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Intelligent Target Visual Tracking and Control Strategy for Open Frame Underwater Vehicles

Published online by Cambridge University Press:  23 February 2021

Chaoyu Sun ,
Zhaoliang Wan ,
Hai Huang Open the ORCID record for Hai Huang [Opens in a new window] ,
Guocheng Zhang ,
Xuan Bao ,
Jiyong Li ,
Mingwei Sheng  and
Xu Yang Open the ORCID record for Xu Yang [Opens in a new window]
Chaoyu Sun
Affiliation:
Hospital of Harbin Medical University, Harbin150026, China. E-mail: sary_1023@126.com
Zhaoliang Wan
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: wanzhaoliang@hrbeu.edu.cn, zhangguocheng168@sina.com, 928755274@qq.com, lijiyong@hrbeu.edu.cn, smwsky@hrbeu.edu.cn
Hai Huang*
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: wanzhaoliang@hrbeu.edu.cn, zhangguocheng168@sina.com, 928755274@qq.com, lijiyong@hrbeu.edu.cn, smwsky@hrbeu.edu.cn
Guocheng Zhang
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: wanzhaoliang@hrbeu.edu.cn, zhangguocheng168@sina.com, 928755274@qq.com, lijiyong@hrbeu.edu.cn, smwsky@hrbeu.edu.cn
Xuan Bao
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: wanzhaoliang@hrbeu.edu.cn, zhangguocheng168@sina.com, 928755274@qq.com, lijiyong@hrbeu.edu.cn, smwsky@hrbeu.edu.cn
Jiyong Li
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: wanzhaoliang@hrbeu.edu.cn, zhangguocheng168@sina.com, 928755274@qq.com, lijiyong@hrbeu.edu.cn, smwsky@hrbeu.edu.cn
Mingwei Sheng
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: wanzhaoliang@hrbeu.edu.cn, zhangguocheng168@sina.com, 928755274@qq.com, lijiyong@hrbeu.edu.cn, smwsky@hrbeu.edu.cn
Xu Yang
Affiliation:
State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Science, Beijing100190, China. E-mail: xu.yang@ia.ac.cn
*
*Corresponding author. E-mail: haihus@163.com
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Summary

Visual tracking is an essential building block for target tracking and capture of the underwater vehicles. On the basis of remotely autonomous control architecture, this paper has proposed an improved kernelized correlation filter (KCF) tracker and a novel fuzzy controller. The model is trained to learn an online correlation filter from a plenty of positive and negative training samples. In order to overcome the influence from occlusion, the improved KCF tracker has been designed with an added self-discrimination mechanism based on system confidence uncertainty. The novel fuzzy logic tracking controller can automatically generate and optimize fuzzy rules. Through Q-learning algorithm, the fuzzy rules are acquired through the estimating value of each state action pairs. An S surface based fitness function has been designed for the improvement of learning based particle swarm optimization. Tank and channel experiments have been carried out to verify the proposed tracker and controller through pipe tracking and target grasp on the basis of designed open frame underwater vehicle.

