基于改进人工蜂群算法的无人飞行器路径规划  

Unmanned Aerial Vehicle Path Planning Method Based on Improved Artificial Bee Colony Algorithm

作　　者：任鑫磊 胡燕海[2] 丁智坚 曲俐鹏 REN Xinlei;HU Yanhai;DING Zhijian;QU Lipeng(Aerospace Technology Institute of CARDC,Mianyang,Mianyang Sichuan 621000,China;School of Mechanical Engineering and Mechanics,Ningbo University,Ningbo Zhejiang 315211,China)

机构地区：[1]中国空气动力研究与发展中心空天技术研究所,四川绵阳621000 [2]宁波大学机械工程与力学学院,浙江宁波315211

出　　处：《机械设计与研究》2024年第3期58-63,共6页Machine Design And Research

基　　金：国家自然科学基金(51705263);宁波市重点研发计划(2023Z169)资助项目。

摘　　要：针对复杂三维未知环境中无人飞行器路径规划问题研究,提出一种基于改进人工蜂群算法的路径规划方法。基于传统人工蜂群算法引入正态模型优化蜜源初始化分布,提高蜜源分布的目的性;构建具有动态步长搜索因子的正弦扰动、柯西分布策略优化蜜源位置搜索方式,提高蜜源搜索的方向性,加快算法的收敛速度。采用Matlab软件曲面网格划分,构建威胁区和侦察区的三维静态、动态,协同仿真环境模型。通过Matlab软件仿真,结果表明相比传统人工蜂群算法和粒子群算法,基于改进人工蜂群算法规划的路径长度短、收敛速度快、初始适应度低,误差波动小、重复实验稳定性高,验证了该方法的可行性。A path planning method based on improved artificial bee colony algorithm is proposed for unmanned aerial vehicles in complex three-dimensional unknown environments.Based on the traditional artificial bee colony algorithm,a normal model is introduced to optimize the initial distribution of honey sources,improving the purposiveness of honey source distribution.A sine perturbation and Cauchy distribution strategy is constructed with dynamic step size search factor to optimize the honey source location search method.The directionality of honey source search is improved and the convergence speed of the algorithm is accelerated.Using Matlab software for surface mesh partitioning,a three-dimensional static,dynamic and collaborative simulation environment model is constructed for threat and reconnaissance areas.Through Matlab software simulation,the results show that compared to traditional artificial bee colony algorithm and particle swarm algorithm,the improved artificial bee colony algorithm has shorter path length,faster convergence speed,lower initial fitness,smaller error fluctuations and higher stability in repeated experiments,which verifies the feasibility of the proposed method in this paper.

关 键 词：人工蜂群算法 无人飞行器 路径规划 机器人 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]