计算机与数字工程2024，Vol.52Issue(5)：1265-1269,1274,6.DOI:10.3969/j.issn.1672-9722.2024.05.001
基于改进Q-learning算法移动机器人局部路径研究
Research on Local Path of Mobile Robot Based on Improved Q-learning Algorithm
摘要
Abstract
In local path planning,the mobile robot can't find a suitable path because it can't get the environmental information in advance,and there are some problems such as low learning efficiency and slow convergence speed when the traditional reinforce-ment learning algorithm in markov decision processes is applied to local path planning.In this paper,an improved Q-learning(QL)algorithm is proposed.Firstly,a dynamic adaptive greedy strategy is designed to balance the problems between mobile robots'explo-ration and utilization of the environment.Secondly,a heuristic learning evaluation model is designed according to the idea of A*al-gorithm,so as to dynamically adjust learning factors and provide guidance for searching paths.Finally,the third-order Bezier curve programming is introduced to smooth the path.The simulation results on Pycharm platform show that the path length,search efficien-cy and path smoothness planned by the improved QL algorithm are superior to those of the traditional Sarsa algorithm and QL algo-rithm.Compared with the traditional Sarsa algorithm,the iteration times are increased by 32.3%,the search time is shortened by 27.08%,the iteration times are increased by 27.32%,the search time is shortened by 17.28%,the inflection point of path planning is greatly reduced,and the local path optimization effect is obvious.关键词
移动机器人/Q-learning算法/局部路径/A*算法/贝塞尔曲线Key words
mobile robot/Q-learning algorithm/local path/A*algorithm/bezier curve分类
信息技术与安全科学引用本文复制引用
方文凯,廖志高..基于改进Q-learning算法移动机器人局部路径研究[J].计算机与数字工程,2024,52(5):1265-1269,1274,6.基金项目
国家自然科学基金面上项目(编号:71771157) （编号:71771157）
广西自动检测技术与仪器重点实验室开放基金项目(编号:YQ20208) （编号:YQ20208）
2020年广西汽车零部件与整车技术重点实验室自主研究课题(编号:2020GKLACVTZZ01)资助. （编号:2020GKLACVTZZ01）
