一种中继无人机快速部署策略

张小孟; 杨森; 宋晓; 胡永江; 李文广

doi:10.13700/j.bh.1001-5965.2020.0249

一种中继无人机快速部署策略

doi: 10.13700/j.bh.1001-5965.2020.0249

张小孟^{1, 2, 3},
杨森^{1, 2, ,},
宋晓²,
胡永江¹,
李文广¹

1.
陆军工程大学无人机工程系, 石家庄 050003
2.
北京航空航天大学网络空间安全学院, 北京 100083
3.
中国人民解放军31700部队, 辽阳 111000

基金项目:

陆军工程大学石家庄校区科研创新发展基金 School Education (2019) No. 71

详细信息

通讯作者:
杨森. E-mail: 568657132@qq.com

中图分类号: V279
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- 被引次数: 0
出版历程
- 收稿日期: 2020-06-07
- 录用日期: 2020-08-01
- 网络出版日期: 2021-08-20

A rapid deployment strategy of relay unmanned aerial vehicle

ZHANG Xiaomeng^{1, 2, 3},
YANG Sen^{1, 2
, ,},
SONG Xiao²,
HU Yongjiang¹,
LI Wenguang¹

1.
Department of Unmanned Aerial Vehicle Engineering, Army Engineering University, Shijiazhuang 050003, China
2.
School of Cyber Science and Technology, Beihang University, Beijing 100083, China
3.
Army of 31700 of PLA, Liaoyang 111000, China

Funds:

Scientific Research and Innovation Development Fund of Shijiazhuang Campus of Army Engineering University School Education (2019) No. 71

More Information

Corresponding author: YANG Sen. E-mail: 568657132@qq.com

摘要

摘要:
针对任务规划中中继无人机部署效率低，部署方案无法满足最少数量要求等问题，提出了一种中继无人机快速部署策略。首先，根据最少中继节点的任务要求，建立了基于最少中继节点的部署模型。其次，优化了深度优先搜索算法的搜索方式，实现了节点间可行链路的快速搜索。最后，在人工蜂群(ABC)算法中引入快速深度优先搜索(DFS)算法，来求解最少中继节点部署方案。仿真结果表明：在相同任务规模下，所提策略的求解速度相较于改进前提高了53.56%左右，部署的中继无人机数量相较于现有方法减小了11.88%左右。
- 中继无人机 /
- 部署方案 /
- 深度优先 /
- 最少节点 /
- 人工蜂群(ABC)
Abstract:
In order to solve the problems in mission planning, such as the low deployment efficiency of relay Unmanned Aerial Vehicle (UAV) and the deployment scheme cannot meet the minimum number requirements, a fast relay UAV deployment strategy is proposed. First, according to the task requirements of the least relay nodes, a deployment model based on the least relay nodes is established. Then, the search mode of the depth-first search algorithm is optimized, and the fast search of feasible links between nodes is realized. Finally, the Rapid Depth-First Search (RDFS) algorithm is introduced into the Artificial Bee Colony (ABC) algorithm to solve the deployment scheme of the least relay nodes. The simulation results show that under the same task scale, the solution speed of this strategy is about 53.56% higher than that before improvement, and the number of deployed relay UAVs is reduced by about 11.88% compared with the existing methods.
- relay UAV /
- deployment scheme /
- depth first /
- minimum nodes /
- Artificial Bee Colony (ABC)

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图 1 变异算子操作

Figure 1. Mutation operator operation

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图 2 RDFS-ABC算法流程

Figure 2. RDFS-ABC algorithm flowchart

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图 3 中继节点部署图

Figure 3. Deployment diagram of relay nodes

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图 4 运行时间对比图

Figure 4. Run time comparison chart

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图 5 PSO算法中继节点部署图

Figure 5. Relay node deployment diagram with PSO algorithm

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图 6 RDFS-ABC算法中继节点部署图

Figure 6. Relay node deployment diagram with RDFS-ABC algorithm

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图 7 AHOP算法中继节点部署图

Figure 7. Relay node deployment diagram with AHOP algorithm

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表 1 实验参数设置

Table 1. Experimental parameter setting

参数	数值
有效通信距离d₀/m	500
安全距离d_sf/m	20
测控系统位置g(x, y)/m	(350, 300)
种群规模N_P	100
蜜源维度D	28
最大搜索次数l	5
最大迭代次数c	60

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表 2 任务节点位置坐标

Table 2. Task node location coordinates

目标	位置(x, y)/m	目标	位置(x, y)/m
1	(1 150, 1 176)	16	(1 280, 1 200)
2	(630, 1 660)	17	(230, 590)
3	(40, 2 090)	18	(460, 860)
4	(750, 1 100)	19	(1 040, 950)
5	(750, 2 030)	20	(590, 1 390)
6	(1 030, 2 070)	21	(830, 1 770)
7	(1 650, 650)	22	(490, 500)
8	(1 490, 1 630)	23	(1 840, 1 240)
9	(790, 2 260)	24	(1 260, 1 500)
10	(710, 1 310)	25	(1 280, 790)
11	(840, 550)	26	(490, 2 130)
12	(1 170, 2 300)	27	(1 460, 1 420)
13	(970, 1 340)	28	(1 260, 1 910)
14	(510, 700)	29	(360, 1 980)
15	(750, 900)	30	(110, 900)

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表 3 两种算法运行的平均时间

Table 3. Average schedule of two algorithms

目标数量	平均时间/s		降低比例/%
目标数量	RDFS-ABC算法	DFS-ABC算法	降低比例/%
5	5.59	13.05	57.16
10	13.31	27.19	51.05
15	18.12	40.07	54.78
20	21.62	47.10	54.10
25	26.67	57.09	53.28
30	33.56	68.43	50.96

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表 4 中继节点平均数量

Table 4. Average number of relay nodes

算法	中继节点个数	增加率/%
RDFS-ABC	7.05	0
PSO	8.34	15.47
AHOP	8.00	11.88

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