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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.
  • [1]
    E. W. Hawkes, L. H. Blumenschein, J. D. Greer, and A. M. Okamura, “A soft robot that navigates its environment through growth,” Science Robotics, vol. 2, no. 8, Article No. eaan3028, 2017.
    L. Li, A. Liu, W. Wang, S. Ravi, R. Fu, J. Yu, and G. Xie, “Bottomlevel motion control for robotic fish to swim in groups: Modeling and experiments,” Bioinspiration &Biomimetics, vol. 14, no. 4, Article No. 046001, 2019.
    L. Li, M. Nagy, J. M. Graving, J. Bak-Coleman, G. Xie, and I. D. Couzin, “Vortex phase matching as a strategy for schooling in robots and in fish,” Nature Communications, vol. 11, no. 1, pp. 1–9, 2020. doi: 10.1038/s41467-019-13993-7
    S. Butail, T. Bartolini, and M. Porfiri, “Collective response of zebrafish shoals to a free-swimming robotic fish,” PLoS One, vol. 8, no. 10, Article No. e76123, 2013. doi: 10.1371/journal.pone.0076123
    F. Bonnet, Y. Kato, J. Halloy, and F. Mondada, “Infiltrating the zebrafish swarm: Design, implementation and experimental tests of a miniature robotic fish lure for fish–robot interaction studies,” Artificial Life and Robotics, vol. 21, no. 3, pp. 239–246, 2016. doi: 10.1007/s10015-016-0291-8
    C. Wang, X. Chen, G. Xie, and M. Cao, “Emergence of leadership in a robotic fish group under diverging individual personality traits,” Royal Society Open Science, vol. 4, no. 5, Article No. 161015, 2017. doi: 10.1098/rsos.161015
    J. Yuan, J. Yu, Z. Wu, and M. Tan, “Precise planar motion measurement of a swimming multi-joint robotic fish,” Science China Information Sciences, vol. 59, no. 9, pp. 1–15, 2016.
    K. Terayama, H. Habe, and M.-A. Sakagami, “Multiple fish tracking with an NACA airfoil model for collective behavior analysis,” IPSJ Trans. Computer Vision and Applications, vol. 8, no. 1, pp. 1–7, 2016. doi: 10.1186/s41074-016-0002-3
    Y. Ma, J. Kosecka, and S. S. Sastry, “Vision guided navigation for a nonholonomic mobile robot,” IEEE Trans. Robotics and Automation, vol. 15, no. 3, pp. 521–536, 1999. doi: 10.1109/70.768184
    A. Phillips, “Robot fish: Bio-inspired fishlike underwater robots,” Underwater Technology, vol. 34, no. 3, pp. 143–145, 2017.
    A. Pérez-Escudero, J. Vicente-Page, R. C. Hinz, S. Arganda, and G. G. De Polavieja, “idTracker: Tracking individuals in a group by automatic identification of unmarked animals,” Nature Methods, vol. 11, no. 7, pp. 743–748, 2014. doi: 10.1038/nmeth.2994
    R. K. Katzschmann, A. D. Marchese, and D. Rus, “Hydraulic autonomous soft robotic fish for 3D swimming,” in Experimental Robotics, Cham, Germany: Springer, 2016, pp. 405–420.
    X. Tan, D. Kim, N. Usher, D. Laboy, J. Jackson, A. Kapetanovic, J. Rapai, B. Sabadus, and X. Zhou, “An autonomous robotic fish for mobile sensing,” in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems. IEEE, 2006, pp. 5424–5429.
    W. Wang and G. Xie, “Online high-precision probabilistic localization of robotic fish using visual and inertial cues,” IEEE Trans. Industrial Electronics, vol. 62, no. 2, pp. 1113–1124, 2014.
    M. Penmetcha, S. Luo, A. Samantaray, J. E. Dietz, B. Yang, and B.-C. Min, “Computer vision-based algae removal planner for multi-robot teams,” in Proc. IEEE Int. Conf. Systems, Man and Cybernetics (SMC). IEEE, 2019, pp. 1575–1581.
    Y. Liu, Z. Meng, Y. Zou, and M. Cao, “Visual object tracking and servoing control of a nano-scale quadrotor: System, algorithms, and experiments,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 2, pp. 344–360, 2020.
    Z. Zhang, T. Yang, T. Zhang, F. Zhou, N. Cen, T. Li, and G. Xie, “Global vision-based formation control of soft robotic fish swarm,” Soft Robotics, vol. 8, no. 3, pp. 310–318, 2021.
    S. Butail, N. Abaid, S. Macrì, and M. Porfiri, “Fish–robot interactions: robot fish in animal behavioral studies,” in Robot Fish, Berlin, Heidelberg: Springer, 2015, pp. 359–377.
    M. Porez, F. Boyer, and A. J. Ijspeert, “Improved lighthill fish swimming model for bio-inspired robots: Modeling, computational aspects and experimental comparisons,” Int. J. Robotics Research, vol. 33, no. 10, pp. 1322–1341, 2014. doi: 10.1177/0278364914525811
    E. Fontaine, D. Lentink, S. Kranenbarg, U. K. Müller, J. L. van Leeuwen, A. H. Barr, and J. W. Burdick, “Automated visual tracking for studying the ontogeny of zebrafish swimming,” J. Experimental Biology, vol. 211, no. 8, pp. 1305–1316, 2008. doi: 10.1242/jeb.010272
    L. Shao and G. Xie, “Real-time tracking of moving objects on a water surface,” in Proc. IEEE Int. Conf. Mechatronics and Automation, 2012, pp. 2114–2119.
