Keywords: Control applications in marine renewable energy, Ocean renewable energy, Adaptive and robust control in marine systems
Abstract: Model predictive control (MPC) is an effective control method to improve the energy conversion efficiency of wave energy converters (WECs). However, the current developed WEC MPC has not reached commercial viability since the control performance is significantly dependent on the WEC model fidelity. To overcome the plant-model mismatch issue in the WEC MPC control problem, this paper proposes a robust tube-based MPC method to bound plant states within disturbance invariant sets centered around the noise-free model trajectory. The invariant sets are also utilized for tightening the nominal model's constraints that robustly enable the constraint satisfaction. Yet overly conservative invariant sets can narrow the feasible region of the states and control inputs, and hence a data-driven quantile recurrent neural network (QRNN) is proposed in this work to form a learning-based adaptive tube with reduced conservatism by quantifying WEC model uncertainties. The theoretical root is that time-dependent historical data can offer valuable insight into future behaviour of uncertainties. Numerical simulations have validated that the proposed method can improve the energy capture rate compared to the TMPC approach, by synthesizing the QRNN-based tube with MPC.

Keywords: Control applications in marine renewable energy, Modeling, identification, simulation, and control of marine systems, Offshore systems modeling and control
Abstract: We present, in this paper, an experimental framework for design and synthesis of impedance-matching-based (IM) controllers capable of maximising energy extraction in inherently multi degree-of-freedom wave energy converter (WEC) systems, and its subsequent application to the Intertial Sea Wave Energy Converter (ISWEC) device, by incorporating recent advances in IM-based theory. In particular, we consider a 1/20th scale prototype of the ISWEC system, tested as part of a larger experimental campaign conducted within the tank facilities available at Universita degli Studi di Napoli Federico II, subject to a variety of wave conditions. We adopt two different control structures to realise an approximation of the IM principle, fully tuned based upon interpolation of a particular (experimentally obtained) non-parametric empirical transfer function estimate, which defines the optimal frequency-domain input-output response for energy-maximising behaviour. Furthermore, a performance comparison between controller tuning based upon traditional linear boundary element method models, and the presented experimental approach, is also offered, showing that the latter can consistently outperform the former in realistic scenarios, for the set of analysed sea-states.

Keywords: Control applications in marine renewable energy
Abstract: This paper presents tank testing experimental results of applying a noncausal optimal control strategy to a hinged-raft wave energy converter (WEC). The linear non-causal optimal control (LNOC) algorithm is designed and implemented in real-time to calculate the PTO torque signal based on the WEC response feedback information and the incoming wave feed-forward information. A controllable DC motor is used to provide the required torque to the WEC hinge. Tank testing results show that, compared with a well-tuned passive damper, the LNOC can improve the absorbed mechanical power in most of the tested sea conditions by up to 126%.

Keywords: Control applications in marine renewable energy, Ocean renewable energy, Nonlinear and optimal control in marine systems
Abstract: Energy-maximising control has proven to be of fundamental aid in the pathway towards commercialisation of wave energy conversion technology. The WEC control problem is based upon the design of a suitable control law capable of maximising energy extraction from the wave resource, while effectively minimising any risk of component damage. A particularly well-established family of WEC controllers is based upon a composite structure, where an optimal velocity reference is generated via direct optimal control procedures, followed by a suitable tracking control strategy. This paper presents the design and synthesis of a second order sliding mode controller to attain a reference tracking for a wave energy system. The presented approach can inherently handle parameter uncertainty in the model, which is ubiquitous within hydrodynamic modelling procedures. Furthermore, the proposed sliding mode controller has relatively mild computational requirements, and finite-time convergence to the designed surface, hence being an ideal candidate for real-time energy-maximising control of WEC systems.

Keywords: Vision, recognition and reconstruction for underwater applications, Aquaculture, Underwater localization techniques
Abstract: A main concern for the aquaculture industry is the fish behaviour and welfare. Motion trajectory analysis of salmon at aquaculture farming sites with respect to certain aquaculture operations aims to provide information about the behaviour and possibly stress level of the farmed salmon and may help to generate a general welfare indicator index. Towards this aim we present an innovative computer vision and machine learning based approach for motion trajectory estimation of salmon. Video footage was recorded with a stereo camera setup. Deep learning based object detection was performed to detect particular features. We focused on tracking the fish eyes and heads as a reliable indicators of the fish’s position. Feature matching and subsequent 3D reconstruction was performed to calculate the 3D position of the fish from which trajectories of the fish movement were estimated. Related experiments were conducted at an aquaculture research facility under natural lighting conditions and extracted trajectories allowed a qualitative verification. The developed method was verified using synthetic ground truth data produced with an open source computer graphics software for quantifiable performance metrics.

