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Smooth-optimal Adaptive Trajectory Tracking Using an Uncalibrated Fish-eye Camera

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Abstract

This paper presents a two-stage smooth-optimal trajectory tracking strategy. Different from existing methods, the optimal trajectory tracked point can be directly determined in an uncalibrated fish-eye image. In the first stage, an adaptive trajectory tracking controller is employed to drive the tracking error and the estimated error to an arbitrarily small neighborhood of zero. Afterwards, an online smooth-optimal trajectory tracking planner is proposed, which determines the tracked point that can be used to realize smooth motion control of the mobile robot. The tracked point in the uncalibrated image can be determined by minimizing a utility function that consists of both the velocity change and the sum of cross-track errors. The performance of our planner is compared with other tracked point determining methods in experiments by tracking a circular trajectory and an irregular trajectory. Experimental results show that our method has a good performance in both tracking accuracy and motion smoothness.

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Acknowledgements

This work was supported by National Key Research and Development Program (No. 2018YFB1306303) and National Natural Science Foundation of China (No. 61773374).

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Zhao-Bing Kang, Wei Zou, Zheng Zhu & Hong-Xuan Ma

  2. University of Chinese Academy of Sciences, Beijing, 100190, China

    Zhao-Bing Kang, Wei Zou, Zheng Zhu & Hong-Xuan Ma

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  2. Wei Zou
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Correspondence to Wei Zou.

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Recommended by Associate Editor Qing-Long Han

Zhao-Bing Kang received the B. Eng. degree in mechanical engineering and automation from Dezhou University, China in 2008, the M. Eng. degree in mechanical and electronic engineering from Harbin Institute of Technology, China in 2016. Currently, he is a Ph.D. degree candidate in Institute of Automation at Chinese Academy of Science (CASIA), China. He is also with University of Chinese Academy of Science, China.

His research interests include visual servoing and robot location and navigation.

Wei Zou received the B.Eng. degree in control science and engineering from Baotou University of Iron and Steel Technology, China in 1997, the M.Eng. degree in control science and engineering from Shandong University of Technology, China in 2000, and the Ph.D. degree in control science and engineering from Institute of Automation, Chinese Academy of Science (CASIA), China in 2003. Currently, he is a professor at the Research Center of Precision Sensing and Control, CASIA.

His research interests include intelligent robotics, visual servoing, and robot localization and navigation.

Zheng Zhu received the B.Sc. degree from Zhengzhou University, China in 2014. He is currently a Ph.D. degree candidate in IACAS, China. He is also with University of Chinese Academy of Sciences, China.

His research interests include computer vision, deep learning and robotics.

Hong-Xuan Ma received the B.Sc. degree from Central South University, China in 2016. He is currently a Ph.D. degree candidate in CASIA, China. He is also with University of Chinese Academy of Sciences, China.

His research interests include computer vision and robotics.

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Kang, ZB., Zou, W., Zhu, Z. et al. Smooth-optimal Adaptive Trajectory Tracking Using an Uncalibrated Fish-eye Camera. Int. J. Autom. Comput. 17, 267–278 (2020). https://doi.org/10.1007/s11633-019-1209-4

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