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

Kaiyuan Chen; Runqi Chai; Runda Zhang; Zhida Xing; Yuanqing Xia; Guoping Liu

doi:10.1109/JAS.2024.124269

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

Kaiyuan Chen, Runqi Chai^*, Runda Zhang, Zhida Xing, Yuanqing Xia, Guoping Liu

^*Corresponding author for this work

Tsinghua University

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	1292-1294
Number of pages	3
Journal	IEEE/CAA Journal of Automatica Sinica
Volume	11
Issue number	5
DOIs	https://doi.org/10.1109/JAS.2024.124269
Publication status	Published - 1 May 2024
Externally published	Yes

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Chen, K., Chai, R., Zhang, R., Xing, Z., Xia, Y., & Liu, G. (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. https://doi.org/10.1109/JAS.2024.124269

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abstract = "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.",

author = "Kaiyuan Chen and Runqi Chai and Runda Zhang and Zhida Xing and Yuanqing Xia and Guoping Liu",

note = "Publisher Copyright: {\textcopyright} 2014 Chinese Association of Automation.",

year = "2024",

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doi = "10.1109/JAS.2024.124269",

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AU - Chai, Runqi

AU - Zhang, Runda

AU - Xing, Zhida

AU - Xia, Yuanqing

AU - Liu, Guoping

PY - 2024/5/1

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N2 - 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.

AB - 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.

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

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