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Random vector functional link network with L21 norm regularization for robot visual servo control with feature constraint

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Abstract

Uncalibrated visual servoing control still encounters some challenges, such as calculating the interaction matrix with less cost and keeping the current image features within a camera’s field of view (FOV) in a noisy system environment. To solve these problems, we propose a new control method that uses a random vector functional link network with L21 norm regularization to calculate the interaction matrix and further estimate it with a robust information filter (RIF). L21 norm regularization can deal with the global sparsity of input weights and reduce the inherent complexity of a model. The RIF limits noise variance within a certain range to reduce the influence of uncertain noise on the servoing task. We also design a method that reacts to the control law in accordance with the coordinates of image features. It can adjust running speed in real time and keep image features within a camera’s FOV. We apply this method to a six-degrees-of-freedom eye-in-hand manipulator, and several simulations are performed. Simulation results show that the proposed algorithm performs well in the task and achieves good performance in terms of noise resistance. Image features barely escape from the camera’s FOV through the proposed constraint method.

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Acknowledgments

This work is supported by the Key R&D Program of Zhejiang Province (No. 2021C03013) and the Zhejiang Provincial Natural Science Foundation of China (No. LZ20F020003).

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Authors and Affiliations

  1. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China

    Zhiyu Zhou & Jiusen Guo

  2. Zhejiang Key Laboratory of DDIMCCP, Lishui University, Lishui, China

    Yaming Wang

  3. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Zefei Zhu

Authors
  1. Zhiyu Zhou
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  2. Jiusen Guo
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  3. Yaming Wang
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  4. Zefei Zhu
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Correspondence to Zhiyu Zhou.

Additional information

Zhiyu Zhou obtained his Master’s degree and Ph.D. from Zhejiang Sci-Tech University in 2004 and 2018, respectively. He is currently an Associate Professor at Zhejiang Sci-Tech University and is engaged in computer vision, machine learning, and robotic tracking.

Jiusen Guo graduated with a Bachelor’s degree from Southwest Jiaotong University in 2017. He is currently a Postgraduate Student at Zhejiang Sci-Tech University. He is engaged in machine vision.

Yaming Wang obtained his Ph.D. in Biomedical Engineering from Zhejiang University, China. He is currently a Professor of Computer Science at Lishui University, Zhejiang, China. He had been a visiting researcher and a visiting scientist at Hong Kong University of Science and Technology. His research interests include computer vision, pattern recognition, and signal processing.

Zefei Zhu obtained his M.S. and Ph.D. from Zhejiang University in 1986 and 1999, respectively. He is currently a Professor at Hangzhou Dianzi University and engaged in machine vision, mechanical automation, and manipulator control.

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Zhou, Z., Guo, J., Wang, Y. et al. Random vector functional link network with L21 norm regularization for robot visual servo control with feature constraint. J Mech Sci Technol 36, 4747–4759 (2022). https://doi.org/10.1007/s12206-022-0834-y

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  • DOI: https://doi.org/10.1007/s12206-022-0834-y

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