基于改进Q-learning算法移动机器人局部路径研究OACSTPCD

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.