The significant growth in the aquaculture industry over the last few decades encourages new technological and robotic solutions to help improve the efficiency and safety of production. In sea-based farming of Atlantic salmon in Norway, Unmanned Underwater Vehicles (UUVs) are already being used for inspection tasks. While new methods, systems and concepts for sub-sea operations are continuously being developed, these systems generally does not take into account how their presence might impact the fish. This abstract presents an experimental study on how underwater robotic operations at fish farms in Norway can affect farmed Atlantic salmon, and how the fish behaviour changes when exposed to the robot. The abstract provides an overview of the case study, the methods of analysis, and some preliminary results.
Remotely operated vehicles (ROVs) are often used for inspection in aquaculture net pens which serves the important purpose of localizing holes in the net and reporting potential irregularities and damages. Manual control of the vehicle inside a net pen, while simultaneously inspecting the net structure, is difficult and puts a lot of stress on the vehicle operators. Adaptation of new solutions that enables autonomous traversal of net pens where the vehicle maintains a fixed distance, heading, and velocity relative to the net is considered essential. One of the main challenges of such autonomous solutions is a robust and tight control of the vehicle's velocities. To target this challenge, this paper presents adaptive speed controllers for the surge and sway speeds of a remotely operated vehicle with unknown parameters and under the influence of unknown external disturbances. The stability properties of the controllers are proven through Lyapunov theory, and both simulations and field experiments demonstrate their ability to track the desired speeds through the use of a net following scheme.
This paper proposes methods to enable autonomous operation, specifically for localization and motion planning, of net grooming robots in aquaculture net pens and validates the proposed methods in both simulations and experimental fieldwork. Moreover, this paper suggests enabling uninterrupted operation by investigating the use of data from an inertial measurements unit that is a common sensor in underwater vehicles, rather than investing and upgrading to costly sensory systems that often require additional installation and calibration. In particular, the presented work consists of a localization method capable of estimating a robotic system's cylindrical position in an aquaculture net pen, a 3 DOF cylindrical robotic model, a method for path planning and collision avoidance, and a heading guidance and control system. The simulations demonstrate successful localization of the robotic system, while simultaneously planning and following collision-free trajectories in an environment obstructed by obstacles. Furthermore, the field trials successfully demonstrate that the system, when applied to net crawling robots, is capable of localization, path planning, and collision avoidance in an aquaculture setting. As follows, the presented work contributes to establishing net grooming robots as competitive candidates for biofouling management.
Aquaculture is a big marine industry and contributes to securing global food demands. Underwater vehicles such as remotely operated vehicles (ROVs) are commonly used for inspection, maintenance, and intervention (IMR) tasks in fish farms. However, underwater vehicle operations in aquaculture face several unique and demanding challenges, such as navigation in dynamically changing environments with time-varying sealoads and poor hydroacoustic sensor capabilities, challenges yet to be properly addressed in research. This paper will present various endeavors to address these questions and improve the overall autonomy level in aquaculture robotics, with a focus on field experiments. We will also discuss lessons learned during field trials and potential future prospects in aquaculture robotics.
This paper proposes a low-cost solution for localizing a remotely operated vehicle (ROV) inside a fish net pen. The solution consists of a kinematic Kalman Filter capable of estimating the absolute ROV position and orientation in a fish net pen using primarily the onboard compass, laser-camera triangulation, and a model of the cylindrical net pen. The solution is demonstrated in a real fish net pen, under realistic operating conditions, and the performance is comparable to that of specialized positioning sensor systems such as ultra short baseline systems and Doppler velocity loggers.
Aquaculture net cage inspection and maintenance is a central issue in fish farming. Inspection using autonomous underwater vehicles is a promising solution. This paper proposes laser-camera triangulation for pose estimation to enable autonomous net following for an autonomous vehicle. The laser triangulation 3D data is experimentally compared to a doppler velocity log (DVL) in an active fish farm. We show that our system is comparable in performance to a DVL for distance and angular pose measurements. Laser triangulation is promising as a short distance ranging sensor for autonomous vehicles at a low cost compared to acoustic sensors.
This article presents a method for guiding a remotely operated vehicle (ROV) to autonomously traverse an aquaculture net pen. The method is based on measurements from a Doppler velocity log (DVL) and uses the measured length of the DVL beam vectors to approximate the geometry of a local region of the net pen in front of the ROV. The ROV position and orientation relative to this net pen approximation are used as inputs to a nonlinear guidance law. The guidance law is based upon the line-of-sight (LOS) guidance law. By utilizing that an ROV is fully actuated in the horizontal plane, the crosstrack error is minimized independently of the ROV heading. A Lyapunov analysis of the closed-loop system with this guidance law shows that the ROV is able to follow a continuous path in the presence of a constant irrotational ocean current. Finally, results from simulations and experiments demonstrating the performance of the net pen approximation and control system are presented.
The aquaculture industry is a very important provider of food and employment worldwide. This industry faces many challenges with one of the problems being the growth of biofouling on the net cages surrounding the fish. Lately, there has been an increasing trend in using advanced technologies and robotic systems to address several challenges in fish farms and thus increase the level of objectivity and automation during daily operations. As a part of this trend, robots that are attached to the net have been developed to prevent biofouling growth through regular grooming of the nets. This paper presents a control-oriented mathematical model of such a robot that is well-suited for the development of control strategies for autonomous operations of biofouling prevention robots. The model focuses on the degrees of freedom available for control and uses a coordinate transform to a cylindrical coordinate system to reduce the number of degrees of freedom needed to describe the robot's position. For simulation purposes, a feedback linearizing control law ensuring uniformly globally exponentially stable equilibrium points for the robot's controlled states has been proposed. The integrated system consisting of the model of a net cage, the model of the robot and the proposed control strategy have been demonstrated in simulation studies for autonomous navigation of an underwater biofouling prevention and inspection robot.
Dynamic positioning is an important control feature for an underwater remotely operated vehicle. This paper presents a nonlinear dynamic positioning controller suited for application to vehicles with model uncertainties, operating in environments with unpredictable disturbances, such as an aquaculture net cage. The proposed controller combines the backstepping approach with an adaptation term to ensure robustness. Using Lyapunov theory and Matrosov's theorem the origin of the closed-loop system is proven to be: (i) globally asymptotically stable when assuming persistency of excitation, and (ii) stable and bounded, with the true position converging to the desired position if there is no persistency of excitation. This paper also presents results from simulations where the proposed controller is contextualized and compared to similar controllers, showing promising results. Finally, as the main result of the manuscript that demonstrates the effectiveness of the proposed control law, an extensive field trial campaign is conducted at a full-scale aquaculture site using an industrial ROV where the proposed controller is successfully tested under realistic operational conditions.
