信息与控制2024，Vol.53Issue(3)：365-376,12.DOI:10.13976/j.cnki.xk.2024.3090
基于改进深度双Q网络的移动机器人路径规划算法
Mobile Robot Path Planning Algorithm with Improved Deep Double Q Networks
摘要
Abstract
To solve the problems of the conventional mobile robot path planning method based on the deep double Q-network(DDQN),such as incomplete search and slow convergence,we propose an improved DDQN(I-DDQN)learning algorithm.First,the proposed I-DDQN algorithm uses the competitive network structure to estimate the value function of the DDQN algorithm.Second,we propose a robot path exploration strategy based on a two-layer controller structure,where the value function of the upper controller is used to explore the local optimal action of the mobile robot and the value function of the lower controller is used to learn the global task strategy.In addition,during algorithm learning,we use the priority experience playback mechanism for data collection and sampling and the small-batch data for network training.Finally,we perform a comparative analysis with the conventional DDQN algorithm and its improved algorithm in two different simulation environments,Open AI Gym and Gazebo.The experimental results show that the proposed I-DDQN al-gorithm is superior to the conventional DDQN algorithm and its improved algorithm in terms of vari-ous evaluation indicators in the two simulation environments and effectively overcomes the problems of incomplete path search and slow convergence speed in the same complex environment.关键词
深度学习/强化学习/分层深度强化学习/竞争网络结构/机器人路径规划/优先经验回放Key words
deep learning/reinforcement learning/hierarchical deep reinforcement learning/competitive network structure/robot path planning/priority experience playback分类
信息技术与安全科学引用本文复制引用
张磊,母亚双,潘泉..基于改进深度双Q网络的移动机器人路径规划算法[J].信息与控制,2024,53(3):365-376,12.基金项目
国家自然科学基金青年基金项目(62006071) （62006071）
河南省重点研发与推广专项(科技攻关)项目(242102210016) （科技攻关）
