Vision based autonomous docking for work class ROVs. (15th January 2020)

• Record Type: Journal Article
• Authors: Trslic, Petar; Rossi, Matija; Robinson, Luke; O'Donnel, Cathal W.; Weir, Anthony; Coleman, Joseph; Riordan, James; Omerdic, Edin; Dooly, Gerard; Toal, Daniel
• Is Part Of: Ocean engineering. Volume 196(2020)
• Publication Date: 2020-01-15
• Subjects: Resident ROV -- Autonomous docking -- Computer vision; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2019.106840
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: This paper presents autonomous docking of an industry standard work-class ROV to both static and dynamic docking station (Tether Management System — TMS) using visual based pose estimation techniques. This is the first time autonomous docking to a dynamic docking station has been presented. Furthermore, the presented system does not require a specially designed docking station but uses a conventional cage type TMS. The paper presents and discusses real-world environmental tests successfully completed during January 2019 in the North Atlantic Ocean. To validate the performance of the system, a commercial state of the art underwater navigation system has been used. The results demonstrate a significant advancement in resident ROV automation and capabilities, and represents a system which can be retrofitted to the current ROV fleet. Highlights: Autonomous docking of a work class ROV to a suspended TMS has been presented. The system has been tested during January 2019. in North Atlantic Ocean. The system has been validated using state of the art underwater navigation system. The results demonstrate significant advancement in resident ROV automation.