基于改进鹈鹕优化算法的移动机器人路径规划

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关键词：优化算法；移动机器人；混沌映射;Levy飞行;路径规划；正余弦优化；智能算法;全局优化

中图分类号：TP242.6 文献标志码：A doi：10.12415/j.issn.1671-7872.25070

Abstract：To address the issues of the pelican optimization algorithm （POA）easily falling into local optima and exhibiting slow convergence speed in mobile robot path planning，a multi-strategy improved pelican optimization algorithm was proposed in thispaper.Firstly，the population was initialized using Logisticchaotic mapping to enhance its diversity and distribution uniformity.Secondly，the sine optimization algorithmand a nonlinear inertia weight coeficient were incorporated to balance global exploration and local exploitation capabilities.Furthermore， the Levy flight strategy was introduced to improve the algorithm's ability to escape local optima and maintain satisfactory global search performance in the later iterations.Simulation results demonstrate that on six benchmark test functions，the improved algorithm significantly outperforms the original algorithm in terms of global search capability and convergence accuracy; in path planning tasks on 20×20 and 40×40 grid maps， the average path length is reduced by more than 7% to 10% compared with the original pelican optimization algorithm， sparrow search algorithm，and grey wolf optimizer，with higher computational efficiency，exhibiting superior path planning performance and robustnessin complex environments，and this study provides an efective and stable new solution for global path planning of mobile robots.

Keywords：pelican optimization algorithm; mobile robot; chaotic mapping; Levy flight; path planning; sine cosine optimization; intelligent algorithm; global optimization

随着智能化与自动化技术的深人发展，移动机器人在工业自动化[1-2]、危险区域作业[3]等复杂环境中的应用日益广泛。（剩余13305字）