Keywords

Remotely autonomous underwater vehicleKernelized correlation filter trackingLearning based fuzzy controller
Type
Article
Information
Robotica , Volume 39 , Issue 10 , October 2021 , pp. 1791 - 1805
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Shi, J.-g., Li, D.-j. and Yang, C.-j., “Design and analysis of an underwater inductive coupling power transfer system for autonomous underwater vehicle docking applications,” J. Zhejiang Univ. Sci. C (Comput. Electron.) 15(1), 51–62 (2014).CrossRefGoogle Scholar
Li, Y., Cui, R., Li, Z. and Xu, D., “Neural network approximation based near-optimal motion planning with kinodynamic constraints using RRT,” IEEE Trans. Ind. Electron. 65(11), 8718–8729 (2018).CrossRefGoogle Scholar
Peymani, E. and Fossen, T. I., “Path following of underwater robots using lagrange multipliers,” Rob. Auton. Syst. 67, 44–52 (2015).CrossRefGoogle Scholar
Qin, H., Chen, H. and Sun, Y., “Distributed finite-time fault-tolerant containment control for multiple Ocean Bottom Flying Nodes,” J. Franklin Inst. (2019). https://doi.org/10.1016/j.jfranklin.2019.05.034.CrossRefGoogle Scholar
Ferri, G., Munafò, A. and LePage, K. D., “An autonomous underwater vehicle data-driven control strategy for target tracking,” IEEE J. Oceanic Eng. 43(2), 323–343 (2018).CrossRefGoogle Scholar
Villarl, S. A., Acosta, G. G., Sousa, A. L. and Rozenfeld, A., “Evaluation of an efficient approach for target tracking from acoustic imagery for the perception system of an autonomous underwater vehicle,” Int. J. Adv. Rob. Syst. 11(1), 1–13 (2014).Google Scholar
Lapierre, L. and Jouvencel, B., “Robust non-linear path-following control of an AUV,” IEEE J. Oceanic Eng. 33(2), 89–92 (2008).CrossRefGoogle Scholar
Gao, J., “Transfer Learning Based Visual Tracking with Gaussian Processes Regression,” Proceedings of European Conference on Computer Vision (2014) pp. 188–203.Google Scholar
Hare, S., “Struck: Structured Output Tracking with Kernels,” Proceedings of International Conference on Computer Vision (2011) pp. 263–270.Google Scholar
Babenko, B., “Robust object tracking with online multiple instance learning,” IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)CrossRefGoogle ScholarPubMed
Hong, S., “Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network,” Proceedings of International Conference on Machine Learning (2015) pp. 597–606.Google Scholar
Wang, L., “Stct: Sequentially Training Convolutional Networks for Visual Tracking,” Proceedings of Conference on Computer Vision and Pattern Recognition (2016) pp. 1373–1381.Google Scholar
Wang, L., “Visual Tracking with Fully Convolutional Networks,” Proceedings of International Conference on Computer Vision (2015) pp. 3119–3127.Google Scholar
Sun, F. H., “Stability control for the head of a biomimetic robotic fish with embedded vision,” Robot. 37(2), 188–203 (2015).Google Scholar
Bolme, D., “Visual Object Tracking Using Adaptive Correlation Filters,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2010) pp. 2544–2550.Google Scholar
Henriques, J., “Exploiting the Circulant Structure of Tracking-by-Detection with Kernels,” Proceedings of 12th European Conference on Computer Vision (2012) pp. 702–715.Google Scholar
Huang, R. J., “Applying Convolutional Networks to Underwater Tracking without Training,” Proceedings of IEEE International Conference on Applied System Innovation 2018 (2018) pp. 342–345.Google Scholar
Subudhi, B., Mukherjee, K. and Ghosh, S., “A static output feedback control design for path following of autonomous underwater vehicle in vertical plane,” Ocean Eng. 63, 72–76 (2013).CrossRefGoogle Scholar
Qi, X., “Spatial target path following control based on Nussbaum gain method for underactuated underwater vehicle,” Ocean Eng. 104, 680–685 (2015).CrossRefGoogle Scholar
Shen, C., Buckham, B. and Shi, Y., “Modified C/GMRES algorithm for fast nonlinear modeln predictive tracking control of AUVs,” IEEE Trans.Control Syst. Technol. 25(5), 1896–1904 (2017).CrossRefGoogle Scholar
Karkoub, M., Wu, H.-M. and Hwang, C.-L., “Nonlinear trajectory-tracking control of an autonomous underwater vehicle,” Ocean Eng. 145, 188–198 (2017).CrossRefGoogle Scholar
Elmokadema, T., Zribia, M. and Youcef-Toumi, K., “Terminal sliding mode control for the trajectory tracking of underactuated autonomous underwater vehicles,” Ocean Eng. 129, 613–625 (2017).CrossRefGoogle Scholar
Chu, Z., Zhu, D. and Yang, S. X., “Observer-based adaptive neural network trajectory tracking control for remotely operated vehicle,” IEEE Trans. Neural Networks Learn. Syst. 28(7), 1633–1645 (2017).CrossRefGoogle ScholarPubMed
Peng, Z. and Wang, J., “Output-feedback path-following control of autonomous underwater vehicles based on an extended state observer and projection neural networks,” IEEE Trans. Syst. Man Cybern. Syst. (Accepted).Google Scholar
Harik, E. H. C., Guérinc, F., Guinand, F., Brethé, J.-F., Pelvillan, H. and Parédé, J.-Y., “Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle,” Adv. Rob. 31(7), 368–381 (2017).CrossRefGoogle Scholar
Galceran, E., Campos, R., Palomeras, N., Ribas, D., Carreras, M. and Ridao, P., “Coverage path planning with real-time replanning and surface reconstruction for inspection of three-dimensional underwater structures using autonomous underwater vehicles,” J. Field Rob. 32(7), 952–983 (2015).CrossRefGoogle Scholar
Carreras, M., Yuh, J., Batlle, J. and Ridao, P., “A behavior-based scheme using reinforcement learning for autonomous underwater vehicles,” IEEE J. Oceanic Eng. 30(2), 416–427 (2005).CrossRefGoogle Scholar
Hu, Y., Zhao, W., Xie, G. and Wang, L., “Development and target following of vision-based autonomous robotic fish,” Robotica 27, 1075–1089 (2009).CrossRefGoogle Scholar
Davis, P., Circulant Matrices (American Mathematical Society, New York, 1979).Google Scholar
Hai, H., Hao, Z., Xu, Y., Lu, Z., Qi, L. and Zang, A.-Y., “Faster R-CNN for marine organisms detection and recognition using data augmentation,” Neurocomputing 337, 372–384 (2019).Google Scholar
Henriques, J. F., “High-speed tracking with kernelized correlation filters,” IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015).CrossRefGoogle ScholarPubMed