基于信息素决策的无人机集群协同搜索算法

吴傲; 杨任农; 梁晓龙; 侯岳奇

doi:10.13700/j.bh.1001-5965.2020.0026

基于信息素决策的无人机集群协同搜索算法

doi: 10.13700/j.bh.1001-5965.2020.0026

吴傲^{1, 2},
杨任农³,
梁晓龙^{1, 2, ,},
侯岳奇^{1, 2}

1.
空军工程大学航空集群技术与作战运用实验室, 西安 710051
2.
空军工程大学国家空管防相撞技术重点实验室, 西安 710051
3.
空军工程大学空管领航学院, 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

作者简介:
吴傲  男, 硕士研究生。主要研究方向: 航空集群理论与技术、自主空战决策

杨任农  男, 博士, 教授, 博士生导师。主要研究方向: 任务规划、自主空战

梁晓龙  男, 博士, 教授, 博士生导师。主要研究方向: 航空集群理论与技术、空管智能化、系统建模与仿真

侯岳奇  男, 博士研究生。主要研究方向: 航空集群智能决策

通讯作者:
梁晓龙, E-mail: afeu_lxl@sina.com

中图分类号: V279⁺.3;V249.122
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- 被引次数: 0
出版历程
- 收稿日期: 2020-01-16
- 录用日期: 2020-04-17
- 网络出版日期: 2021-04-20

Cooperative search algorithm based on pheromone decision for UAV swarm

WU Ao^{1, 2},
YANG Rennong³,
LIANG Xiaolong^{1, 2
, ,},
HOU Yueqi^{1, 2}

1.
Aviation Swarm Technology and Operational Application Laboratory, Air Force Engineering University, Xi'an 710051, China
2.
National Key Laboratory of Air Traffic Collision Prevention, Air Force Engineering University, Xi'an 710051, China
3.
Air Traffic Control and Navigation College, Air Force Engineering University, 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: LIANG Xiaolong, E-mail: afeu_lxl@sina.com

摘要

摘要:
针对无人机(UAV)集群在未知环境中无先验信息条件下的搜索问题，提出了一种以信息素为决策机制的无人机集群协同搜索算法。首先，考虑无人机通信约束，建立了有外部节点的星型网络通信和无外部节点的自组织网络通信2种形式的搜索模型。其次，通过环境地图向信息素地图映射的方法建立任务环境模型。将任务过程分为3个阶段，在搜索阶段，无人机通过不断地移动实现本机信息素地图的更新；在通信阶段，通过通信网络实现多机信息素地图的融合；在决策阶段，根据局部信息和全局信息做出决策，并将栅格信息素浓度作为决策函数来引导无人机的位置更新。基于信息素地图覆盖率来定量描述搜索效果。最后，仿真结果表明，所提算法能够对区域进行覆盖搜索，表现为搜索效率高、抗毁性强、受集群的初始位置影响小。
- 信息素 /
- 未知环境 /
- 无人机(UAV)集群 /
- 协同搜索 /
- 搜索覆盖率
Abstract:
To solve the problem of Unmanned Aerial Vehicle (UAV) swarm search in unknown environment without prior information, this paper proposes a UAV swarm cooperative search algorithm with pheromone as decision mechanism. Firstly, considering the communication constraints of UAV, two search models which are star communication network with external nodes and self-organizing communication network without external nodes are established. Secondly, the task environment model is established by mapping environment map to pheromone map. In this paper, the task process is divided into three stages. In the search stage, the UAV can update the local pheromone map by moving constantly. In the communication stage, the fusion of UAV swarm pheromone maps is realized through the communication network. In the decision-making stage, the decision is made based on the local information and the global information, and the grid pheromone concentration is taken as the decision function to guide the position update of the UAV. Based on pheromone map coverage rate, the search results are quantitatively described. Finally, the simulation results show that the method proposed in this paper can search and cover the region, which is characterized by high search efficiency, strong destruction resistance and little influence by the initial location of the swarm.
- pheromone /
- unknown environment /
- Unmanned Aerial Vehicle (UAV) swarm /
- cooperative search /
- search coverage rate

HTML全文

图 1 无人机集群协同搜索任务示意图

Figure 1. Schematic diagram of UAV swarm cooperative search task

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图 2 无人机集群及信息素地图任务状态

Figure 2. Task status of UAV swarm and pheromone map

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图 3 无人机集群协同搜索任务流程

Figure 3. Cooperative search task process of UAV swarm

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图 4 环境地图

Figure 4. Environment map

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图 5 无人机运动模型

Figure 5. Motion model of UAV

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图 6 有外部节点的星型网络通信示意图

Figure 6. Schematic diagram of star network communication with external nodes

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图 7 无外部节点的自组织网络通信示意图

Figure 7. Schematic diagram of self-organizing network communication without external nodes

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图 8 环境地图向信息素地图的映射

Figure 8. Mapping of environment map to pheromone map

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图 9 星型网络通信流程

Figure 9. Process of star network communication

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图 10 自组织网络通信流程

Figure 10. Process of self-organizing network communication

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图 11 提取局部信息素地图

Figure 11. Extracting local pheromone map

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图 12 提取全局信息素地图

Figure 12. Extracting global pheromone map

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图 13 搜索环境初始化

Figure 13. Search environment initialization

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图 14 信息素地图初始化

Figure 14. Pheromone map initialization

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图 15 星型网络通信模式下搜索效果图

Figure 15. Search rendering under star network communication

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图 16 自组织网络通信模式下搜索效果图

Figure 16. Search rendering under self-organizing network communication

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图 17 星型网络通信和自组织网络通信下覆盖率随集群密度变化曲线

Figure 17. Curves of coverage rate changing with swarm's UAV density under star network communication and self-organizing network communication

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图 18 覆盖率随搜索时间变化曲线

Figure 18. Curves of coverage rate changing with search time

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图 19 不同无人机数目下搜索效率随占空比的变化

Figure 19. Curves of search efficiency rate changing with duty cycle for different swarm sizes

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图 20 不同无人机数目最佳搜索占空比下的实时覆盖率变化曲线

Figure 20. Real-time changing curves of coverage rate in the best search duty cycle for different swarm sizes

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图 21 无故障和有故障时覆盖率实时变化曲线

Figure 21. Real-time changing curves of coverage rate with and without fault

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图 22 有故障和无故障时无人机搜索轨迹

Figure 22. UAV search trajectory diagram with and without fault

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图 23 四种无人机集群初始位置

Figure 23. Four initial positions of UAV swarm

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图 24 四种初始位置下的搜索覆盖率

Figure 24. Search coverage rate in 4 initial positions

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表 1 无人机集群协同搜索原则

Table 1. Principle of UAV swarm cooperative search

条件	原则
搜索效率	覆盖率高重叠率低搜索时间短
搜索范围	不飞出搜索边界不进入非搜索区域
初始位置	搜索效率对集群编队的初始位置不敏感
抗毁性	搜索效率受单机故障影响小

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表 2 星型网络通信模式下搜索结果

Table 2. Search results under star network

决策方式	集群规模	T_s/steps	T_c/steps	覆盖率/%
随机决策	20	200	50	18.732 1
局部信息决策	20	200	50	70.145 8
全局信息决策	20	200	50	82.843 8

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表 3 自组织网络通信模式下搜索结果

Table 3. Search results under self-organizing network

决策方式	集群规模	覆盖率/%
随机决策	20	20.157 6
局部信息决策	20	65.458 3
全局信息决策	20	76.562 5

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