    I. Ahmed, S. Din, G. Jeon, F. Piccialli, and G. Fortino, “Towards collaborative robotics in top view surveillance: A framework for multiple object tracking by detection using deep learning,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 7, pp. 1253–1270, Jul. 2021. doi: 10.1109/JAS.2020.1003453
    B. Xiao, H. Wu, and Y. Wei, “Simple baselines for human pose estimation and tracking,” in Proc. European Conf. Computer Vision (ECCV), 2018, pp. 466–481.
    Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime multi-person 2d pose estimation using part affinity fields,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2017, pp. 7291–7299.
    E. Insafutdinov, L. Pishchulin, B. Andres, M. Andriluka, and B. Schiele, “Deepercut: A deeper, stronger, and faster multi-person pose estimation model,” in Proc. European Conf. Computer Vision, Springer, 2016, pp. 34–50.
    A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M. Bethge, “DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning,” Nature Neuroscience, vol. 21, no. 9, pp. 1281–1289, 2018. doi: 10.1038/s41593-018-0209-y
    T. D. Pereira, D. E. Aldarondo, L. Willmore, M. Kislin, S. S.-H. Wang, M. Murthy, and J. W. Shaevitz, “Fast animal pose estimation using deep neural networks,” Nature Methods, vol. 16, no. 1, pp. 117–125, 2019. doi: 10.1038/s41592-018-0234-5
    J. Yu, C. Wang, and G. Xie, “Coordination of multiple robotic fish with applications to underwater robot competition,” IEEE Trans. Industrial Electronics, vol. 63, no. 2, pp. 1280–1288, 2015.
    C. Wang, G. Xie, L. Wang, and M. Cao, “CPG-based locomotion control of a robotic fish: using linear oscillators and reducing control parameters via PSO,” Int. Journal of Innovative Computing,Information and Control, vol. 7, no. 7, pp. 4237–4249, 2011.
    J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv preprint arXiv: 1804.02767, 2018.
    M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in Proc. Int. Conf. Machine Learning, PMLR, 2019, pp. 6105–6114.
    M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2018, pp. 4510–4520.
    M. Andriluka, U. Iqbal, E. Insafutdinov, L. Pishchulin, A. Milan, J. Gall, and B. Schiele, “Posetrack: A benchmark for human pose estimation and tracking,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2018, pp. 5167–5176.
    P. Voigtlaender, M. Krause, A. Osep, J. Luiten, B. B. G. Sekar, A. Geiger, and B. Leibe, “Mots: Multi-object tracking and segmentation,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2019, pp. 7942–7951.
    T. Mikolov, M. Karafiat, L. Burget, J. Černockỳ, and S. Khudanpur, “Recurrent neural network based language model,” in Proc. 11th Annual Conf. Int. Speech Communication Association, 2010.
    W. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent neural network regularization,” arXiv preprint arXiv: 1409.2329, 2014.
    A. Graves, “Generating sequences with recurrent neural networks,” arXiv preprint arXiv: 1308.0850, 2013.
    A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social lstm: Human trajectory prediction in crowded spaces,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2016, pp. 961–971.
    K. He, R. Girshick, and P. Dollár, “Rethinking imagenet pre-training,” in Proc. IEEE/CVF Int. Conf. Computer Vision, 2019, pp. 4918–4927.
    C. Wang and G. Xie, “Limit-cycle-based decoupled design of circle formation control with collision avoidance for anonymous agents in a plane,” IEEE Trans. Automatic Control, vol. 62, no. 12, pp. 6560–6567, 2017. doi: 10.1109/TAC.2017.2712758
    C. Wang, W. Xia, and G. Xie, “Limit-cycle-based design of formation control for mobile agents,” IEEE Trans. Automatic Control, vol. 65, no. 8, pp. 3530–3543, 2019.
    Z. Gao and G. Guo, “Fixed-time sliding mode formation control of auvs based on a disturbance observer,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 2, pp. 539–545, 2020. doi: 10.1109/JAS.2020.1003057
    Y. Zheng, Q. Zhao, J. Ma, and L. Wang, “Second-order consensus of hybrid multi-agent systems,” Systems &Control Letters, vol. 125, pp. 51–58, 2019.
    Y. Zheng, J. Ma, and L. Wang, “Consensus of hybrid multi-agent systems,” IEEE Trans. Neural Networks and Learning Systems, vol. 29, no. 4, pp. 1359–1365, 2017.
    T. Zhang, Y. Li, S. Li, Q. Ye, C. Wang, and G. Xie, “Decentralized circle formation control for fish-like robots in real-world via reinforcement learning,” in Proc. Int. Conf. Robotics and Automation (ICRA), IEEE, arXiv preprint arXiv: 2103.05293, 2021.


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    • 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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