Automatic inspection of subsea optical cable by an autonomous underwater vehicle

The changes of the seafloor environment caused by natural disasters and human activities greatly shorten the life span of the subsea optical cable. It is urgent to carry out routine inspection and maintenance for subsea cables. In this paper, an automatic inspection system to address these problems is proposed where the localization and inspection mission is conducted by an autonomous underwater vehicle (AUV) carrying with a tri-axial electromagnetic sensor. Firstly, the framework of the inspection system is presented, and the function of each subsystem is introduced briefly. Secondly, the inspection algorithms are designed which include localization algorithm, online path planning and path following control algorithms. A dedicated particle swarm optimization (PSO) algorithm is adopted to localize the subsea optical cable. In addition, the swath path is planned online so that the AUV can detect the cable in the repeatedly crossing manner. With the planned of swath path, the cable detection is elaborately constructed as a classic path following control problem, such that the AUV can track the planned path and inspect the optical cable automatically. Finally, the numerical simulation results are provided to validate the effectiveness and feasibility of the automatic inspection system.

[1]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[2]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[3]  Yoichi Kato,et al.  Experimental results of Autonomous Underwater Vehicle 'AQUA EXPLORER 2' for inspection of underwater cables , 1998, IEEE Oceanic Engineering Society. OCEANS'98. Conference Proceedings (Cat. No.98CH36259).

[4]  S. Cowls,et al.  The enhancement and verification of a pulse induction based buried pipe and cable survey system , 2002, OCEANS '02 MTS/IEEE.

[5]  Roger Skjetne,et al.  Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory , 2005, Autom..

[6]  Wen-Miin Tian,et al.  Integrated method for the detection and location of underwater pipelines , 2008 .

[7]  Khac Duc Do,et al.  Control of Ships and Underwater Vehicles: Design for Underactuated and Nonlinear Marine Systems , 2009 .

[8]  Gabriel Oliver,et al.  A Bayesian approach for tracking undersea narrow telecommunication cables , 2009, OCEANS 2009-EUROPE.

[9]  Gabriel Oliver,et al.  A particle filter-based approach for tracking undersea narrow telecommunication cables , 2011, Machine Vision and Applications.

[10]  Yan Pailhas,et al.  Detection of buried and partially buried objects using a bio-inspired wideband sonar , 2010, OCEANS'10 IEEE SYDNEY.

[11]  B. Jouvencel,et al.  3D Reconstruction of seabed surface through sonar data of AUVs , 2012 .

[12]  Sanjay Sharma,et al.  Developments in subsea power and telecommunication cables detection: Part 1 – Visual and hydroacoustic tracking , 2013 .

[13]  Bruno Jouvencel,et al.  Smooth transition of AUV motion control: From fully-actuated to under-actuated configuration , 2015, Robotics Auton. Syst..

[14]  Caoyang Yu,et al.  Motion forecast of intelligent underwater sampling apparatus —— Part II: CFD simulation and experimental results , 2015 .

[15]  Caoyang Yu,et al.  Motion forecast of intelligent underwater sampling apparatus —— Part I: Design and algorithm , 2015 .

[16]  Leigh McCue,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control [Bookshelf] , 2016, IEEE Control Systems.

[17]  Qin Zhang,et al.  Path-Following Control of an AUV: Fully Actuated Versus Under-actuated Configuration , 2016 .

[18]  Caoyang Yu,et al.  Subsea Cable Tracking by Autonomous Underwater Vehicle with Magnetic Sensing Guidance , 2016, Sensors.

[19]  Weidong Zhang,et al.  Robust Neural Control for Dynamic Positioning Ships With the Optimum-Seeking Guidance , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[20]  Caoyang Yu,et al.  Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties , 2017, Comput. Oper. Res..

[21]  Qin Zhang,et al.  On intelligent risk analysis and critical decision of underwater robotic vehicle , 2017 .