常州大学学报（自然科学版）2026，Vol.38Issue(1)：57-65,9.DOI:10.3969/j.issn.2095-0411.2026.01.007
基于改进强化学习的移动机器人最短路径寻找方法
A shortest path finding method for mobile robots based on improved reinforcement learning
摘要
Abstract
Path planning is a pivotal concern in mobile robotics,with effective planning significantly enhancing the operational efficiency of robots.The crux of the issue lies in accelerating the environ-mental exploration speed of mobile robots and identifying the shortest possible path.Current rein-forcement learning-based methods for shortest path discovery suffer from slow update speeds,resul-ting in excessive time spent on initialization and data iteration,particularly in smaller models.To address this,a novel method for shortest path discovery in mobile robots were proposed,leveraging reinforce-ment learning.Given the unique characteristics of unknown environments,the grid method was adap-ted and a strategy that simultaneously explores,interacts,and models was implemented.Further-more,a back tracking update strategy into traditional Q-learning to expedite convergence speed was incorporated.The experimental results demonstrate that when the ε-greedy algorithm and normaliza-tion methods are employed as update strategies,the proposed method significantly reduces the train-ing time required for robot pathfinding,while also enhancing training accuracy.The efficacy and ad-vantages of the proposed scheme are further validated by simulation results.关键词
强化学习/Q学习/栅格法/智能机器人/路径规划Key words
reinforcement learning/Q-learning/grid method/intelligent robot/path planning分类
信息技术与安全科学引用本文复制引用
柴泽,高志鹏..基于改进强化学习的移动机器人最短路径寻找方法[J].常州大学学报（自然科学版）,2026,38(1):57-65,9.基金项目
国家自然科学基金资助项目(62072049) （62072049）
北京市自然科学基金资助项目(4232029). （4232029）
