改进蜉蝣算法的移动机器人路径规划研究  

Path Planning of Mobile Robot Based on Improved Mayfly Optimization Algorithm

作　　者：邹阿威 王雷 李伟民 李凡 蔡劲草 王海 谭铁龙 桂劲松 ZOU Awei;WANG Lei;LI Weimin;LI Fan;CAI Jingcao;WANG Hai;TAN Tielong;GUI Jingsong(School of Mechanical and Automotive Engineering,Anhui Polytechnic University,Wuhu 241000,Anhui,China;Wuhu Kepu Intelligent Equipment Co.,Ltd.,Wuhu 241000,Anhui,China;Wuhu Ruilong Robot Technology Co.,Ltd.,Wuhu 241000,Anhui,China)

机构地区：[1]安徽工程大学机械与汽车工程学院,安徽芜湖241000 [2]芜湖柯埔智能装备有限公司,安徽芜湖241000 [3]芜湖锐龙机器人科技有限公司,安徽芜湖241000

出　　处：《机械科学与技术》2024年第11期1993-1999,共7页Mechanical Science and Technology for Aerospace Engineering

基　　金：安徽省高校优秀拔尖人才培育项目(gxbjZD2022023);安徽工程大学-鸠江区产业协同创新专项(2022cyxtb6,2022cyxtb4);安徽工程大学检测技术与节能装置安徽省重点实验室开放研究项目(JCKJ2021A06);芜湖市科技计划(2022jc26);机器视觉检测安徽省重点实验室开放基金项目(KLMVI-2024-HIT-15)。

摘　　要：针对传统蜉蝣算法在机器人路径规划领域应用时存在收敛速度较慢、精度差、稳定性不足等问题,提出了一种改进的蜉蝣算法。该算法引入了一种动态参数调整策略,使算法局部搜索与全局搜索能力达到更好的平衡,并融合了莱维飞行策略与跳出策略,避免了算法陷入局部最优。在20×20的地图环境下对改进的蜉蝣算法进行20次随机仿真实验,仿真表明本算法在求解精度和求解速度上均有较显著提升。同时,为进一步验证本文改进蜉蝣算法的可靠性和有效性,在30×30的栅格地图环境下对改进蜉蝣算法进行20次随机仿真实验,结果表明改进蜉蝣算法在可靠性和稳定性上也有所提升。In order to solve the problems of slow convergence speed,poor convergence accuracy and insufficient stability in applying the traditional mayfly optimization algorithm to a robot′s path planning,an improved mayfly optimization algorithm is proposed.In the improved mayfly optimization algorithm,a dynamic parameter adjustment strategy is introduced to achieve a better balance between local search and global search.The Levy flight strategy and the exiting strategy are combined to avoid falling into local optimization.The improved mayfly optimization algorithm is randomly tested for 20 times in a 20×20 grid model.The simulation results show that the improved optimization algorithm significantly improves the solving accuracy and speed and the smoothness of the path of the robot.At the same time,in order to further verify the reliability and effectiveness of the improved mayfly optimization algorithm,it is randomly tested for 20 times with the 30×30 grid model.The results show that the reliability and stability of the improved optimization algorithm are improved.

关 键 词：改进蜉蝣优化算法 路径规划 莱维飞行 自适应参数调整策略 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]