[1] Xu Xinzhou, Chen Yongfa, Liu Guangming, Li Ziqian, et al. Motion estimation for cable-driven distal end-effectors usingattention-based bi-directional gated recurrent neural networks [J]. Journal of Southeast University (English Edition), 2023, 39 (2): 187-193. [doi:10.3969/j.issn.1003-7985.2023.02.010]

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Motion estimation for cable-driven distal end-effectors usingattention-based bi-directional gated recurrent neural networks()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

39

Issue:

2023 2

Page:

187-193

Research Field:

Automation

Publishing date:

2023-06-20

Info

Title:

Motion estimation for cable-driven distal end-effectors usingattention-based bi-directional gated recurrent neural networks

Author(s):

Xu Xinzhou¹;  Chen Yongfa¹;  Liu Guangming²;  Li Ziqian²;  Zhao Li³;  Wang Zhengyu²

¹School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
²School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China
³School of Information Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:

cable-driven distal end-effectors;  motion estima-tion;  bi-directional gated recurrent neural networks;  attention mechanism

PACS:

TP241.3

DOI:

10.3969/j.issn.1003-7985.2023.02.010

Abstract:

A data-driven motion estimation approach based on attention-based bi-directional gated recurrent neural networks was proposed to adaptively estimate the motion of a cable-driven distal end-effector. First, the data construction was performed to obtain short-term temporal sequences as training samples. The data were then processed using the bi-directional gated recurrent neural networks with self-attention modules for building sequential models on the samples. Finally, based on the motion dataset of the cable-driven distal end-effectors, the estimation-performance comparison experiments were performed using the motor’s position, speed, and input time sequence for the system-control signal as the sample features. The results show that compared with conventional sequence modeling regression approaches, the proposed approach can achieve better performance for estimating the motion of the end-effector. Therefore, it can effectively estimate the motion of cable-driven distal end-effectors under complex conditions.

References:

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Memo

Memo:

Biographies: Xu Xinzhou(1987—), male, doctor, associate professor; Wang Zhengyu(corresponding author), male, doctor, associate professor, wangzhengyu_hfut@hfut.edu.cn.
Foundation items: The China Postdoctoral Science Foundation(No.2022M711693), the Natural Science Foundation of China(No.52175221, 61801241, 62071242), the Natural Science Foundation of Jiangsu Province(No.BK20191381).
Citation: Xu Xinzhou, Chen Yongfa, Liu Guangming, et al. Motion estimation for cable-driven distal end-effectors using attention-based bi-directional gated recurrent neural networks[J].Journal of Southeast University(English Edition), 2023, 39(2):187-193.DOI:10.3969/j.issn.1003-7985.2023.02.010.

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