重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (6): 129-135.

• “智能机器人感知、规划及应用技术”专栏 • 上一篇    下一篇

改进灰狼算法的变电站巡检机器人路径规划

张 威,张鑫中,王丛佼,孙 兵   

  1. (1.上海电机学院 电气学院,上海 201306;2.上海海事大学 物流工程学院,上海 201306
  • 出版日期:2023-07-12 发布日期:2023-07-12
  • 作者简介:张威,女,博士,讲师,主要从事巡检机器人优化与控制研究,Email:viweizhang@163.com。

Path planning of substation inspection robots based on an improved grey wolf optimizer

  • Online:2023-07-12 Published:2023-07-12

摘要: 为进一步提高智能变电站巡检机器人的巡检效率,针对传统灰狼算法易陷入局部 最优和收敛效率低的问题,改进灰狼算法,用于智能变电站巡检机器人路径优化。根据灰狼算 法中不同参数对算法性能的影响,仿真分析了基于收敛函数和种群权重参数,以及同时进行混 合改进后灰狼算法在智能变电站巡检机器人路径规划中的性能。结果表明:混合改进的灰狼算 法,不仅缩短了智能变电站巡检机器人优化路径,还提高了路径规划效率

关键词: 智能变电站, 巡检机器人, 路径规划, 改进的灰狼算法, 蚁群算法

Abstract: To further improve the efficiency of intelligent substation inspection robots,this paper proposes an improved grey wolf optimizer for inspection robot path planning,aiming at the problem that the traditional grey wolf optimizer is prone to local optima and has a low convergence efficiency.Based on the impact of different parameters of the grey wolf optimizer on the algorithm performance,a simulation analysis is conducted on the performance of the optimizer in path planning for intelligent substation inspect robots based on the convergence function and population weight parameters respectively after the mixed improvement of the optimizer is conducted.The simulation results show that the mixed improved grey wolf optimizer not only shortens the optimized path of the intelligent substation inspection robot but also improves its path planning efficiency.

中图分类号: 

  • TP242.6