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
关键词
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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,
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页码:1292 / 1294
页数:3
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