一种多无人机集群持续侦察分层控制框架及关键算法  

A Hierarchical Control Framework and Key Algorithms of Multi-Swarm Persistent Surveillance

作　　者：王涛[1] 王维平[1] 李小波 井田 Wang Tao;Wang Weiping;Li Xiaobo;Jing Tian(School of System Engineering, National University of Defense Technology, Changsha 410073, China)

机构地区：[1]国防科技大学系统工程学院,湖南长沙410073

出　　处：《系统仿真学报》2018年第4期1221-1228,共8页Journal of System Simulation

基　　金：国家自然科学基金(61273198)

摘　　要：持续侦察作为多无人机集群的一种典型应用模式,持续侦察过程中无人机集群的动态部署,尤其是时敏环境下的自适应调整一直是该领域研究的难点问题,本文聚焦于此,提出了一种多无人机集群持续侦察的分层控制框架及其关键算法。该框架将时敏目标特征和集群侦察效果用一种可演化、可交互的数字草皮人工势场表征;将各栅格的数字草皮势函数作为数据点权重,设计了一种基于栅格的加权动态数据聚类方法,自适应调整无人机子群辖区和子群无人机数量。案例研究表明,该方法能够有效提升多无人机集群的侦察效率和工作载荷均衡度。Persistent surveillance is a typical application of multi-swarm aerial vehicle systems （UAVs）. And dynamic deployment for multi-swarm UAVs in persistent surveillance has been proved to be a complex problem, especially when the self-adjustment is required to adapt the time-sensitive environment. This paper proposes a multi-swarm hierarchical control scheme and key algorithms. We design the digital turf potential field model to approximate the evolving and interactive information of time-sensitive target features and surveillance effects. Moreover, using the digital turf potential function of each grid as the data point weight, we design a grid-based weighted data-clustering algorithm for the dynamic assignment of UA V swarms, which can adaptively adjust the number of UA Vs in each swarm and its sub-region. Finally, we evaluate the proposed architecture by means of case studies and find that our method can promote surveillance efficiency and workload balance of multiple UAV swarms.

关 键 词：多无人机集群 持续侦察 动态部署 数字草皮模型 数据聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]