智能科学与技术学报2025，Vol.7Issue(2)：246-256,11.DOI:10.11959/j.issn.2096-6652.202513
基于灰狼优化的自适应混合A*和人工势场的无人矿卡路径规划研究
Research on unmanned mining truck path planning based on grey wolf optimization adaptive hybrid A* and artificial potential field
摘要
Abstract
To enhance the path planning capabilities of unmanned mining trucks in open-pit mining scenarios,a grey wolf optimization-based adaptive hybrid A* and artificial potential field(GWO-HAPF)method was proposed.The proposed method emploied the grey wolf optimization algorithm to adaptively adjust the key parameters of the hybrid A*(HA*)al-gorithm,achieving a multi-objective balance among path length,smoothness,and planning time.This effectively over-came the HA* algorithm's limited adaptability to fixed parameter settings,significantly improving the quality and adapt-ability of global path planning.For local path planning,an improved artificial potential field method was adopted,opti-mizing the repulsive force function and incorporating an escape force mechanism,which effectively enhanced real-time obstacle avoidance feasibility and path smoothness.Experimental results demonstrate that,compared to the standard HA*algorithms and the improved hybrid A*(IHA*)algorithms,GWO-HAPF improves computational efficiency in global planning by an average of 80%and 14.9%,respectively,reduces path length by over 9.8%,and increases smoothness by over 53%.In local path planning,GWO-HAPF achieves a planning time that is 95.8%shorter than IHA*,while its smoothness improves to 10.19%of IHA*.These findings indicate that the proposed method exhibits outstanding advan-tages in planning efficiency,path length,smoothness,and real-time obstacle avoidance,showcasing its practical applica-tion value in path planning for unmanned mining trucks in open-pit mining scenarios.关键词
路径规划/灰狼优化/自适应混合A*/人工势场/无人矿卡Key words
path planning/grey wolf optimization/adaptive hybrid A*/artificial potential field/unmanned mining truck分类
信息技术与安全科学引用本文复制引用
韩彦岭,吕金科,周国峰,张力珂,张云..基于灰狼优化的自适应混合A*和人工势场的无人矿卡路径规划研究[J].智能科学与技术学报,2025,7(2):246-256,11.基金项目
国家自然科学基金项目(No.42176175,No.42271335) The National Natural Science Foundation of China(No.42176175,No.42271335) （No.42176175,No.42271335）
