As salmon farm sites are moved further offshore and to more exposed locations, working conditions become increasingly challenging. Farmers therefore aim to automate certain operations to facilitate safer working conditions. Automation and autonomous unmanned underwater vehicles (UUVs) are key elements in meeting this goal, and will contribute to increasing precision in finfish farming operations that in turn will enable the aquaculture industry to advance operational efficiency, safety and thus sustainability . In this paper , an advanced control scheme for UUVs operating in complex environments has been investigated. The proposed scheme is suited for enabling verifiable collision-free navigation in dynamically changing environments. During demonstrations the UUVs were successful in autonomous navigation while successfully avoiding both static and moving obstacles.
This work was financed by the Research Council of Norway through the project: CHANGE ̶ An underwater robotics concept for dynamically changing environments .
Materials and methods
The elastic band method has been a suggested method for planning collision-free paths  and was included in an adapted guidance, navigation, and control (GNC) architecture shown in Figure 1. The guidance system featured the elastic band path planner and a guidance law. Waypoints and p ositions of obstacles and the vehicle were used to calculate the control system reference signals. The low-level control system then used these signals and feedback of the vehicle states to calculate the control input for each thruster . Vehicle states were estimated using a n Extended Kalman Filter (EKF ) based on sensor readings.
Extensive simulation results were obtained using FhSim  as shown in Figure 2 . In addition, lab and field trials were conducted in the NTNU Marine Cybernetics Lab (MCLab ) and the SINTEF ACE full-scale aquaculture laboratory to investigate the performance of the proposed control scheme for obstacle avoidance of UUVs in fish farms. Figure 3 and Figure 4 show some demonstrated case studies with a BlueROV2 vehicle from MCLab and field trials using an Argus Mini in an industrial scale fish farm at SINTEF ACE, respectively. All simulations, lab and field trials showed that the robot was able to avoid both static and moving obstacles during autonomous navigation of UUVs. The results demonstrate that the proposed method worked well at obstacle avoidance, and suggest that the elastic band method is a viable method for underwater collision avoidance in dynamically changing environments.
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 lack 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. The proposed methods and demonstrations show the great potential towards increasing the level of autonomous during daily operations in fish farms.
 Kelasidi, E., Svendsen, E. (2023). Robotics for Sea-Based Fish Farming. In: Zhang, Q. (eds) Encyclopedia of Smart Agriculture Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-89123-7\_202-1
 CHANGE ̶ An Underwater Robotics Concept for Dynamically Changing Environments. https://www.sintef.no/en/projects/2021/change-an-underwater-robotics-concept-for-dynamically-changing-environments/
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