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题名

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

作者
通讯作者Chai,Runqi
发表日期
2024-05-01
DOI
发表期刊
ISSN
2329-9266
EISSN
2329-9274
卷号11期号:5页码:1292-1294
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, we construct a long-short term memory recurrent deep neural network (LSTMRDNN) to improve the performance of the data-driven approximation instrument. The proposed strategy is evaluated and verified through theoretical analyses and experiments.
相关链接[Scopus记录]
收录类别
SCI ; EI
语种
英语
学校署名
第一 ; 通讯
Scopus记录号
2-s2.0-85191023932
来源库
Scopus
引用统计
成果类型期刊论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/760913
专题工学院_电子与电气工程系
作者单位
The authors are with the Vanke School of Public Health, Institute for Healthy China, Tsinghua University, Beijing 100084, the School of Automation, Beijing Institute of Technology, Beijing 100081, and the Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
第一作者单位电子与电气工程系
通讯作者单位电子与电气工程系
第一作者的第一单位电子与电气工程系
推荐引用方式
GB/T 7714
Chen,Kaiyuan,Chai,Runqi,Zhang,Runda,et al. A Data-Driven Real-Time Trajectory Planning and Control Methodology for UGVs Using LSTMRDNN[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(5):1292-1294.
APA
Chen,Kaiyuan,Chai,Runqi,Zhang,Runda,Xing,Zhida,Xia,Yuanqing,&Liu,Guoping.(2024).A Data-Driven Real-Time Trajectory Planning and Control Methodology for UGVs Using LSTMRDNN.IEEE/CAA Journal of Automatica Sinica,11(5),1292-1294.
MLA
Chen,Kaiyuan,et al."A Data-Driven Real-Time Trajectory Planning and Control Methodology for UGVs Using LSTMRDNN".IEEE/CAA Journal of Automatica Sinica 11.5(2024):1292-1294.
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