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Volume 8 Issue 12
Dec.  2021

IEEE/CAA Journal of Automatica Sinica

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Tianhao Zhang, Jiuhong Xiao, Liang Li, Chen Wang and Guangming Xie, "Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1964-1976, Dec. 2021. doi: 10.1109/JAS.2021.1004228
Citation: Tianhao Zhang, Jiuhong Xiao, Liang Li, Chen Wang and Guangming Xie, "Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1964-1976, Dec. 2021. doi: 10.1109/JAS.2021.1004228

Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks

doi: 10.1109/JAS.2021.1004228
Funds:  This work was supported in part by the National Natural Science Foundation of China (61973007, 61633002)
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  • Controlling multiple multi-joint fish-like robots has long captivated the attention of engineers and biologists, for which a fundamental but challenging topic is to robustly track the postures of the individuals in real time. This requires detecting multiple robots, estimating multi-joint postures, and tracking identities, as well as processing fast in real time. To the best of our knowledge, this challenge has not been tackled in the previous studies. In this paper, to precisely track the planar postures of multiple swimming multi-joint fish-like robots in real time, we propose a novel deep neural network-based method, named TAB-IOL. Its TAB part fuses the top-down and bottom-up approaches for vision-based pose estimation, while the IOL part with long short-term memory considers the motion constraints among joints for precise pose tracking. The satisfying performance of our TAB-IOL is verified by testing on a group of freely swimming fish-like robots in various scenarios with strong disturbances and by a deed comparison of accuracy, speed, and robustness with most state-of-the-art algorithms. Further, based on the precise pose estimation and tracking realized by our TAB-IOL, several formation control experiments are conducted for the group of fish-like robots. The results clearly demonstrate that our TAB-IOL lays a solid foundation for the coordination control of multiple fish-like robots in a real working environment. We believe our proposed method will facilitate the growth and development of related fields.

     

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  • 1 The supplementary movie is available online at https://ibdl.pku.edu.cn/research/video/926374.htm
    2 The built dataset has been publicly released on https://github.com/xjh19971/Robotic-Fish-Pose-Dataset.
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    Highlights

    • A method for pose estimation and tracking of multiple robotic fish via deep learning.
    • Our proposed method performs on line in real time, and better than existing methods.
    • A solid foundation for coordination control of multiple robotic fish in application.

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