LSTS Toolchain Framework for Deep Learning Implementation into Autonomous Underwater Vehicle

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Aubard, M (Author) Other; Madureira, A (Author) Other; Madureira, L (Author) Other; Campos, R (Author) Other; Conceicao Calhau (Author) Other; Pinto, J (Author) Other; João Tasso Sousa (Author) FEUP

Conference proceedings International

Title: OCEANS 2023 - LIMERICK

OCEANS Conference

Limerick, IRELAND, JUN 05-08, 2023

Indexing

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Other information

Authenticus ID: P-00Z-5QY

DOI: 10.1109/oceanslimerick52467.2023.10244721

Abstract (EN): The development of increasingly autonomous underwater vehicles has long been a research focus in underwater robotics. Recent advances in deep learning have shown promising results, offering the potential for fully autonomous behavior in underwater vehicles. However, its implementation requires improvements to the current vehicles. This paper proposes an onboard data processing framework for Deep Learning implementation. The proposed framework aims to increase the autonomy of the vehicles by allowing them to interact with their environment in real time, enabling real-time detection, control, and navigation.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6