Keywords: Surface and underwater vehicles, Vision, recognition and reconstruction for underwater applications, Guidance, navigation and control (GNC) of unmanned marine vehicles (surface and underwater)
Abstract: Autonomous Underwater Vehicles (AUVs) performing visual surveys aimed at the preservation of marine environments are equipped with optical sensors for image acquisition. In addition, an altitude sensor is usually installed on-board to control the distance from the seabed and avoid possible collisions. Within this context, this work proposes a navigation strategy for underwater monitoring scenarios, which fuses a single bottom-looking camera and altitude information for linear velocity estimation. This allows to exploit the payload already required by monitoring activities also for navigation purposes, thus reducing the number of sensors on-board the AUV. The linear velocity is provided by a monocular Visual Odometry (VO) technique that switches between homography and epipolar models for motion estimation and leverages altitude measurements to overcome the scale ambiguity issue. The navigation framework relies on an Extended Kalman Filter (EKF) that combines visual-based linear velocity with attitude and depth measurements for trajectory estimation. The proposed strategy has been tested on real data acquired by using Zeno AUV, equipped with bottom-looking camera, DVL, Attitude and Heading Reference System (AHRS), and depth sensor. The performance has been assessed comparing the estimated linear velocities with the DVL readings, and the VO-based estimated trajectory with that provided by a DVL-based dead-reckoning approach, yielding to a maximum absolute error of 2.16m for a reference trajectory of 166m. Given the promising results, this strategy could represent an affordable solution for underwater navigation where visibility conditions allow the use of optical sensors.

Keywords: Surface and underwater vehicles, Vision, recognition and reconstruction for underwater applications, Guidance, navigation and control (GNC) of unmanned marine vehicles (surface and underwater)
Abstract: The periodical hull inspection represents a necessary task to ensure the maintenance of a vessel since it allows to counteract decay, check for structural damages, and fight the biofouling phenomenon affecting the navigation efficiency. Typically, this task is executed by divers, resulting in a dangerous job for the human operator, or by Remotely Operated Vehicles, driven by highly trained users. Aiming at automating the task and increasing its operational safety, this work proposes a strategy to perform the ship hull inspection using an Autonomous Underwater Vehicle (AUV), equipped with a stereo camera and a proximity sensor, without a prior knowledge of the target shape. At first, the images from the stereo vision system allow to estimate the lateral velocity of the vehicle and its orientation with respect to the hull surface. Then, the proximity measurement, properly projected along the normal axis to the surface of the target, provides a measure of the distance of the AUV from the surveyed structure. Lastly, the robot control system exploits these estimates to perform the mission with a constant lateral velocity, maintaining both a predefined safety distance from the target and the optical axis of the camera orthogonal to the examined surface. The proposed approach has been tested in a simulated environment, performing the investigation of a simplified model of ship hull. The results suggest the feasibility of the strategy: during the simulations, the AUV completes the mission with a full autonomy, safely, obtaining a 3D reconstruction of the surveyed structure.

Keywords: Surface and underwater vehicles, Vision, recognition and reconstruction for underwater applications, Underwater localization techniques
Abstract: In the last years visual odometers have been improved considerably, but there is still a certain lack of robustness and reliability when used to navigate Autonomous Underwater Vehicles (AUV) in complex underwater scenarios, such as habitats colonized with Posidonia oceanica or Zostera noltii. The work presented in this paper goes one step beyond the current solutions, improving a state of the art approach to be applied in underwater applications. Tests conducted with real marine visual data grabbed in waters of the Balearic Islands from a bottom-looking camera installed on an AUV, show the progress in the vehicle displacement estimation and its viability to be used online

