]. Systems Engineering and Electronics, 2026, 48(1): 144-156.

Figures/Tables 14

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References 33

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参数	设定值
无人机续航时间$ {T}_{{\mathrm{max}}} $/min	50
无人机扫描宽度$ DW $/m	100
最大网格搜索面积/km²	4
覆盖重叠率/%	20
无人机飞行速度$ {v}_{D} $/（km/h）	80
卡车行驶速度$ {v}_{G} $/（km/h）	40
机动航程$ {l}_{D} $/km	6
初始温度$ {T}_{s} $/℃	1000
终止温度$ {T}_{e} $/℃	10
降温速度$ {\mathrm{cr }}$/℃	0.97
等温过程最大迭代次数$ {h}_{{\mathrm{max}}} $	10
最大迭代次数$ {I}_{{\mathrm{max}}} $	100000
禁忌表长度$ {L}_{{\mathrm{max}}} $	50

算例	ISGDS	ALNSAWP	ALNSWPT	ALNS	BRKGA	ALNSAWT
算例1	3043	2952	2795	2948	2928	2932
算例2	4199	4003	3780	4056	3951	3977
算例3	5792	5484	5216	5616	5592	5512
算例4	7372	7018	6582	7149	7089	6984
算例5	9626	9279	8586	9378	9407	9126

多车多无人机情形	算例1	算例2	算例3	算例4	算例5
1车4无人机	12568	16909	22148	28506	34908
1车5无人机	11096	15097	19949	24680	31276
1车6无人机	10757	13876	18507	23710	28988
1车7无人机	10143	13107	17404	21556	27538
2车4无人机	6008	8204	11432	14523	18360
2车5无人机	5532	7562	9630	12340	15535
2车6无人机	5062	6504	9564	11205	14632
2车7无人机	4985	6236	8624	10550	13520
2车8无人机	4733	5984	8372	9398	12368
2车9无人机	4580	5831	8219	9245	12215
2车10无人机	4474	5725	8113	9139	12109
3车6无人机	3560	4625	6192	7904	9664
3车7无人机	3385	4369	5806	7180	9182
3车8无人机	3018	4004	5439	6815	8816
3车9无人机	2795	3780	5216	6582	8586
3车10无人机	2668	3654	5090	6552	8460

多车多无人机情形	算例1	算例2	算例3	算例4	算例5	平均提升度
2车4无人机	8.32	6.22	6.70	10.90	9.70	8.37
2车5无人机	8.12	8.14	6.20	6.90	12.30	8.33
2车6无人机	6.90	9.30	9.10	9.50	13.60	9.68
2车7无人机	10.08	9.62	8.90	10.10	11.20	9.98
2车8无人机	10.39	11.69	13.50	7.40	8.90	10.38
2车9无人机	6.94	10.72	11.30	11.80	10.10	10.17
2车10无人机	7.58	10.03	12.80	12.60	7.80	10.16
3车6无人机	8.08	9.76	5.90	8.70	7.60	8.01
3车7无人机	6.55	11.01	9.60	7.80	10.50	9.09
3车8无人机	9.62	10.48	7.80	15.30	14.50	11.54
3车9无人机	7.02	7.56	14.10	14.20	15.40	11.66
3车10无人机	8.59	11.48	10.20	13.50	6.90	10.13
平均提升度	8.17	9.98	9.95	10.71	10.80	9.92

算例	无人机数量	2辆卡车相比1辆卡车效率提升/%	3辆卡车相比2辆卡车效率提升/%
算例1	4	52.20	—
	5	50.14	—
	6	52.94	29.67
	7	50.85	32.10
算例2	4	51.48	—
	5	49.91	—
	6	53.13	28.89
	7	52.42	29.94
算例3	4	48.38	—
	5	51.73	—
	6	48.32	35.26
	7	50.45	32.68
算例4	4	49.05	—
	5	50.00	—
	6	52.74	29.46
	7	51.06	31.94
算例5	4	47.40	—
	5	50.33	—
	6	49.52	33.95
	7	50.90	32.09
均值		50.65	31.60

Vehicle-based multi-UAV cooperative task planning method for area search

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