计算机与现代化Issue(3)：7-14,8.DOI:10.3969/j.issn.1006-2475.2024.03.002
基于改进D3QN算法的泊车机器人路径规划
Path Planning of Parking Robot Based on Improved D3QN Algorithm
摘要
Abstract
The parking robot emerges as a solution to the urban parking problem,and its path planning is an important research direction.Due to the limitations of the A*algorithm,the deep reinforcement learning idea is introduced in this article,and im-proves the D3QN algorithm.Through replacing the convolutional network with a residual network and introducing attention mechanisms,the SE-RD3QN algorithm is proposed to improve network degradation and convergence speed,and enhance model accuracy.During the algorithm training process,the reward and punishment mechanism is improved to achieve rapid conver-gence of the optimal solution.Through comparing the experimental results of the D3QN algorithm and the RD3QN algorithm with added residual layers,it shows that the SE-RD3QN algorithm achieves faster convergence during model training.Compared with the currently used A*+TEB algorithm,SE-RD3QN can obtain shorter path length and planning time in path planning.Finally,the effectiveness of the algorithm is further verified through physical experiments simulating a car,which provides a new solution for parking path planning.关键词
深度强化学习/泊车机器人/路径规划/激光雷达传感器Key words
deep reinforcement learning/parking robot/path planning/lidar sensors分类
信息技术与安全科学引用本文复制引用
王健铭,王欣,李养辉,王殿龙..基于改进D3QN算法的泊车机器人路径规划[J].计算机与现代化,2024,(3):7-14,8.基金项目
国家自然科学基金资助项目(52275088) （52275088）
中央高校基本科研业务费资助(DUT22LAB507) （DUT22LAB507）
