Path Planning of Mobile Robot Based on Improved Mayfly Optimization Algorithm

邹阿威; 王雷; 李伟民; 李凡; 蔡劲草; 王海; 谭铁龙; 桂劲松

doi:10.13433/j.cnki.1003-8728.20230091

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Path Planning of Mobile Robot Based on Improved Mayfly Optimization Algorithm

更新时间：2026-07-10

- Path Planning of Mobile Robot Based on Improved Mayfly Optimization Algorithm
- Mechanical Science and Technology for Aerospace Engineering Vol. 43, Issue 11, Pages: 1993-1999(2024)
- 作者机构：
  
  1. 安徽工程大学机构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
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20230091
  CLC： TP242;TP18
- Published：2024
- 稿件说明：
移动端阅览
邹阿威, 王雷, 李伟民, et al. Path Planning of Mobile Robot Based on Improved Mayfly Optimization Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(11): 1993-1999.
DOI：

邹阿威, 王雷, 李伟民, et al. Path Planning of Mobile Robot Based on Improved Mayfly Optimization Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(11): 1993-1999. DOI： 10.13433/j.cnki.1003-8728.20230091.

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。

Abstract

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.

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