Abstract
Localization of underwater vehicles, namely remotely operated vehicles (ROVs), used in mariculture autonomous inspection applications represents a challenging problem. The need for accurate localization of an ROV is further emphasized by often cluttered underwater environment of fisheries with many ropes and mooring around the net pens potentially causing entanglement with ROV’s tether. This paper presents an overview and preliminary results of the HEKTOR (Heterogeneous Autonomous Robotic System in Viticulture and Mariculture) project regarding the ROV localization using acoustics mounted onto an autonomous surface vehicle (ASV). The ROV and the developed ASV are described, together with the hardware and software integration of a short baseline acoustic localization system . Preliminary sea trial results show promising performance of the localization system, proving that it could be used in autonomous net pen inspection missions.
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Acknowledgements
Research work presented in this article has been supported by the project Heterogeneous autonomous robotic system in viticulture and mariculture (HEKTOR) financed by the European Union through the European Regional Development Fund-The Competitiveness and Cohesion Operational Programme (KK.01.1.1.04.0036); the ERDF-funded project (KK.01.1.1. 07.0069)
“Multifunkcionalne pametne bove (Multifunctional smart buoys)”; the European Regional Development Fund through the Interreg Italy-Croatia InnovaMare project (Partnership ID 10248782); and by the “Razvoj autonomnog besposadnog višenamjenskog broda” project (KK.01.2.1.02.0342) co-financed by the European Union from the European Regional Development Fund within the Operational Program “Competitiveness and Cohesion 2014–2020”. The content of the publication is the sole responsibility of the project partner UNIZG-FER.
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Kapetanović, N., Nađ, Đ., Lončar, I., Slošić, V., Mišković, N. (2023). Acoustical Underwater Localization of a Remotely Operated Vehicle in Mariculture. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-031-22216-0_54
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