Abstract
This 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.
Highlights
In recent years, operations undertaken by unmanned underwater vehicles (UUVs) in the offshore energy sector are changing rapidly
Once the network is trained, the performance is reduced if certain launch and recovery system (LARS), tether management system (TMS), and deployment vessel combination are changed
This paper presents a suspended TMS depth prediction method for remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)
Summary
Operations undertaken by unmanned underwater vehicles (UUVs) in the offshore energy sector are changing rapidly. Oceaneering developed E-ROV [3], a battery-powered, self-contained, work-class remotely operated vehicle (ROV), whereas IKM developed a fully electric R-ROV based on electric work-class ROV Merlin [4] Such systems include a permanently deployed docking station which serves as a charging point, download/upload data link, and as mechanical protection for the resident vehicle [5]. Within the O&G, and especially the offshore wind production field, multiple assets can be spread across more than 100 km , which need to be continuously inspected for condition monitoring purposes This has been partially addressed through the development of resident autonomous underwater vehicles (AUV) [6,7]. To allow for autonomous work-class ROV docking in higher sea states a TMS heave motion prediction method has been developed. The ANFIS performance is evaluated on a real-world dataset recorded using a work-class ROV with corresponding cage type TMS, deployed during offshore trials in the North Atlantic Ocean
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