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

被引：0

作者：

Kaiyuan Chen ^{[1
,2
,3
,4
]}

Runqi Chai ^{[1
,2
,3
,4
]}

Runda Zhang ^{[2
,3
,4
]}

Zhida Xing ^{[2
,3
,4
]}

Yuanqing Xia ^{[1
,2
,3
,4
]}

Guoping Liu ^{[1
,2
,3
,4
]}

机构：

[1] IEEE

[2] the Vanke School of Public Health, Institute for Healthy China, Tsinghua University

[3] the School of Automation, Beijing Institute of Technology

[4] the Department of Electronic and Electrical Engineering, Southern University of Science and Technology

来源：

IEEE/CAA Journal of Automatica Sinica | 2024年 / 11卷 / 05期

关键词：

D O I：

暂无

中图分类号：

TP273 [自动控制、自动控制系统]; U463.6 [电气设备及附件];

学科分类号：

080201 ; 0835 ;

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). To further the previous research,

引用

页码：1292 / 1294

页数：3

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