Coordinated control of small, remotely operated and submerged vehicle-manipulator systems
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 co...
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| Language: | English en |
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2011
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| Online Access: | http://hdl.handle.net/1828/3757 |
| Summary: | 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. === Graduate |
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