Keywords: Autonomous and remotely operated (surface and underwater) marine vessels, Intelligence and autonomy in marine systems and operations
Abstract: This paper presents the main results and latest developments in a 4-year project called autonomous subsea intervention (SEAVENTION). In the project we have developed new methods for autonomous inspection, maintenance and repair (IMR) in subsea oil and gas operations with Unmanned Underwater Vehicles (UUVs). The results are also relevant for offshore wind, aquaculture and other industries. We discuss the trends and status for UUV-based IMR in the oil and gas industry and provide an overview of the state of the art in intervention with UUVs. We also present a 3-level taxonomy for UUV autonomy: mission-level, task-level and vehicle-level. To achieve robust 6D underwater pose estimation of objects for UUV intervention, we have developed marker-less approaches with input from 2D and 3D cameras, as well as marker-based approaches with associated uncertainty. We have carried out experiments with varying turbidity to evaluate full 6DOF pose estimates in challenging conditions. We have also devised a sensor autocalibration method for UUV localization. For intervention, we have developed methods for autonomous underwater grasping and a novel vision-based distance estimator. For high-level task planning, we have evaluated two frameworks for automated planning and acting (AI planning). We have implemented AI planning for subsea inspection scenarios which have been analyzed and formulated in collaboration with the industry partners. One of the frameworks, called T-REX demonstrates a reactive behavior to the dynamic and potentially uncertain nature of subsea operations. We have also presented an architecture for comparing and choosing between mission plans when new mission goals are introduced.

Keywords: Autonomous and remotely operated (surface and underwater) marine vessels, Maritime robotics (underwater, surface, aerial), Marine cyber-physical systems
Abstract: This paper discusses a novel approach for the exploration of an underwater structure. A team of robots splits into two roles: certain robots approach the structure collecting detailed information (proximal observers) while the rest (distal observers) keep a distance providing an overview of the mission and assist in the localization of the proximal observers via a Cooperative Localization framework. Proximal observers utilize a novel robust switching model-based/visual inertial odometry to overcome vision-based localization failures. Exploration strategies for the proximal and the distal observer are discussed.

Keywords: Guidance, navigation and control (GNC) of unmanned marine vehicles (surface and underwater), Modeling, identification, simulation, and control of marine systems, Aquaculture
Abstract: In this study, a general control framework for autonomous operations in highly complex and dynamically changing environments such as fish farms is proposed and experimentally validated. Since fish farms feature an environment that includes fish, deformable flexible structures and highly variable environmental disturbances, the framework is designed to interact with these. The proposed control approach integrates estimates of the cage structure dynamics and fish behavior, adaptive path planning and path following control concepts in one unified and compact framework that could be used to implement and demonstrate different concept studies in dynamically changing environments. The performance of the control framework is investigated though field trials using a remotely operated vehicle (ROV) in a commercial fish farm. Experimental results show that the proposed framework can be applied to challenging operations in fish farms.

Keywords: Intelligence and autonomy in marine systems and operations, Maritime robotics (underwater, surface, aerial), Guidance, navigation and control (GNC) of marine vessels
Abstract: This paper presents a novel framework for modelling dynamic Bayesian belief networks (BBNs) for online risk assessment in underwater operations. Existing frameworks spans from commercial software with restricted code access to non-profit open source frameworks. Frameworks with restricted code access often provides general user interfaces and visualization tools, while open source frameworks provides access to code for developers. The model presented in this paper pursues a best of both worlds scenario, where the model implementation should be uncomplicated while also providing visualization and verification to provide the user with a clear perception of the implemented model. The presented method is an expansion of the Bayesian model of the pomegranate python library, and simplifies the procedure of building, verifying and utilizing BBN models. The method is applied to a conceptual design of an underwater scenario case study with a model for an underwater vehicle manipulator system.

Keywords: Vision, recognition and reconstruction for underwater applications, Monitoring, diagnosis and fault handling, Intelligence and autonomy in marine systems and operations
Abstract: Protecting the remaining wild salmon stock in Norway is of utmost importance and requires that farmed salmon cannot escape from aquaculture sites. As holes in net-cages are responsible for a large fraction of the escaped salmon the industry has to perform frequent inspections of the fish cage integrity.In this paper we propose an image processing and computer vision based attention mechanism towards a more automated fish-cage inspection. The presented algorithm allows to indicate areas in videos showing net-pen locations where potential holes are present. We show the effectivity of the approach on video-recordings of holes also in commercial fish-cages.

