Vision based autonomous docking for work class ROVs

Petar Trslic; Matija Rossi; Luke Robinson; Cathal W. O'Donnel; Anthony Weir; Joseph Coleman; James Riordan; Edin Omerdic; Gerard Dooly; Daniel Toal

doi:10.1016/j.oceaneng.2019.106840

Vision based autonomous docking for work class ROVs

Petar Trslic, Matija Rossi, Luke Robinson, Cathal W. O'Donnel, Anthony Weir, Joseph Coleman, James Riordan, Edin Omerdic, Gerard Dooly, Daniel Toal

University of Limerick

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	106840
Journal	Ocean Engineering
Volume	196
DOIs	https://doi.org/10.1016/j.oceaneng.2019.106840
Publication status	Published - 15 Jan 2020

Keywords

Autonomous docking
Computer vision
Resident ROV

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10.1016/j.oceaneng.2019.106840

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title = "Vision based autonomous docking for work class ROVs",

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.",

keywords = "Autonomous docking, Computer vision, Resident ROV",

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T1 - Vision based autonomous docking for work class ROVs

AU - Trslic, Petar

AU - Rossi, Matija

AU - Robinson, Luke

AU - O'Donnel, Cathal W.

AU - Weir, Anthony

AU - Coleman, Joseph

AU - Riordan, James

AU - Omerdic, Edin

AU - Dooly, Gerard

AU - Toal, Daniel

PY - 2020/1/15

Y1 - 2020/1/15

N2 - 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.

AB - 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.

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KW - Computer vision

KW - Resident ROV

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JO - Ocean Engineering

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ER -

Vision based autonomous docking for work class ROVs

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Keywords

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