The use of commercially available autonomous underwater vehicles (AUVs) has increased during the last fifteen years. While they are mainly used for routine survey missions, there is a set of applications that nowadays can be only addressed by manned submersibles or work-class remotely operated vehicles (ROVs) equipped with teleoperated arms: the intervention applications. To allow these heavy vehicles controlled by human operators to perform intervention tasks, underwater structures like observatory facilities, subsea panels or oil-well Christmas trees have been adapted, making them more robust and easier to operate. The TRITON Spanish founded project proposes the use of a light-weight intervention AUV (I-AUV) to carry out intervention applications simplifying the adaptation of these underwater structures and drastically reducing the operational cost. To prove this concept, the Girona 500 I-AUV is used to autonomously dock into an adapted subsea panel and once docked perform an intervention composed of turning a valve and plugging in/unplugging a connector. The techniques used for the autonomous docking and manipulation as well as the design of an adapted subsea panel with a funnel-based docking system are presented in this article together with the results achieved in a water tank and at sea.
Variational approaches to density estimation and pattern recognition using Gaussian mixture models can be used to learn the model and optimize its complexity simultaneously. In this brief, we develop an incremental entropy-based variational learning scheme that does not require any kind of initialization. The key element of the proposal is to exploit the incremental learning approach to perform model selection through efficient iteration over the variational Bayes optimization step in a way that the number of splits is minimized. The method starts with just one component and adds new components iteratively by splitting the worst fitted kernel in terms of evaluating its entropy. Our experimental results, on synthetic and real data sets show the effectiveness of the approach outperforming other state-of-the-art incremental component learners.
Abstract-Nowadays, when the results of research in the field of robotics are presented to the scientific community the same question is asked repeatedly: are the results really reproducible? Regarding benchmarking issues, some technological areas, where complex mechatronic devices such as robots have a central role are, in general, very far from other research areas like physics or chemistry, to name but a few, where reproducibility is always mandatory. Leaving aside mechatronic complexities, the comparison between two different algorithms in the same conditions is influenced by the experimental validation scenario. In underwater environments, the difficulties for benchmarking characterization increase substantially. This is especially true when the testbed is the sea where uncertainty is really high. It is the aim of this work to present a software tool which enables a comparison between two different algorithms to be made when these algorithms are being used to solve the same problem in water tank conditions. This is a preliminary stage before the final validation on the seabed. The evaluated algorithms fall into the 3D image reconstruction context, as a prior step to their autonomous manipulation. Performance results are presented for both simulation and real water tank conditions.
The long term of this ongoing research has to do with increasing the autonomy levels for underwater intervention missions. Bearing in mind that the specific mission to face has been the intervention on a panel, in this paper some results in different development stages are presented by using the real mechatronics and the panel mockup. Furthermore, some details are highlighted describing two methodologies implemented for the required visually-guided manipulation algorithms, and also a roadmap explaining the different testbeds used for experimental validation, in increasing complexity order, are presented. It is worth mentioning that the aforementioned results would be impossible without previous generated know-how for both, the complete developed mechatronics for the autonomous underwater vehicle for intervention, and the required 3D simulation tool. In summary, thanks to the implemented approach, the intervention system is able to control the way in which the gripper approximates and manipulates the two panel devices (i.e. a valve and a connector) in autonomous manner and, results in different scenarios demonstrate the reliability and feasibility of this autonomous intervention system in water tank and pool conditions.
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