Intelligent techniques-based approach for ship manoeuvring simulations and analysis

Abstract The aim of this thesis is to establish a reliable, replicable, and consistent Artificial Intelligent (A.I.) system, capable of predicting accurately the turning tracks of ships. The Artificial Neural Networks (ANN) method has been adapted to solve this problem. The physical and operational data of a ship are described and used as inputs into the system in order to predict the turning manoeuvres. The thesis focuses on both approaches of direct and force models. The ship and controllability data such as underwater hull, rudder and propeller are parameterized and introduced into the system in order to build the direct model for simulating ship manoeuvring motion. The developed method has also been explored in order to include the hydrodynamic forces acting on ships. The initial forces and moments acting on the ship have been described and investigated. The neural system is reinstructed and retuned to solve the prediction problem not only for ensuring more accuracy but also to have deeper insights in the effects of hydrodynamic forces on ship motions, especially the turning manoeuvres. To demonstrate creditability, and confidence in the method used, the results of the program performance were tested against data obtained from ship handling simulators. Parallel Artificial Neural Networks (PANN) is formulated and implemented in MATLAB (instructed, tuned and trained) in order to predict the manoeuvring behaviour of different ship types with variation of sizes, displacements, speeds and rudder angles. Results obtained from the models are compared with the results generated by two different simulators using different ships. The system accuracy and consistency are quantified by the standard deviation. Data optimizers with series models have been established and the emphasis came with higher accuracies and better performance than the general model (direct or force model). The prominence of this application leads to wider applications and better abilities to solve multi-problems, related to the focus theme of the research. The thesis investigates and analyses the diverse results obtained from different prediction models. Thus, it leads to one of the essential points in this research in order to realise the range of the coherence of the applied-based approach. It is essential to study these issues and to analyse the performance of both approaches when applying the environmental conditions in the future to ensure a satisfactory prediction system. The outcome results indicate that the introduced system is capable of thoroughly analysing ship manoeuvring motion and of comparing different ships’ parameters. This system provides the users in advance with the characteristics of transient phases of ship motion. It simulates the real manoeuvring motion before any online training. Further, discussions of recent and future applications are stated in this thesis. The introduced approach proved to be systematic and valid and can take on a variety of forms. System identification techniques, theoretical prediction methods and regression analysis results from other application techniques are also discussed in this thesis

A decision-making tool for real-time prediction of dynamic positioning reliability index

PhD ThesisThe Dynamic Positioning (DP) System is a complex system with significant levels of integration between many sub-systems to perform diverse control functions. The extent of information managed by each sub-system is enormous. The sophisticated level of integration between sub-systems creates an array of possible failure scenarios. A systematic analysis of all failure scenarios would be time-consuming and for an operator to handle any such catastrophic situation is hugely demanding. There are many accidents where a failure in a DP system has resulted in fatalities and environmental pollution. Therefore, the reliability assessment of a DP system is critical for safe and efficient operation. The existing methods are time-consuming, involving a lot of human effort which imposes built-in uncertainty and risk in the system during complex operation. This thesis has proposed a framework for a state-of-the-art decision-making tool to assist an operator and prevent incidents by introducing a new concept of Dynamic Positioning – Reliability Index (DP-RI). The DP-RI concept covers three phases, leading to technical suggestions for the operator during complex operations, which are defined as Data, Knowledge, Intelligence, and Action. The proposed framework covers analytics including descriptive, diagnostic, predictive and prescriptive analytics. The first phase of the research involves descriptive and diagnostic analytics by performing big data analytics on the available databases to identify the sub-systems which play critical roles in DP system functionality. The second phase of the research involves a novel approach where predictive analytics are used for the weight assignment of the sub-systems, dynamic reliability modelling and offline and realtime forecasting of DP-RI. The third phase introduces innovative prescriptive analytics to provide possible technical solutions to the operator in a short time during failures in the system to enable them to respond quickly and prevent DP incidents. Thus, the DP-RI acts as an innovative state-of-the-art decision-making tool which can suggest possible solutions to the DPO by using analytics on the knowledge database. The results proved that it is a useful tool if implemented on an actual vessel with diligent integration with the DP control system.Singapore Economic Development Board (EDB) and DNV GL Singapore Pte Ltd

Thrust control design for unmanned marine vehicles

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2012Includes bibliographical references (leaves: 65-70)Text in English; Abstract: Turkish and Englishxv, 74 leavesIn conventional electrically driven propulsion systems with fixed pitch propellers, thruster controllers are usually aimed at controlling propeller shaft speed only. Especially in unmanned marine vehicles which operate in dynamic flow conditions, these type thruster controllers provide unsatisfactory thrust responses. The reason for this is that the thrust force is simultaneously affected by dynamic effects like, variable ambient flow velocity and angle, thruster-thruster interaction and ventilation. It is aimed to achieve acceptable thrust tracking accuracy in all kind of dynamic flow conditions in this thesis work. A novel feed-back based thruster controller which includes the effect of incoming axial flow velocity, is designed for this purpose. In controller design, first, thruster propeller's open water characteristics in four-quadrant flow states are measured. Data collected from open water tests are then non-dimensionalized and embedded in the controller's thrust model code. Relation between ideal shaft speed and desired thrust is derived by using the four-quadrant propeller model. The proposed method is evaluated in the experimental test-setup designed for this study to simulate open water conditions. Results indicate that thrust tracking performance of novel controller is acceptable in all four-quadrant flow tests

Drone deep reinforcement learning: A review

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Aeronautical enginnering: A cumulative index to a continuing bibliography (supplement 312)

This is a cumulative index to the abstracts contained in NASA SP-7037 (301) through NASA SP-7073 (311) of Aeronautical Engineering: A Continuing Bibliography. NASA SP-7037 and its supplements have been compiled by the Center for AeroSpace Information of the National Aeronautics and Space Administration (NASA). This cumulative index includes subject, personal author, corporate source, foreign technology, contract number, report number, and accession number indexes

Hybrid approaches for mobile robot navigation

The work described in this thesis contributes to the efficient solution of mobile robot navigation problems. A series of new evolutionary approaches is presented. Two novel evolutionary planners have been developed that reduce the computational overhead in generating plans of mobile robot movements. In comparison with the best-performing evolutionary scheme reported in the literature, the first of the planners significantly reduces the plan calculation time in static environments. The second planner was able to generate avoidance strategies in response to unexpected events arising from the presence of moving obstacles. To overcome limitations in responsiveness and the unrealistic assumptions regarding a priori knowledge that are inherent in planner-based and a vigation systems, subsequent work concentrated on hybrid approaches. These included a reactive component to identify rapidly and autonomously environmental features that were represented by a small number of critical waypoints. Not only is memory usage dramatically reduced by such a simplified representation, but also the calculation time to determine new plans is significantly reduced. Further significant enhancements of this work were firstly, dynamic avoidance to limit the likelihood of potential collisions with moving obstacles and secondly, exploration to identify statistically the dynamic characteristics of the environment. Finally, by retaining more extensive environmental knowledge gained during previous navigation activities, the capability of the hybrid navigation system was enhanced to allow planning to be performed for any start point and goal point