Keywords: Ocean renewable energy, Offshore systems modeling and control, Nonlinear and optimal control in marine systems
Abstract: The general goal of control methods developed for Wave Energy Converter (WEC) technologies is to improve the amount of energy captured by the WEC from the wave. However, most WEC control methods require some power to be supplied from the grid (reactive power) to drive the floater towards resonance at intervals to improve the device's overall performance. Such controls' reactive power and motion requirements sometimes become large and unrealistic. Additionally, a power take-off (PTO) unit capable of fulfilling a sizeable reactive power requirement will be expensive and complex, if not impossible. In this work, an optimal control formulation that aims to maximize the harvested energy while constraining the reactive power not to exceed a realistic threshold is derived using Pontryagin Minimum Principle. The optimal control formulation is derived for a single WEC device with irregular excitation. Low fidelity numerical simulations are presented comparing the proposed power-constrained Bang Singular Bang (PCBSB) control to an Optimal Resistive Loading (ORL) control.

Keywords: Ocean renewable energy, Control applications in marine renewable energy, Modeling, identification, simulation, and control of marine systems
Abstract: Optimal control of wave energy converters has been shown to require an injection of power into the WEC system to maintain an optimal velocity profile. While this consumption of power results in an overall increase in energy capture, it also brings more stringent requirements on the power take-off (PTO) system. Specifically, the PTO must cater for bi-directional power flow and a source available for the provision of this reactive power, either via a storage device, or the electrical grid itself. However, one aspect which has received relatively little attention is the magnitude of the reactive power peaks, which may have implications for the required overall power rating of the system. In particular, though reactive power flow may only be required for a small fraction of the wave period, reactive power peaks well in excess of active power levels bring a potentially significant capital cost in terms of system power rating, along with a unfavourable capacity factor rating. This paper examines the circumstances under which reactive power flow peaks exceed active power levels and proposes a solution which puts a finite (nonzero) limit on reactive power flow, consistent with active power levels. The problem is solved as a nonlinear constrained optimisation problem, while the consequences of imposing such a limit on energy capture are also examined.

Keywords: Control applications in marine renewable energy, Ocean renewable energy, Offshore systems modeling and control
Abstract: Renewable energy sources can be a solution for the recent pollution increasing scenario and the need for diversification of the energy market. Among such alternative sources, wave energy represents a viable solution, due to the its high power density and accessibility. Nonetheless, wave energy is still in phase of development, and a key stepping stone towards commercialisation is strongly linked to the availability of optimal control strategies for maximum energy harvesting. With its ability to handle system constraints and optimise power absorption directly, model predictive control (MPC) has gained popularity within the WEC community as a potential solution for the corresponding energy-maximising problem. In this study, an MPC strategy is developed for real-time control of the so-called PeWEC energy harvesting system, providing also a solution for the wave excitation estimation and forecasting problem, inherently required by the MPC controller to achieve optimal performance. Improved computational requirements are obtained via definition of a reduced control-oriented model, describing the dynamics of the system in a compact form. The performance of the proposed strategy is illustrated via a comprehensive numerical appraisal.

Keywords: Offshore systems modeling and control, Control applications in marine renewable energy, Ocean renewable energy
Abstract: Given the particular energy-maximising performance objective, wave energy converter (WEC) systems are prone to exhibit highly nonlinear behaviour. We present, in this paper, a detailed dynamic analysis and control synthesis for the Inertial Sea Wave Energy Converter (ISWEC) system, deriving and considering a comprehensive associated nonlinear model. In particular, we adopt a harmonic balance (HB) method to achieve this objective, producing the so-called amplitude-frequency curves (AFC) for the corresponding ISWEC nonlinear model, derived via a Lagrangian approach. We demonstrate that the system can present a variety of different behaviours which are completely neglected by its linear model counterpart. Leveraging both the efficiency and convenient representation of the HB method, we synthesise so-called `passive' (textit{i.e.} proportional) energy-maximising controllers using a variety of input conditions. We provide a comparison of the obtained control parameters with those arising from standard linear modelling, showing a consistent improvement in performance by effectively considering the relevant nonlinear ISWEC dynamics.

Keywords: Guidance, navigation and control (GNC) of marine vessels, Modeling, identification, simulation, and control of marine systems, Offshore systems modeling and control
Abstract: Due to increasing vessel sizes and traffic volumes, maneuvering is becoming more and more challenging. Therefore, bridge personnel should be assisted by providing control systems for maneuvering. In addition to an expected improvement in safety and efficiency, automatic maneuvering is also relevant with regard to future autonomous shipping. This paper contributes to position control and heading control, which form the basis for higher-level guidance systems. An extension of the widely used dynamic positioning approach is presented to realize integral reference tracking. This provides the capability to follow a predefined trajectory. The coupled acceleration behavior of the vessel in terms of a full mass matrix is taken into account by a compensation approach, so that a SISO controller design becomes applicable. In the case of the investigated application, there was a significant input delay, which led to greatly reduced control performance and oscillation around the reference value. These problems were solved by applying the so-called reduction transformation approach to the use case of a maneuvering vessel. The time constant of the closed loop system is chosen as the design parameter of the controller design. It represents the choice between performance and economy, which is made by the operator. The realization of the demanded time constant is implemented by a simple optimization loop that yields the actual feedback gain for linear quadratic optimal control.

