基于秃鹰搜索算法优化的三维多无人机低空突防  

Optimization of 3D multi-UAVs low altitude penetration based on bald eagle search algorithm

作　　者：温夏露 黄鹤[1,2] 王会峰[2] 杨澜[1] 高涛[3] WEN Xialu;HUANG He;WANG Huifeng;YANG Lan;GAO Tao(Xi’an Key Laboratory of Intelligent Expressway Information Fusion and Control,Chang’an University,Xi’an 710064,China;School of Electronics and Control Engineering,Chang’an University,Xi’an 710064,China;School of Data Science and Artificial Intelligence,Chang’an University,Xi’an 710064,China)

机构地区：[1]长安大学西安市智慧高速公路信息融合与控制重点实验室,陕西西安710064 [2]长安大学电子与控制工程学院,陕西西安710064 [3]长安大学数据科学与人工智能研究院,陕西西安710064

出　　处：《浙江大学学报（工学版）》2024年第10期2020-2030,共11页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(52172324,52172379);中央高校基本科研业务费专项资金重点科研平台建设计划水平提升项目(300102324501);西安市智慧高速公路信息融合与控制重点实验室(长安大学)开放基金资助项目(300102323502).

摘　　要：三维空间环境复杂,多无人机的低空突防航迹规划计算量大,现有多目标秃鹰搜索算法存在求解易趋于中心点及精度低的缺陷,为此提出基于改进多目标秃鹰搜索算法(IMBES)的三维多无人机低空突防方法.构建三维环境模型、威胁源模型、无人机物理约束模型、多无人机协同约束模型以及路径平滑度约束模型,确定多目标代价函数.设计耦合混沌映射初始化,有效提高初始化种群质量;设计基于“侦察鹰”的自适应高斯游走策略,平衡开发与搜索能力;引入快速非支配排序,进一步提高算法效率.利用秃鹰位置与无人机速度、转弯角度、爬升角度的对应关系,采用IMBES高效搜索无人机构型空间,找到最优的帕累托前沿.实验结果表明,IMBES的成功率为70.5%.与现有路径规划方法相比,所提方法的优化能力强、能耗低,适用于多无人机协同低空突防.In response to the complex three-dimensional space environment and the high computational complexity of low altitude penetration path planning for multi-UAVs,the existing multi-objective bald eagle search algorithm has the shortcomings of easily approaching the center point and low accuracy.A 3D multi-UAVs low altitude penetration method based on the improved multi-objective bald eagle search algorithm(IMBES)was proposed.Models for the 3D environment,threat sources,UAV physical constraints,multi-UAVs cooperative constraints,and path smoothness were constructed to define a multi-objective cost function.A coupling chaotic mapping initialization was designed to enhance the quality of the initial population.An adaptive Gauss walk strategy based on the“scout eagle”was devised to balance development and search capabilities.Fast non-dominated sorting was introduced to further enhance algorithm efficiency.By leveraging the correspondence between the bald eagle position and UAV speed,turning angle,and climbing angle,the IMBES efficiently explored the UAV configuration space to identify the optimal Pareto front.Experimental results showed that the success rate of the IMBES was 70.5%.Compared with existing path planning methods,the proposed method demonstrates strong optimization capabilities and low energy consumption,making it suitable for collaborative low-altitude penetration by multiple UAVs.

关 键 词：多无人机 低空突防 自主避障 多目标算法 秃鹰搜索算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]