A Data-Driven Real-Time Trajectory Planning and Control Methodology for UGVs Using LSTMRDNN  

作　　者：Kaiyuan Chen Runqi Chai Runda Zhang Zhida Xing Yuanqing Xia Guoping Liu 

机构地区：[1]Vanke School of Public Health,Institute for Healthy China,Tsinghua University,Beijing 100084 [2]School of Automation,Beijing Institute of Technology,Beijing 100081 [3]Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen 518055,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第5期1292-1294,共3页自动化学报（英文版）

摘　　要：Dear Editor,This letter presents a novel data-driven trajectory planning and control scheme for the unmanned ground vehicles(UGVs).A recent work[1]has demonstrated the effectiveness of approximating the optimal state feedback for a nonlinear unmanned system via deep neural network(DNN).

关 键 词：LSTM UGV NEURAL 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] U463.6[自动化与计算机技术—控制科学与工程]