Real-world tests of the presented system were carried out in a model basin with a scale model of an offshore supply vessel to validate the functionality of the entire control system.

Keywords: Modeling, identification, simulation, and control of marine systems, Intelligence and autonomy in marine systems and operations, Guidance, navigation and control (GNC) of marine vessels
Abstract: This paper presents a systematic comparison of different advanced approaches for motion prediction of vessels for docking scenarios. Therefore, a conventional nonlinear gray-box-model, its extension to a hybrid model using an additional regression neural network (RNN) and a black-box-model only based on a RNN are compared. The optimal hyperparameters are found by grid search. The training and validation data for the different models is collected in full-scale experiments using the solar research vessel Solgenia. The performances of the different prediction models are compared in full-scale scenarios. %To use the investigated approaches for controller design, a general optimal control problem containing the advanced models is described. These can improve advanced control strategies e.g., nonlinear model predictive control (NMPC) or reinforcement learning (RL). This paper explores the question of what the advantages and disadvantages of the different presented prediction approaches are and how they can be used to improve the docking behavior of a vessel. Videos are available under: https://www.htwg-konstanz.de/en/research-and-transfer/insti tutes-and-laboratories/isd/control-engineering/videos/

Keywords: Modeling, identification, simulation, and control of marine systems, Automation of ship’s systems
Abstract: Modeling the dynamics of a free floating surface vehicle is known to be challenging due to the complicated vehicle-fluid interaction and inherent nonlinearity in the model. Data-driven machine-learning technologies can be applied to model the vehicle dynamics and predict its motion over a particular time horizon given specific control inputs. However, the learned model typically is not directly interpretable and is susceptible to out-of-distribution data, which could result in significant modeling errors. To overcome this limitation of learning-based models, we propose using a Gaussian process (GP) to model the dynamics of a surface vehicle, enabling the prediction of the motion with uncertainty. However, a naive implementation of GP algorithms is computationally very intensive. Therefore, efficient state-of-the-art techniques are employed to ease the computational complexity of the traditional GP. The performance of several algorithm variants was compared using actual experimental data, which demonstrated the capability of the proposed GP-based hydrodynamics modeling.

Keywords: Modeling, identification, simulation, and control of marine systems
Abstract: Propeller performance prediction are traditionally based on open water calm model basin tests. These are used for prediction of propulsion performance. In waves, and during vessel manoeuvring, inflow to the propeller changes significantly, i.e. the shape of the wake field will change and propeller load and efficiency will change considerably. This paper focus on the changes in wake fraction that can be identified in dynamic conditions. The effects on wake fraction of ship maneuvering and the effects of waves' orbital velocity are investigated from experimental data. Model basin tests with a free sailing container ship model provide data for identification of propeller load and mean wake fraction during manoeuvring and during passage of waves. The results are verified with analysis during maneuvering in calm sea with a scale model and with the full-scale version of a 45.000 m^3 container ship. Experimental results show the wake fraction variation during maneuvering, and estimates of the wake fraction derivative are found with respect to the relative sway velocity at the propeller. The contribution of the paper is to provide evidence for the magnitude of propeller shaft loads that can be expected during passage in a seaway and during manoeuvring.

Keywords: Maritime safety and security for ports and ships, Modeling, identification, simulation, and control of marine systems, Autonomous and remotely operated (surface and underwater) marine vessels
Abstract: As complex autonomous systems emerge in the maritime sector, measures must be taken in order to ensure thorough safety assessment. Real-world testing can be costly and potentially dangerous, and therefore there is a need for suitable simulation-based methods. This paper presents an implementation of the Adaptive Stress Testing (AST) method applied to the collision avoidance (COLAV) system of a small passenger ferry. AST is a simulation-based technique which has shown promising results in safety assessment of aviation and automobile systems. Given a simulator of a system, AST uses reinforcement learning to optimize toward system failure, and returns the most likely failure scenarios. AST is here shown to successfully identify scenarios where the criteria for failure are met, which is when the ferry collides with an adversary vessel controlled by AST. However, most of the initial results exhibit failures where the COLAV system of the ferry is not responsible for the failure, making the results less valuable to system developers. To improve the relevance, augmentations are made to the optimization problem. The augmentations result in four distinct problem formulations presented in the paper. Finally, the results are clustered using an unsupervised machine learning method called Soft Dynamic Time Warping k-means clustering in order to present a general summary of the identified failure scenarios. Our results demonstrate the relevance and potential of AST for the maritime sector and illustrate how common drawbacks of AST can be circumvented by method adjustment

