Trslic_2020_Neuro.pdf (3.59 MB)
Neuro-Fuzzy dynamic position prediction for autonomous work-class ROV docking
journal contribution
posted on 2020-02-26, 10:34 authored by Petar Trslić, Edin Omerdić, Gerard Dooly, DANIEL TOALDANIEL TOALThis paper presents a docking station heave motion prediction method for dynamic
remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the heave motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate heave motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station heave motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system.
Funding
Study on Aerodynamic Characteristics Control of Slender Body Using Active Flow Control Technique
Japan Society for the Promotion of Science
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Publication
Sensors;20, 693Publisher
MDPINote
peer-reviewedOther Funding information
SFI, Marine Institute Ireland, ERC, MaREI Research CentresLanguage
EnglishExternal identifier
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