基于BAS和Q-Learning的移动机器人路径规划算法研究  

Research on Path Planning Algorithm for Mobile Robots Based on BAS and Q-Learning

作　　者：程晶晶[1] 周明龙[1] 邓雄峰 CHENG Jingjing;ZHOU Minglong;DENG Xiongfeng(Anhui Technical College of Mechanical and Electrical Engineering,Wuhu Anhui 241000,China;Anhui Polytechnic University,Wuhu Anhui 241000,China)

机构地区：[1]安徽机电职业技术学院,安徽芜湖241000 [2]安徽工程大学,安徽芜湖241000

出　　处：《长春工程学院学报（自然科学版）》2023年第3期107-111,共5页Journal of Changchun Institute of Technology：Natural Sciences Edition

基　　金：2021年提质培优行动计划项目(2020tzpy1801-3);安徽省教学研究项目(2020jyxm0316);安徽省自然科学研究项目重点项目(2023AH052698)。

摘　　要：针对传统Q-Learning存在的起始阶段盲目搜索、动作选择策略不科学的问题,提出了联合BAS和Q-Learning的移动机器人路径规划算法。该算法运用欧式距离定位坐标,引入搜索因子对搜索时间和学习速度进行平衡改进,并采用BAS和贝塞尔曲线分别优化Q值、平滑路径。将提出的算法应用于仿真试验和实物试验,结果表明其能够高效、精准地在静态和动态障碍物环境下获得最优路径,具有较强的工程适用性。A mobile robot path planning algorithm combining BAS and Q-Learning is proposed to address the problems of blind search in the initial stage and unscientific action selection strategies in traditional Q-Learning.The algorithm uses European distance to locate coordinates,introduces search factors to balance and improve searching time and learning speed,and smooths paths by BAS,Bessel curves,and optimizing Q values respectively.As the algorithm proposed using in the simulation and physical experiments,the result is indicated that the algorithm can efficiently and accurately obtain the optimal path in static and dynamic obstacle environments,and has a strong engineering applicability.

关 键 词：移动机器人 路径规划 Q-Learning算法 天牛须搜索 路径平滑 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]