Identification of a Surface Marine Vessel Using LS-SVM

The availability of adequate system models to reproduce, as faithfully as possible, the actual behaviour of the experimental systems is of key importance. In marine systems, the changing environmental conditions and the complexity of the infrastructure needed to carry out experimental tests call for mathematical models for accurate simulations. There exist a wide number of techniques to define mathematical models from experimental data. Support Vector Machines (SVMs) have shown a great performance in pattern recognition and classification research areas having an inherent potential ability for linear and nonlinear system identification. In this paper, this ability is demonstrated through the identification of the Nomoto second-order ship model with real experimental data obtained from a zig-zag manoeuvre made by a scale ship. The mathematical model of the ship is identified using Least Squares Support Vector Machines (LS-SVMs) for regression by analysing the rudder angle, surge and sway speed, and yaw rate. The coefficients of the Nomoto model are obtained with a linear kernel function. The model obtained is validated through experimental tests that illustrate the potential of SVM for system identification

Parameter estimation for a ship's roll response model in shallow water using an intelligent machine learning method

In order to accurately identify the ship's roll model parameters in shallow water, and solve the problems of difficult estimating nonlinear damping coefficients by traditional methods, a novel Nonlinear Least Squares Support Vector Machine (NLS-SVM) is introduced. To illustrate the validity and applicability of the proposed method, simulation and decay tests data are combined and utilized to estimate unknown parameters and predict the roll motions. Firstly, simulation data is applied in the NLS-SVM model to obtain estimated damping parameters, compared with pre-defined parameters to verify the validity of the proposed method. Subsequently, decay tests data are used in identifying unknown parameters by utilizing traditional models and the new NLS-SVM model, the results illustrate that the intelligent method can improve the accuracy of parametric estimation, and overcome the conventional algorithms' weakness of difficult identification of the nonlinear damping parameter in the roll model. Finally, to show the wide applicability of the proposed model in shallow water, experimental data from various speeds and Under Keel Clearances (UKCs) are applied to identify the damping coefficients. Results reveal the potential of using the NLS-SVM for the problem of the roll motion in shallow water, and the effectiveness and accuracy are verified as well

An adaptive autopilot design for an uninhabited surface vehicle

An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council

Reliable Navigational Scene Perception for Autonomous Ships in Maritime Environment

Due to significant advances in robotics and transportation, research on autonomous ships has attracted considerable attention. The most critical task is to make the ships capable of accurately, reliably, and intelligently detecting their surroundings to achieve high levels of autonomy. Three deep learning-based models are constructed in this thesis to perform complex perceptual tasks such as identifying ships, analysing encounter situations, and recognising water surface objects. In this thesis, sensors, including the Automatic Identification System (AIS) and cameras, provide critical information for scene perception. Specifically, the AIS enables mid-range and long-range detection, assisting the decision-making system to take suitable and decisive action. A Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) is used to detect ships or objects. Following that, a Semi- Supervised Convolutional Encoder-Decoder Network (SCEDN) is developed to classify ship encounter situations and make a collision avoidance plan for the moving ships or objects. Additionally, cameras are used to detect short-range objects, a supplementary solution to ships or objects not equipped with an AIS. A Water Obstacle Detection Network based on Image Segmentation (WODIS) is developed to find potential threat targets. A series of quantifiable experiments have demonstrated that these models can provide reliable scene perception for autonomous ships

Modelling of a surface marine vehicle with kernel ridge regression confidence machine

This paper describes the use of Kernel Ridge Regression (KRR) and Kernel Ridge Regression Confidence Machine (KRRCM) for black box identification of a surface marine vehicle. Data for training and test have been obtained from several manoeuvres typically used for marine system identification. Thus, a 20/20 degrees Zig-Zag, a 10/10 degrees Zig-Zag, and different evolution circles have been employed for the computation and validation of the model. Results show that the application of conformal prediction provides an accurate model that reproduces with large accuracy the actual behaviour of the ship with confidence margins that ensure that the model response is within these margins, making it a suitable tool for system identification

Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm

Funding: The fieldwork was registered in the Research in Svalbard database (RiS ID 11578). Fieldwork and research were financed by Arctic Field Grant Project AZKABAN-light (Norwegian Research Council project no. 322 332), Deep Impact (Norwegian Research Council project no. 300 333), Deeper Impact (Norwegian Research Council project no. 329 305), Marine Alliance for Science and Technology in Scotland (MASTS), the Ocean Frontier Institute (SCORE grant no. HR09011), and Glider Phase II financed by ConocoPhillips Skandinavia AS. Geir Pedersen’s participation was co-funded by CRIMAC (Norwegian Research Council project no. 309 512). Maxime Geoffroy was financially supported by the Ocean Frontier Institute of the Canada First Research Excellence Fund, the Natural Sciences and Engineering Research Council Discovery Grant Programme, the ArcticNet Network of Centres of Excellence Canada, the Research Council of Norway Grant Deep Impact, and the Fisheries and Oceans Canada through the Atlantic Fisheries Fund.Classification of zooplankton to species with broadband echosounder data could increase the taxonomic resolution of acoustic surveys and reduce the dependence on net and trawl samples for ‘ground truthing’. Supervised classification with broadband echosounder data is limited by the acquisition of validated data required to train machine learning algorithms (‘classifiers’). We tested the hypothesis that acoustic scattering models could be used to train classifiers for remote classification of zooplankton. Three classifiers were trained with data from scattering models of four Arctic zooplankton groups (copepods, euphausiids, chaetognaths, and hydrozoans). We evaluated classifier predictions against observations of a mixed zooplankton community in a submerged purpose-built mesocosm (12 m3) insonified with broadband transmissions (185–255 kHz). The mesocosm was deployed from a wharf in Ny-Ålesund, Svalbard, during the Arctic polar night in January 2022. We detected 7722 tracked single targets, which were used to evaluate the classifier predictions of measured zooplankton targets. The classifiers could differentiate copepods from the other groups reasonably well, but they could not differentiate euphausiids, chaetognaths, and hydrozoans reliably due to the similarities in their modelled target spectra. We recommend that model-informed classification of zooplankton from broadband acoustic signals be used with caution until a better understanding of in situ target spectra variability is gained.Publisher PDFPeer reviewe

Rapid Quantification of Biofouling With an Inexpensive, Underwater Camera and Image Analysis

To reduce the transport of potentially invasive species on ships\u27 submerged surfaces, rapid-and accurate-estimates of biofouling are needed so shipowners and regulators can effectively assess and manage biofouling. This pilot study developed a model approach for that task. First, photographic images were collected in situ with a submersible, inexpensive pocket camera. These images were used to develop image processing algorithms and train machine learning models to classify images containing natural assemblages of fouling organisms. All of the algorithms and models were implemented in a widely available software package (MATLAB©). Initially, an unsupervised clustering model was used, and three types of fouling were delineated. Using a supervised classification approach, however, seven types of fouling could be identified. In this manner, fouling was successfully quantified over time on experimental panels immersed in seawater. This work provides a model for the easy, quick, and cost-effective classification of biofouling