Station Keeping of a Subsea Shuttle Tanker System Under Extreme Current During Offloading

A subsea shuttle tanker has been proposed as a multipurpose, versatile transport and storage system. This paper presents the station keeping challenge of the subsea shuttle tanker design during underwater loading and offloading at a subsea well under an extreme current environment. Understanding the behaviour of the proposed subsea shuttle tanker during offloading in extreme currents is vital for both the design of the subsea shuttle tanker itself but also the required actuator effort needed to uphold the demanded station keeping abilities. During the offloading process, the hoovering subsea shuttle tanker would current-vane in a water depth of approximately 70 metres. Recent studies have shown that the drag force exerted on the subsea shuttle tanker body is up to 80 times larger for side-ways current compared to the head-on current. With current-waning capabilities, the generated lift forces are low, and thus the subsea shuttle tanker will use less effort to maintain its desired position and water depth. The paper further investigates the movement of the subsea shuttle tanker during offloading with extreme current speeds, i.e., above 1.6 m/s, in the surge, heave, and pitch motions, respectively. The planar model is built up using a Luenberger observer, where the vessel motions are measured and fed into a linear quadratic regulator (LQR) for calculations of the control input. The LQR control’s primary focus is to hold and achieve the target for the subsea shuttle tanker during the offloading process, i.e., minimize the horizontal and vertical motion. Finally, a state-of-the-art probabilistic method is used to predict the maximum potential displacement during offloading, i.e., the Average Exceedance Rate Method

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Applied Model-Based Analysis and Synthesis for the Dynamics, Guidance, and Control of an Autonomous Undersea Vehicle

Model-based analysis and synthesis applied to the dynamics, guidance, and control of an autonomous undersea vehicle are presented. As the dynamic model for describing vehicle motion mathematically, the equations of motion are derived. The stability derivatives in the equations of motion are determined by a simulation-based technique using computational fluid dynamics analysis. The dynamic model is applied to the design of the low-level control systems, offering model-based synthetic approach in dynamics and control applications. As an intelligent navigational strategy for undersea vehicles, we present the optimal guidance in environmental disturbances. The optimal guidance aims at the minimum-time transit of a vehicle in an environmental flow disturbance. In this paper, a newly developed algorithm for obtaining the numerical solution of the optimal guidance law is presented. The algorithm is a globally working procedure deriving the optimal guidance in any deterministic environmental disturbance. As a fail-safe tactic in achieving the optimal navigation in environments of moderate uncertainty, we propose the quasi-optimal guidance. Performances of the optimal and the quasi-optimal guidances are demonstrated by the simulated navigations in a few environmental disturbances

Formation Control and Fault Accommodation for a Team of Autonomous Underwater Vehicles

The purpose of this thesis is the development of efficient formation control and fault accommodation algorithms for a team of autonomous underwater vehicles (AUVs). The team of AUVs are capable of performing a wide range of deep water marine applications such as seabed mapping and surveying, oil and gas exploration and extraction, and oil and gas pipeline inspection. However, communication limitations and the presence of undesirable events such as component faults in any of the team members can prevent the whole team to achieve safe, reliable, and efficient performance while executing underwater mission tasks. In this regard, the semi-decentralized control scheme is developed to achieve trajectory tracking and formation keeping while requiring information exchange only among neighboring agents. To this end, model predictive control (MPC) technique and dynamic game theory are utilized to formulate and solve the formation control problem. Moreover, centralized and decentralized control schemes are developed to assess the performance of the proposed semi-decentralized control scheme in the simulation studies. The simulation results verify that the performance of the proposed semi-decentralized scheme is very close to the centralized scheme with lower control effort cost while it does not impose stringent communication requirements as in the centralized scheme. Moreover, the semi-decentralized active fault recovery scheme is developed to maintain a graceful degraded performance and to ensure that the team of autonomous underwater vehicles can satisfy mission objectives when an actuator fault occurs in any of the team members. In this regard, online fault information provided by fault detection and isolation (FDI) modules of each agent and its neighbors are incorporated to redesign the nominal controllers based on the MPC technique and dynamic game theory. Additionally, FDI imperfections such as fault estimation error and time delay are taken into account, and a performance index is derived to show the impact of FDI imperfections on the performance of team members. Moreover, centralized and decentralized active fault recovery schemes are developed to evaluate the performance of the proposed semi-decentralized recovery scheme through comparative simulation studies with various fault scenarios. The comparative simulation studies justify that the proposed semi-decentralized fault recovery scheme meets the design specifications even if the performance of the FDI module is not ideal

Fault Detection, Isolation and Identification of Autonomous Underwater Vehicles Using Dynamic Neural Networks and Genetic Algorithms

The main objective of this thesis is to propose and develop a fault detection, isolation and identification scheme based on dynamic neural networks (DNNs) and genetic algorithm (GA) for thrusters of the autonomous underwater vehicles (AUVs) which provide the force for performing the formation missions. In order to achieve the fault detection task, in this thesis two level of fault detection are proposed, I) Agent-level fault detection (ALFD) and II) Formation-level fault detection (FLFD). The proposed agent-level fault detection scheme includes a dynamic neural network which is trained with absolute measurements and states of each thruster in the AUV. The genetic algorithm is used in order to train the DNN. The results from simulations indicate that although the ALFD scheme can detect the high severity faults, for low severity faults the accuracy is not satisfy our expectations. Therefore, a formation-level fault detection scheme is developed. In the proposed formation-level fault detection scheme, a fault detection unit consist of two dynamic neural networks corresponding to its adjacent neighbors, is employed in each AUV to detect the fault in formation. Each DNN of the fault detection unit is trained with one relative and one absolute measurements. Similar to ALFD scheme, these two DNNs are trained with GA. The simulation results and confusion matrix analysis indicate that our proposed FLFD can detect both low severity and high severity faults with high level of accuracy compare to ALFD scheme. In order to indicate the type and severity of the occurred fault the agent-level and formation-level fault isolation and identification schemes are developed and their performances are compared. In the proposed fault isolation and identification schemes, two neural networks are employed for isolating the type of the fault in the thruster of the AUV and determining the severity of the occurred fault. In the fist step, a multi layer perceptron (MLP) neural network categorize the type of the fault into thruster blocking, flooded thruster and loss of effectiveness in rotor and in the next step a MLP neural network classify the severity into low, medium and high. The neural networks in fault isolation and identification schemes are trained based on genetic algorithm with various data sets which are obtained through different faulty operating condition of the AUV. The simulation results and the confusion matrix analysis indicate that the proposed formation-level fault isolation and identification schemes have a better performance comparing to agent-level schemes and they are capable of isolating and identifying the faults with high level of accuracy and precision

Autonomous Underwater Intervention: Experimental Results of the MARIS Project

open11noopenSimetti, E. ;Wanderlingh, F. ;Torelli, S. ;Bibuli, M. ;Odetti, A. ;Bruzzone, G. ; Lodi Rizzini, D. ;Aleotti, J. ;Palli, G. ;Moriello, L. ;Scarcia, U.Simetti, E.; Wanderlingh, F.; Torelli, S.; Bibuli, M.; Odetti, Angelo; Bruzzone, G.; Lodi Rizzini, D.; Aleotti, J.; Palli, G.; Moriello, L.; Scarcia, U