Underwater autonomous manipulation for intervention missions AUVs

Underwater manipulators: a review

This paper presents the control framework that has been proposed and successfully employed within the TRIDENT EU FP7 project, the aim of which is to develop a multipurpose Intervention Autonomous Underwater Vehicle I-AUV exhibiting smart manipulation capabilities, for interventions within unstructured underwater environments. In particular, the work focuses on the exploitation of the highly redundant system for achieving a dexterous object grasping, while also satisfying a set of conditions of scalar inequality type to be achieved ultimately. These represent safety and/or operational-enabling conditions for the overall system itself, such as, for instance, respecting joint limits and keeping the object grossly centered in the camera system. Thus the design of a control architecture exhibiting such a property first required an extension of the classical task priority framework, to be performed in such a way as to also account, in a uniform manner, for inequality conditions to be achieved ultimately. Then, following a description on how such an extension has been made, both simulations and experimental trials are successively presented to show how the developed TRIDENT I-AUV system is able to properly exploit all the redundant degrees of freedom for achieving all the established objectives.

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Floating Underwater Manipulation: Developed Control Methodology and Experimental Validation within the TRIDENT Project

The control issue of underwater robots is very challenging due to the nonlinearity, time variance, unpredictable external disturbances, such as the sea current fluctuation, and the difficulty in accurately modeling the hydrodynamic effect. Conventional linear controllers may fail in satisfying performance requirements, especially when changes in the system and environment occur during the operation since it is almost impossible to manually retune the control parameters in water. Therefore, it is highly desirable to have an underwater robot controller capable of self-adjusting control parameters when the overall performance degrades. This paper presents the theory and experimental work of the adaptive plus disturbance observer (ADOB) controller for underwater robots, which is robust with respect to external disturbance and uncertainties in the system. This control scheme consists of disturbance observer (DOB) as the inner-loop controller and a nonregressor based adaptive controller as the outer-loop controller. The effectiveness of the ADOB was experimentally investigated by implementing three controllers: PID, PID plus DOB, and ADOB on an autonomous underwater robot, ODIN III.

Experimental study on advanced underwater robot control

This dissertation describes the development of a new, robust, relative-position sensing strategy suitable for unstructured and unprepared environments. Underwater manipulation is the particular application that motivated this research. Although many relative position sensing systems have already been developed, achieving the level of robustness that is required for operation in the underwater environment is very challenging. 
The sensing strategy is based on fusing bearing measurements from computer vision and inertial rate sensor measurements to compute the relative position between a moving observer and a stationary object. The requirements on the vision system have been chosen to be as simple as possible: tracking a single feature on the object of interest with a single camera. Simplifying the vision system has the potential to create a more robust sensing system. The relative position between a moving observer and a stationary object is observable if these bearing measurements, acquired at different observer positions, are combined with the inertial rate sensor measurements, which describe the motion of the observer. 
The main contribution of this research is the development of a new, recursive estimation algorithm which enables the sensing strategy by providing a solution to the inherent sensor fusion problem. Fusing measurements from a single bearing sensor with inertial rate sensor measurements is a nonlinear estimation problem that is difficult to solve with standard recursive estimation techniques, like the Extended Kalman Filter. A new, successful estimator design—based on the Kalman Filtering approach but adapted to the unique requirements of this sensing strategy—was developed. The new design avoids the linearization of the nonlinear system equations. This has been accomplished by developing a special system representation with a linear sensor model and by incorporating the Unscented Transform to propagate the nonlinear state dynamics. 
The dissertation describes the implementation of the sensing strategy and a demonstration that illustrates how the sensing strategy can be incorporated into the closed-loop control of an autonomous robot to perform an object manipulation task. The performance of the sensing strategy is evaluated with this hardware experiment and extensive computer simulations. Centimeter-level position sensing for a typical underwater vehicle scenario has been achieved.

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Relative position sensing by fusing monocular vision and inertial rate sensors

As the research in the autonomous underwater vehicle (AUV) field intensifies and the necessity of underwater robotic vehicles (URVs) increases, the requirements of an URV have expanded from simple fly-by missions to more complex, intervention missions. The Autonomous Systems Laboratory, Department of Mechanical Engineering, University of Hawaii is in the midst of designing and developing a semi-autonomous underwater vehicle for intervention missions (SAUVIM). The proposed open structure AUV possesses a fully functional manipulator, various mission sensors, and composite pressure vessels enclosed in a flooded composite fairing. The vehicle allows human-intervention from a land-based computer system capable of vehicle path-planning and monitoring.

G.H. Kim

Papers

Design of a semi-autonomous underwater vehicle for intervention missions (SAUVIM)