033 | Innovative autonomous surface vehicles for enhanced welfare and continuous monitoring in sustainable aquaculture

Alex Carnevale¹, Ayman El Qemmah², Rosario Sorbello³, Alessia Avolicino¹ | ¹DIBEST, Università della Calabria; ²DIMES, Università della Calabria; ³Dipartimento di Ingegneria Industriale e dell’Informazione, Università eCampus, Italy.

Abstract

The present contribution highlights the potential of advanced Autonomous Surface Vehicles (ASVs) as a strategic technological asset for improving sustainability, operational efficiency, and welfare standards in modern aquaculture. The proposed approach introduces an electric, omnidirectional ASV platform designed to operate with a negligible acoustic footprint and zero emissions, ensuring a non-invasive presence that minimizes stress on aquatic organisms and reduces environmental impact. Within an integrated farm management perspective, the primary aim of this technology is to support operators and enhance operational efficiency by simplifying current monitoring techniques. This advanced tool complements established practices, extending surveillance capabilities to a continuous 24/7 basis, which is often logistically challenging to achieve using human resources alone. The vehicle is equipped with a comprehensive and versatile sensor suite, together with advanced guidance, navigation, and control architecture, enabling precise maneuverability in constrained environments, obstacle detection, and collision avoidance, thereby ensuring the safety of both infrastructure and biological assets. The performance of the platform is supported by a solid technical background and previous experimental validations in aquatic environments, which have demonstrated its reliability and its ability to perform autonomous tasks such as visual census with high accuracy. Furthermore, the system holds significant potential for additional applications, such as autonomous net inspection. The integration of traditional monitoring techniques and advanced autonomous tools enables superior outcomes compared to the use of single methodologies. In particular, combining direct human observation, diver-based inspections, and continuous ASV-based surveillance provides a more comprehensive and timely understanding of farm conditions and stock health. This integrated approach supports early anomaly detection, the maintenance of optimal rearing conditions, and an overall improvement in fish welfare, while preserving and enhancing the effectiveness of established operational practices.