Keywords: Guidance, navigation and control (GNC) of marine vessels, Underwater localization techniques, Autonomous and remotely operated (surface and underwater) marine vessels
Abstract: This paper proposes a methodology for target tracking of an underwater glider using an unmanned surface vessel (USV). The topside USV is assumed to have knowledge about the position of the underwater glider from an acoustic positioning system, which is exploited to track the planar motions of the submerged vehicle from the surface. We propose a target tracking method for the purpose of glider localization using unmanned systems to reduce the operational costs and potential hazards. A guidance law is implemented on the topside vehicle to track the submerged vehicle when the latter performs generic glider maneuvers. A numerical simulation environment of the two vehicles is presented to validate the target-tracking scheme.

Keywords: Actuators, thrusters, propulsion systems, and sensors in marine systems, Maritime robotics (underwater, surface, aerial), Risk and life cycle assessment in marine systems
Abstract: Autonomous unmanned underwater vehicles (UUVs) are increasingly used for inspection and cleaning tasks. While automating these tasks could greatly reduce the cost, it requires reliable feedback from position and surroundings. Both internal effects and different physical properties affect sensors, resulting in inaccurate feedback if not handled correctly by the navigation system. In this study, an overview of these effects and properties are examined for the most common sensor technologies used for underwater navigation. A fault tree analysis (FTA) is conducted to get knowledge about how the sensor faults, as a result of these effects, affect automated near-structure and off-structure missions, respectively. Moreover, experiments are carried out with a high-resolution sonar and stereo camera to compare the measurement accuracy at different distances. The sensor comparing test shows that cameras can, in some cases, be insufficient to use as the only sensor for obstacle avoidance. It is concluded that the sensor criticality is case-specific; in general, especially faults on attitude feedback are severe for an acceptably-working navigation system and should therefore have high priority when selecting the robotic sensors.

Keywords: Underwater localization techniques
Abstract: Environmental monitoring applications are growing in popularity as the awareness of severe climate change risk rises. In recent times, the focus is moving toward using autonomous robotic systems for underwater monitoring missions. Underwater localization is a crucial part that often requires expensive equipment and labor. Reducing deployment cost and effort can be achieved through the utilization of autonomous surface vessels (ASV) for underwater localization of acoustic sensor networks. In this paper we propose a modification of TDoA-based AML localization algorithm which incorporates depth measurements in position determination procedure. Due to specific nature of the application the algorithm was analyzed in situations that commonly arise in underwater localization missions, which is localizing underwater agent using surface positioned acoustic anchors. Localization performance is analyzed for different locations and noise levels. Sensor depth influence on localization performance was investigated.

Keywords: Guidance, navigation and control (GNC) of unmanned marine vehicles (surface and underwater), Surface and underwater vehicles, Autonomous and remotely operated (surface and underwater) marine vessels
Abstract: This paper considers the problem of collision avoidance for surface vehicles moving under the influence of ocean currents. The vehicles we consider have underactuated dynamics, where the vehicle cannot directly control its lateral motion, which is a common trait of marine vehicles. We propose a reactive algorithm where the vehicle dynamics, including its underactuation and the effects of an ocean current disturbance, are handled directly in the avoidance strategy. Moreover, the algorithm only requires a limited amount of information about the obstacle and is proven to guarantee collision avoidance, as well as the completion of a nominal goal, under explicitly derived conditions. The theory is validated by simulations.

Keywords: Decision support and safe operation, Autonomous and remotely operated (surface and underwater) marine vessels
Abstract: An observer is designed to estimate the disturbance force and moment acting on an underwater vehicle. The observer design leverages the structure of the nonlinear dynamic model. Provided the dynamics are sufficiently slow, the designer may choose observer parameters to ensure that the estimate error is locally, ultimately bounded and converges to an arbitrarily small neighborhood of the origin.