基于多策略改进灰狼算法的无人机路径规划  

UAV path planning based on multi-strategy improved gray wolf algorithm

作　　者：宋宇[1] 高岗 梁超[1] 徐军生[1] Song Yu;Gao Gang;Liang Chao;Xu Junsheng(School of Computer Science and Engineering,Changchun University of Technology,Changchun 130012,China)

机构地区：[1]长春工业大学计算机科学与工程学院,长春130012

出　　处：《电子测量技术》2025年第1期84-91,共8页Electronic Measurement Technology

基　　金：吉林省科技发展计划项目(20220201030GX)资助。

摘　　要：针对传统的灰狼算法在三维路径规划中容易陷入局部最优等问题,本文提出了一种改进的灰狼算法。首先,对三维威胁区域进行环境建模,对约束条件规定无人机飞行的总成本函数;其次,在灰狼种群初始化中加入了混沌序列和准反向学习策略,增加了群种多样性以及未知领域的搜索范围,通过对自适应权重因子的改进来更新个体位置,从而加快收敛速度;最后,为了避免陷入局部最优,引入了粒子群算法从而平衡全局开发与局部收敛。通过实验结果表明,相较于另外3种典型路径规划算法,改进灰狼算法可以寻找出一条安全可行的路径,并且有着较稳定的寻优能力。Aiming at the problem that traditional grey wolf algorithm is prone to local optimality in 3D path planning,an improved grey wolf algorithm is proposed in this paper.Firstly,the environment of the three-dimensional threat region is modeled,and the total cost function of UAV flight is specified under the constraint conditions.Secondly,chaotic sequences and quasi-reverse learning strategies were added to the initialization of grey wolf population,which increased the diversity of species and the search scope of unknown domain,and improved the adaptive weight factors to update individual positions,thus speeding up the convergence speed.Finally,in order to avoid falling into local optimization,particle swarm optimization algorithm is introduced to balance global development and local convergence.The experimental results show that compared with the other three typical path planning algorithms,the improved gray wolf algorithm can find a safe and feasible path,and has a stable optimization ability.

关 键 词：无人机 三维路径规划 混沌序列 准反向学习 灰狼算法 粒子群算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TN911.7[自动化与计算机技术—计算机科学与技术]