基于搜索意图交互的无人机集群协同搜索算法

王宁; 李哲; 梁晓龙; 侯岳奇; 吴傲

doi:10.13700/j.bh.1001-5965.2020.0602

基于搜索意图交互的无人机集群协同搜索算法

doi: 10.13700/j.bh.1001-5965.2020.0602

王宁^{1, 2},
李哲^{1, 2, ,},
梁晓龙^{1, 2},
侯岳奇^{1, 2},
吴傲^{1, 2}

1.
空军工程大学空管领航学院, 西安 710051
2.
陕西省电子信息系统综合集成重点实验室, 西安 710051

基金项目:

国家自然科学基金 61703427

国防创新特区项目

“十三五”装备预研共用技术项目

详细信息

通讯作者:
李哲, E-mail: kongyanshi@126.com

中图分类号: V279⁺.3;V249.122
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- 文章访问数: 360
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- PDF下载量: 77
- 被引次数: 0
出版历程
- 收稿日期: 2020-10-26
- 录用日期: 2021-01-15
- 网络出版日期: 2022-03-20

Cooperative search algorithm for UAV swarm based on search intention interaction

WANG Ning^{1, 2},
LI Zhe^{1, 2
, ,},
LIANG Xiaolong^{1, 2},
HOU Yueqi^{1, 2},
WU Ao^{1, 2}

1.
Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China
2.
Shaanxi Province Lab. of Meta-synthesis for Electronic & Information System, Xi'an 710051, China

Funds:

National Natural Science Foundation of China 61703427

National Defense Innovation Special Zone Project

"13th Five Year Plan"Equipment Pre-research Sharing Technology Project

More Information

Corresponding author: LI Zhe, E-mail: kongyanshi@126.com

摘要

摘要:
针对无先验信息条件下无人机集群的协同搜索问题，提出一种以覆盖率为引导，以机间安全距离、通信距离、偏航角调整及搜索边界等为约束的无人机集群协同搜索算法。通过建立环境地图矩阵对任务区域进行描述，进一步定义环境地图更新算子实现搜索过程中环境地图的快速更新。设计了集群协同搜索任务的回报函数，采用粒子群算法进行求解，得到每架无人机在已知环境地图下的最优决策，即决策意图。每架无人机在获取其他成员决策意图的基础上重新进行决策，实现协同决策。针对不同规模集群提出了集中式和分布式2种协同决策方案。仿真结果表明，所提算法能够对存在未知威胁的不规则任务区域进行有效覆盖搜索，覆盖率远高于不进行协同决策的个体决策方法。
- 意图交互 /
- 无人机集群 /
- 协同搜索 /
- 模型预测控制 /
- 滚动时域优化决策
Abstract:
Aimed at the cooperative search problem of UAV swarm without prior information, a cooperative search algorithm of UAV swarm is proposed, which is guided by coverage rate and constrained by safe distance, communication distance, yaw angle adjustment and search boundary. The task area is described by establishing the environmental map matrix, and the environmental map update operator is further defined to realize the rapid update of the environmental map in the search process. The return function of swarm cooperative search task is designed, and particle swarm optimization algorithm is used to solve the problem in order to obtain the optimal decision of each UAV under the known environment map, namely the decision intention. Each UAV makes decisions again based on acquiring the decision-making intentions of other members to achieve cooperative decision-making. Two cooperative decision-making schemes, centralized and distributed, are proposed for swarms with different scales. The simulation results show that the proposed algorithm can effectively search the irregular task area with unknown threat, and the coverage is much higher than that of the individual decision method without cooperative decision.
- intention interaction /
- UAV swarm /
- cooperative search /
- model predictive control /
- rolling horizon optimization decision

HTML全文

图 1 UAV集群协同搜索示意图

Figure 1. Schematic diagram of UAV swarm collaborative search

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图 2 任务区域栅格化

Figure 2. Rasterized task area

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图 3 UAV_i航路预测图

Figure 3. UAV_i route forecast chart

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图 4 UAV探测区域栅格化

Figure 4. Rasterized UAV detection area

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图 5 环境地图矩阵更新流程

Figure 5. Update process of environment map matrix

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图 6 决策环境信息矩阵产生流程

Figure 6. Decision environment information matrix generation process

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图 7 UAV集群环境地图矩阵更新流程

Figure 7. UAV swarm enviornment map update process

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图 8 集中式意图交互

Figure 8. Centralized intention interaction

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图 9 分布式意图交互

Figure 9. Distributed intention interaction

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图 10 三种搜索算法仿真结果

Figure 10. Simulation results of three search algorithms

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图 11 三种搜索算法覆盖率变化曲线

Figure 11. Coverage change curves of three search algorithms

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图 12 10架UAV集群协同搜索仿真结果

Figure 12. Collaborative search simulation results for swarm with 10 UAVs

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图 13 集群中机间最小距离曲线

Figure 13. Curve of minimum distance between UAVs of swarm

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图 14 覆盖率变化曲线

Figure 14. Change curve of coverage

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表 1 环境地图融合算子运算规则

Table 1. Environmental map fusion operator operation rules

g_i(k)	g_j(k)
g_i(k)	0	-1	+1
0	0	0	0
-1	0	-1	+1
+1	0	+1	+1

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表 2 算法参数

Table 2. Algorithm parameters

参数	ω₁	ω₂	ω₃	ω₄	ω₅	d_s/m	d_c/m
数值	0.3	0.2	0.2	0.1	0.2	20	500

下载: 导出CSV

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