Introduction
Aquaculture is an important global contributor to the production of seafood for human consumption. However, this industry is also known for a substantial HSE risks and a high frequency of work-related injuries. Consequently, increasing the automation level of high-risk operations within aquaculture could lead to economical as well as social and ethical benefits. Increased automation can contribute to improving the level of control humans have over aquaculture operations by facilitating increased use of technological solutions and unmanned underwater vehicles (UUVs) [1] . To target these challenges, the newly established SINTEF ACE-RoboticLab aims to be the pioneer for developing both basic and applied research on autonomy and robotics with the overall target being to contribute increasing efficiency and objectivity during daily operations in fish farms, and thus provide solutions that will contribute to addressing the current challenges in the aquaculture domain (Figure 1).
Materials and methods
Research on autonomy and robotic solutions in SINTEF Ocean targets the development of dedicated solutions for the aquaculture industry and thus aims to address the industry’s challenges for optimal and sustainable production. Therefore, as a part of the SINTEF ACE infrastructure hub, SINTEF Ocean recently established the SINTEF ACE-RoboticLab which aims to create innovative tools and autonomous solutions which expand the ability of the aquaculture industry and improve precision and efficiency in the aquaculture segment. To accomplish the vision of the lab, we pursue projects focused on autonomous and robotised solutions applied to the aquaculture industry [2] . Our team’s vision has targeted research and solutions for autonomous intelligent systems capable of long-term autonomous operations in unknown, complex and dynamically changing environments in the aquaculture industry.
One of the main goals of SINTEF ACE-RoboticLab is to introduce and develop innovative solutions that will be able to operate in any environment and in interaction with the biomass under any possible conditions, while performing demanding aquaculture operations [3]. The SINTEF ACE-RoboticLab research group addresses the challenges related to the development of autonomous technological solutions and control of systems that adapt to different situations and cope with an uncertain and dynamically changing environment. With a large focus on interdisciplinary research linking the fields of technology and biology, we perform fundamental research on the modelling of UUVs, aquaculture structures, fish behaviour and environmental disturbances, and on advanced control strategies for autonomous navigation and exploration of unmanned underwater and surface vehicles operating in dynamically changing environments.
Results
Through variety of projects,
we developed methods and robotic solutions for autonomous operations in fish farms (to name few of them: CHANGE, ResiFarm , NetClean 24/7, Autosmolt2025, RACE-Fish Machine Interaction, RACE Digital Cage, SalmonInsight , Indisal , Crowdquard , CageReporter , Artifex, Bioracer , Sensordrone , MerdROV and several others). We own and use a variety of robotic systems including aerial, surface and underwater vehicles. In addition, the group contributes to the development of new, dedicated robotic systems suitable for operating in fish farms. Aquaculture Robotic Simulation Framework has also been developed in our group based on the FhSim – Simulation of Marine Operations and Systems, which features a large collection of mathematical models including ships, fishing trawl systems, aquaculture net cages, closed containment systems, fish behaviour and energetics, feeding systems, remotely operated vehicles (ROVs)
[2 ]. We have a very close collaboration with the SINTEF ACE – Facilities and resources for Precision Fish Farming (PFF), and technology for better fish welfare to secure developing technological solutions that match the biological perspectives. Several autonomous features have been demonstrated in industrial scale fish farms (Figure 2-3).
Conclusion and future work
Management of sea-based fish farms typically entails manual, and often challenging, inspection operations to monitor equipment, structures and biomass, which may
result in sub-optimal and costly operations, insufficient maintenance, a general lac k of control in daily routines and potential high risks for welfare of personnel and
fish. This implies a need for new methods and technology for operations in modern
fish farms, especially when moving operations to more exposed locations with more
challenging environmental conditions, and new farm designs. Therefore , the goal of establishing the SINTEF ACE – RoboticLab is to contribute to making operations in the aquaculture industry more efficient by using different types of autonomous concepts and robotic systems. We aim to develop new knowledge and technology for the underwater robotics systems of the future, where these systems must coexist and cooperate with living fish and flexible structures during operations.
By incorporating biological parameters (i.e. fish behaviour) into the mathematical models, this interdisciplinary approach will provide a foundation for the resident underwater robotics systems of the future and thus enable them to operate in adaptation to live fish. This impact will be expressed both in the short term by enabling new research activities, and in the long term by resulting in innovations that help industries to improve the safety and efficiency of using robotics systems in demanding operations.
References
[1] Føre, M., et. al., 2018. Precision fish farming: A new framework to improve production in aquaculture, Biosystems Engineering 173, Pages 176-193.
[2] H. B. Amundsen, "Robust Nonlinear ROV Motion Control for Autonomous Inspections of Aquaculture Net Pens," NTNU, 2020.
[3] Su B, et. al., (2019) A multipurpose framework for modelling and simulation of marine aquaculture systems. In: International Conference on Offshore Mechanics and Arctic Engineering, American Society of Mechanical Engineers, vol 58837, p V006T05A002