改进凤头豪猪优化算法的无人机三维路径规划  

UAV 3D path planning with improved Crested Porcupine optimization algorithm

作　　者：徐光辉[1,2] 邓赟 王淑青 舒军[1,2] 肖义平[1,2] XU Guanghui;DENG Yun;WANG Shuqing;SHU Jun;XIAO Yiping(Hubei University Of Technology,School of Electrical and Electronic Engineering,Wuhan 430068,China;Hubei University Of Technology,Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System,Wuhan 430068,China)

机构地区：[1]湖北工业大学电气与电子工程学院,武汉430068 [2]湖北工业大学太阳能高效利用及储能运行控制湖北省重点实验室,武汉430068

出　　处：《中南民族大学学报(自然科学版)》2025年第2期237-244,共8页Journal of South-Central Minzu University(Natural Science Edition)

基　　金：国家自然科学基金资助项目(61603127)。

摘　　要：针对无人机路径规划问题,将其抽象为一个多约束优化模型,同时整合了禁飞区、地形特征等多种因素,建立了综合成本函数评价系统.引入一种基于凤头豪猪优化算法(CPO)的解决方案,指导无人机应对复杂场景,并借助非弹性碰撞原理刻画了对抗物理攻击的数学模型.通过在CEC2017基准函数集中对比差分进化(DE)、灰狼优化(GWO)以及其他优秀算法的性能,证实所提出的CPO算法在收敛速度和全局寻优能力上能够展现出优势,较少陷入局部最优解.同时通过真实DEM地图路径规划仿真验证CPO在实际应用中的高效性.The unmanned aerial vehicle(UAV)path planning problem by formulating it as a multi-constraint optimization model,integrating various considerations such as no-fly zones and terrain features,thereby establishing an integrated cost function evaluation system.The research introduces a solution based on the Crested Porcupine Optimization(CPO)algorithm,which guides UAV navigation through complex scenarios and employs the concept of non-elastic collision to mathematically model resistance against physical attacks.By comparing the performance of the proposed CPO algorithm against Differential Evolution(DE),Grey Wolf Optimizer(GWO),and other prominent algorithms within the benchmark functions of the CEC2017 competition,the study empirically demonstrates that CPO exhibits superior convergence speed and enhanced global search capability,reducing the likelihood of being trapped in local optima.Furthermore,practical efficacy of the CPO algorithm is validated through simulation experiments on real Digital Elevation Model(DEM)maps for path planning applications.

关 键 词：三维路径规划 凤头豪猪优化算法 无人机 混沌映射 

分 类 号：V249[航空宇航科学与技术—飞行器设计] TP242.6[自动化与计算机技术—检测技术与自动化装置]