工矿自动化2026，Vol.52Issue(3)：102-113,12.DOI:10.13272/j.issn.1671-251x.2025120051
基于改进灰狼优化的煤矿巡检机器人路径规划算法研究
Path planning algorithm for coal mine inspection robots based on improved Grey Wolf Optimizer
摘要
Abstract
To address the problems that path planning algorithm for coal mine inspection robots based on Grey Wolf Optimization(GWO)is prone to falling into local optima and shows insufficient dynamic adaptability in complex underground environments,an Improved GWO(IGWO)-based path planning algorithm for coal mine inspection robots was proposed.A Piecewise Linear Chaotic Map(PWLCM)was introduced for population initialization to ensure uniform population distribution and enhance global search capability.A nonlinear convergence factor was designed to effectively balance the algorithm's global exploration and local exploitation and avoid falling into local optima.A dual-population structure,differential evolution,and an elimination mechanism were introduced to enhance population diversity and improve the algorithm's adaptability to the environment.A cubic B-spline curve was incorporated to smooth the generated path,improving path executability and reducing redundant turning points.A two-dimensional spatial model based on feature grids was proposed to effectively reduce the computational complexity of path search and improve real-time performance of the algorithm.Comparative experiments were conducted under various typical coal mine environments including random obstacle maps,fixed obstacle maps,and narrow maps,with IGWO compared against GWO,Memory,Evolutionary Operator,Local Search,and Linear Population Size Reduction Technique based GWO(MELGWO),A*,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO).The results showed that IGWO outperformed the comparison algorithms in terms of path length and safety.In random complex scenarios,the path length of IGWO was 56.9％shorter than that of MELGWO.In a 20×20 fixed scenario,the average number of turning points of IGWO was reduced by 12.5％and 44.4％compared with WOA and A*,respectively.In a 40×40 fixed scenario,the range and variance of the IGWO path length were both lower than those of WOA and PSO.In a narrow map environment,IGWO successfully planned a smoother path than A*and required less running time.关键词
煤矿巡检机器人/路径规划/灰狼算法/差分进化/路径平滑/特征栅格/三次B样条曲线/分段线性混沌映射Key words
coal mine inspection robots/path planning/Grey Wolf Optimization/differential evolution/path smoothing/feature grid/cubic B-spline curve/Piecewise Linear Chaotic Map分类
矿业与冶金引用本文复制引用
温海骏,崔雪岩,刘永姜..基于改进灰狼优化的煤矿巡检机器人路径规划算法研究[J].工矿自动化,2026,52(3):102-113,12.基金项目
山西省科技成果转化引导专项(202304021301029) （202304021301029）
山西电子科技学院科研启动经费资助项目(2025KJ023) （2025KJ023）
宁夏市场监督管理厅科技计划项目(2024SJKY0007). （2024SJKY0007）
