A Multi-AGV Routing Planning Method Based on Deep Reinforcement Learning and Recurrent Neural Network  

作　　者：Yishuai Lin Gang Hu Liang Wang Qingshan Li Jiawei Zhu 

机构地区：[1]the School of Computer Science and Technology,Xidian University,Xi’an 710000,China [2]Suzhou Mingyi Intelligence Warehousing Information Technology Co.,Ltd.,Kunshan 215300,China [3]the School of Information Engineering,Chang’an University,Xi’an 710000,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第7期1720-1722,共3页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (62202352,61902039,61972300);the Basic and Applied Basic Research Program of Guangdong Province (2021A1515110518);the Key Research and Development Program of Shaanxi Province (2020ZDLGY09-04)。

摘　　要：Dear Editor,This letter presents a multi-automated guided vehicles(AGV) routing planning method based on deep reinforcement learning(DRL)and recurrent neural network(RNN), specifically utilizing proximal policy optimization(PPO) and long short-term memory(LSTM).

关 键 词：network AGV DEEP 

分 类 号：TP23[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]