北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (4): 814-827.doi: 10.13700/j.bh.1001-5965.2020.0026

• 论文 • 上一篇    下一篇

基于信息素决策的无人机集群协同搜索算法

吴傲1,2, 杨任农3, 梁晓龙1,2, 侯岳奇1,2   

  1. 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
  • 收稿日期:2020-01-16 发布日期:2021-04-30
  • 通讯作者: 梁晓龙 E-mail:afeu_lxl@sina.com
  • 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。
  • 基金资助:
    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

Cooperative search algorithm based on pheromone decision for UAV swarm

WU Ao1,2, YANG Rennong3, LIANG Xiaolong1,2, HOU Yueqi1,2   

  1. 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
  • Received:2020-01-16 Published:2021-04-30
  • Supported by:
    National Natural Science Foundation of China (61703427); National Defense Innovation Special Zone Project; “13th Five Year Plan” Equipment Pre-Research Sharing Technology Project

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。

关键词: 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.

Key words: pheromone, unknown environment, Unmanned Aerial Vehicle (UAV) swarm, cooperative search, search coverage rate

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