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

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.