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金属矿山 ›› 2025, Vol. 54 ›› Issue (10): 166-174.

• 机电与自动化 • 上一篇    下一篇

用于矿用空压机组智能巡检机器人路径规划的改进蝙蝠算法#br#

王建华1 万 超2 韩楠楠3   

  1. 1. 鹤壁能源化工职业学院电子信息工程系,河南 鹤壁 458030;2. 郑州大学水利与土木工程学院,河南 郑州 450000;
    3. 河南科技大学应用工程学院,河南 三门峡 472000
  • 出版日期:2025-10-15 发布日期:2025-11-07
  • 通讯作者: 万 超(1987—),男,博士研究生。
  • 作者简介:王建华(1977—),女,讲师,硕士。
  • 基金资助:
    河南省高等学校重点科研项目计划(编号:24A560021)。

Path Planning of Intelligent Inspection Robot for Mining Air Compressor Unit Based on Improved Bat Algorithm#br#

WANG Jianhua1 WAN Chao2 HAN Nannan3   

  1. 1. Department of Electronic Information,Hebi Vocational College of Energy and Chemistry,Hebi 458030,China;
    2. School of Water Resources and Civil Engineering,Zhengzhou University,Zhengzhou 450000,China;
    3. School of Applied Engineering,Henan University of Science and Technology,Sanmenxia 472000,China
  • Online:2025-10-15 Published:2025-11-07

摘要: 在矿用空压机组智能巡检机器人中,传统算法用于智能巡检机器人路径规划时,面对复杂矿井环境存
在寻优速度慢、易陷入局部最优解等问题。为提升巡检效率和精度,提出了改进的蝙蝠算法(Improved Bat Algorithm,
IBA)。首先,采用均匀初始化策略确保初始位置能广泛覆盖决策空间;其次,在迭代更新过程中,引入黄金正弦算子
对在适应度评价中表现优异的蝙蝠个体进行优化更新,同时运用种群平均位置引导部分个体,在缩小搜索范围的同
时维持较快收敛速度;最后在全局搜索阶段引入动态惯性权重系数,并采用单维与全维相结合的搜索策略。试验表
明:IBA 算法在5 维条件下,Sphere 函数测试中的收敛迭代次数仅20 次,远少于蝙蝠算法(Bat Algorithm,BA),50 维条
件下同样表现出色;在机器人路径规划效果上,IBA 算法规划路径长度比BA、自适应蝙蝠算法(Adaptive BA,ABA)和
全局混沌蝙蝠算法(Global Chaos BA,GCBA)规划的路径更短,且在多个场景中转折点数量更少、收敛迭代次数更少、
适应度值更低。研究反映出,基于IBA 算法的智能巡检机器人路径规划方法可使矿用空压机组巡检效率提升45.
9%,故障检测准确率提高至98. 9%。所提算法有助于实现矿用空压机组智能巡检机器人路径高效规划,助力矿山安
全生产。

关键词: 矿用空压机组 智能巡检 路径规划 蝙蝠算法 黄金正弦

Abstract: In the intelligent inspection robot for mine air compressor units,when traditional algorithms are used for the
path planning of intelligent inspection robots,they encounter problems such as slow optimization speed and easy trapping in local
optimal solutions in complex mine environments. To improve the inspection efficiency and accuracy,an Improved Bat Algorithm
(IBA) is proposed. Firstly,a uniform initialization strategy is adopted to ensure that the initial positions can widely cover
the decision space. Secondly,in the iterative update process,a golden sine operator is introduced to optimize and update the bat
individuals that perform well in the fitness evaluation,while the average position of the population is used to guide some individuals
to narrow the search range while maintaining a fast convergence speed. Additionally,a dynamic inertia weight coefficient
is introduced in the global search stage,and a search strategy combining single-dimensional and full-dimensional is adopted.
Experiments show that under 5-dimensional conditions,the IBA algorithm converges in only 20 iterations in the Sphere function
test,far fewer than the Bat Algorithm (BA),and performs well under 50-dimensional conditions. In terms of the path planning
effect of the robot,the path planned by the IBA algorithm is shorter than that planned by BA,Adaptive Bat Algorithm (ABA),
and Global Chaos Bat Algorithm (GCBA),and has fewer turning points,fewer convergence iterations,and lower fitness values in multiple scenarios. The research reflects that the intelligent inspection robot path planning method based on the IBA algorithm
can increase the inspection efficiency of mine air compressor units by 45. 9% and improve the fault detection accuracy to
98. 9%. The proposed algorithm is helpful for the efficient path planning of intelligent inspection robots for mine air compressor
units and contributes to the safe production of mines.

Key words: mining air compressor unit,intelligent inspection,path planning,bat algorithm,golden sine

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