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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: [email protected]
Zhaoliang Wan
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Hai Huang*
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Guocheng Zhang
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Xuan Bao
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Jiyong Li
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Mingwei Sheng
Affiliation:
National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin150001, China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Xu Yang
Affiliation:
State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Science, Beijing100190, China. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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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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