Coordinated control of small, remotely operated and submerged vehicle-manipulator systems




Soylu, Serdar

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Current submerged science projects such as VENUS and NEPTUNE have revealed the need for small, low-cost and easily deployed underwater remotely operated vehiclemanipulator (ROVM) systems. Unfortunately, existing small remotely operated underwater vehicles (ROV) are not equipped to complete the complex and interactive submerged tasks required for these projects. Therefore, this thesis is aimed at adapting a popular small ROV into a ROVM that is capable of low-cost and time-efficient underwater manipulation. To realize this objective, the coordinated control of ROVM systems is required, which, in the context of this research, is defined as the collection of hardware and software that provides advanced functionalities to small ROVM systems. In light of this, the primary focus of this dissertation is to propose various technical building blocks that ultimately lead to the realization of such a coordinated control system for small ROVMs. To develop such a coordinated control of ROVM systems, it is proposed that ROV and manipulator motion be coordinated optimally and intelligently. With coordination, the system becomes redundant: there are more degrees of freedom (DOF) than required. Hence, the extra DOFs can be used to achieve secondary objectives in addition to the primary end-effector following task with a redundancy resolution scheme. This eliminates the standard practice of holding the ROV stationary during a task and uncovers significant potential in the small ROVM platform. In the proposed scheme, the ROV and manipulator motion is first coordinated such that singular configurations of the manipulator are avoided, and hence dexterous manipulation is ensured. This is done by using the ROV's mobility in an optimal, coordinated manner. Later, to accommodate a more comprehensive set of secondary objectives, a fuzzy based approach is proposed. The method considers the human pilot as the main operator and the fuzzy machine as an artificial assistant pilot that dynamically prioritizes the secondary objectives and then determines the optimal motion. Several model-based control methodologies are proposed for small ROV/ROVM systems to realize the desired motion produced by the redundancy resolution, including an adaptive sliding-mode control, an upper bound adaptive sliding-mode control with adaptive PID layer, and an H∞ sliding-mode control. For the unified system (redundancy resolution and controller), a new human-machine interface (HMI) is designed that can facilitate the coordinated control of ROVM systems. This HMI involves a 6-DOF parallel joystick, and a 3-D visual display and a graphical user interface (GUI) that enables a human pilot to smoothly interact with the ROVM systems. Hardware-in-theloop simulations are carried out to evaluate the performance of the coordination schemes. On the thrust allocation side, a novel fault-tolerant thrust allocation scheme is proposed to distribute forces and moments commanded by the controller over the thrusters. The method utilizes the redundancy in the thruster layout of ROVM systems. The proposed scheme minimizes the largest component of the thrust vector instead of the two-norm, and hence provides better manoeuvrability. In the first phase of implementation, a small inspection-class ROV, a Saab-Seaeye Falcon™ ROV, is adopted. To improve the navigation, a navigation skid is designed that contains a Doppler Velocity Log, a compass, an inertial measurement unit, and acoustic position data. The sensor data is blended using an Extended Kalman Filter. The developed ROV system uses the upper bound adaptive sliding-mode control with adaptive PID layer. The theoretical and practical results illustrate that the proposed tools can transform, a small, low-cost ROVM system into a highly capable, time-efficient system that can complete complex subsea tasks.



Underwater Remotely Operated Vehicles (ROV), Coordinated Control of Underwater Remotely Operated Vehicle-Manipulator (ROVM), Thrust Allocation, Redundancy Resolution, Human-Machine Interface for ROVM systems, Nonlinear Control