改进SARSA算法的移动机器人路径规划研究  

Research on Path Planning of Mobile Robot with Improved SARSA Algorithm

作　　者：井征淼 刘宏杰 周永录 JING Zhengmiao;LIU Hongjie;ZHOU Yonglu(School of Information,Yunnan University,Kunming 650504,China;Key Laboratory of Digital Media Technology in Yunnan Province′s Universities,Kunming 650223,China)

机构地区：[1]云南大学信息学院,云南昆明650504 [2]云南省高校数字媒体技术重点实验室,云南昆明650223

出　　处：《软件导刊》2024年第12期119-124,共6页Software Guide

摘　　要：针对传统SARSA算法在移动机器人路径规划中存在收敛速度慢、探索随机性较大且在有动态障碍物的环境下路径规划效果不佳的问题,提出一种将人工势场法与传统SARSA算法相结合的改进SARSA算法。首先,利用人工势场的引力函数控制算法的奖励函数,增加探索时的导向性;其次,利用人工势场的斥力函数生成μ值,以有效调整路径规划中的Q值,使越靠近障碍物时Q值越低,从而提高路径规划算法的收敛速度,减少与障碍物的碰撞频率。将改进SARSA算法与其他算法进行仿真实验,对比验证结果表明,改进后的SARSA算法在收敛速度、平均学习时间、平均学习步数、撞到障碍物的平均次数等性能上提高明显,可有效提升移动机器人智能化行动的路径规划能力。Aiming at the problems of slow convergence speed,high exploration randomness,and poor path planning performance in mobile robot path planning using traditional SARSA algorithm,this paper proposes an improved SARSA algorithm that combines artificial potential field method with traditional SARSA algorithm.Firstly,the reward function of the algorithm is controlled by the gravity function of the artificial potential field to increase the guidance during exploration;Secondly,using the repulsive function of the artificial potential field to generateμTo effectively adjust the Q value in path planning,the value should be adjusted to lower the Q value as it approaches obstacles,thereby improving the convergence speed of the path planning algorithm and reducing the frequency of collisions with obstacles.The improved SARSA algorithm was compared with other algorithms through simulation experiments,and the results showed that the improved SARSA algorithm has significantly improved performance in convergence speed,average learning time,average learning steps,and average number of collisions with obstacles,which can effectively enhance the path planning ability of intelligent actions of mobile robots.

关 键 词：强化学习 改进SARSA 人工势场法 路径规划 移动机器人 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]