Aquaculture Europe 2023

September 18 - 21, 2023


Add To Calendar 19/09/2023 14:30:0019/09/2023 14:45:00Europe/ViennaAquaculture Europe 2023CHANGE – AN UNDERWATER ROBOTICS CONCEPT FOR DYNAMICALLY CHANGING ENVIRONMENTSStolz 2The European Aquaculture Societywebmaster@aquaeas.orgfalseDD/MM/YYYYaaVZHLXMfzTRLzDrHmAi181982


Herman  B. Amundsen*,1,2, Eleni Kelasidi2 and Martin Føre1 ,

1Department of Engineering Cybernetics, NTNU, Norway

2Department of Aquaculture Technology, SINTEF Ocean, Norway

*E-mail :



 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 [1]. 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 [2].

Materials and methods

The elastic band method has been a suggested method for planning collision-free paths [3] 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 [4] 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.


[1]                        Kelasidi, E., Svendsen, E. (2023).  Robotics for Sea-Based Fish Farming. In: Zhang, Q. (eds) Encyclopedia of Smart Agriculture Technologies. Springer, Cham.\_202-1

[2]                        CHANGE  ̶ An Underwater Robotics Concept for Dynamically Changing Environments.

[3]                        Khatib, O. (1985). Real-time obstacle avoidance for manipulators and mobile robots. In Proc. IEEE International Conference on Robotics and Automation, pages 500–505.

 [4]                        Reite, K.-J., Føre, M., Aarsæther, K. G., Jensen, J., Rundtop, P., Kyllingstad, L. T., Endresen, P. C.,  Kristiansen, D., Johansen, V., and Fredheim, A. (2014). FhSim - time domain simulations of marine

systems. In Proc. ASME 33rd International Conference on Ocean, Offshore and Arctic Engineering.