ATD-DBO驱动的无人机在不规则区域的渗透路径规划  

Penetration Path Planning of UAV Driven by ATD-DBO in Irregular Areas

作　　者：袁晓飞 白梅娟 王智慧 尹茂振 侯帅[1] 周敏敏 YUAN Xiao-fei;BAI Mei-juan;WANG Zhi-hui;YIN Mao-zhen;HOU Shuai;ZHOU Min-min(School of Information and Electrical Engineering,Hebei University of Engineering,Handan 056038,China;Yuanguang Energy Internet Industry Development(Hengqin)Co.,Ltd.,Beijing 100176,China)

机构地区：[1]河北工程大学信息与电气工程学院,河北邯郸056038 [2]远光能源互联网产业发展(横琴)有限公司,北京100176

出　　处：《电脑与信息技术》2024年第4期36-40,共5页Computer and Information Technology

基　　金：河北省自然科学基金面上项目(项目编号:F2021402009、A2020402013)。

摘　　要：城市无人机渗透作战中使用智能无人机执行隐蔽穿插、渗入和目标定位等任务,但其面临着城市环境复杂、空域限制等挑战。为了解决城市渗透背景下无人机的路径规划难题,提出了一种ATD-DBO(Adaptive T Distribution-Dung Beetle Optimizer)驱动的无人机在不规则区域的渗透路径规划算法。首先,提出融合城市建筑物分布、岗哨位置以及无人机特性的无人机城市渗透模型。其次,提出了虫口混沌映射初始化种群、自适应t分布和动态变异策略扰动蜣螂位置和将非精英个体进行二次变异的ATD-DBO算法。最后,提出了一种融合城市实战不规则区域场景和打击意图的快速突进模型。实验证明,算法规划出的路径在有效避开岗哨位置的同时能够确保路径较短。In the penetration operation of urban UAVs,intelligent UAVs are used for covert interpenetration,penetration and target positioning,but they are faced with challenges such as complex urban environment and airspace restrictions.In order to solve the path planning problem of UAV under the background of urban penetration,an ATD-DBO(Adaptive T Distribution-Dung Beetle Optimizer) driven penetration path planning algorithm for UAV in irregular areas is proposed.Firstly,a UAV urban penetration model integrating urban building distribution,sentry position and UAV characteristics is proposed.Secondly,an ATD-DBO algorithm is proposed to initialize the population by the chaotic map of the insect population,the adaptive t distribution and the dynamic mutation strategy to disturb the position of the dung beetle and the secondary mutation of the non-elite individuals.Finally,a fast advance model integrating urban actual irregular area scene and attack intention is proposed.Experiments show that the path planned by the algorithm can effectively avoid the sentry position and ensure that the path is short.

关 键 词：城市渗透模型 不规则突进区域 蜣螂优化算法 优选策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] V279[自动化与计算机技术—控制科学与工程]