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Monitoring Underwater Operations with Virtual Environments

Authors: Juan C. Santamaria; Augusto Opdenbosch;

Monitoring Underwater Operations with Virtual Environments

Abstract

Abstract This paper introduces a new set of software tools that integrate near-real-time visualization with a publish-and-subscribe mechanism to achieve remote monitoring and control of dynamic objects in an underwater scene. The approach proposed in this paper involves the integration of existing technologies to produce a powerful and flexible solution easily adapted to the extensive and diverse set of situationsencountered in underwater construction, underwater surveying, and maritime navigation. Users can define elaborate virtual scenes that can accurately represent all the relevant elements associated with an underwater construction job, including complex structures and dynamic objects, such as ROVs, vessels, etc. Real-time data from instruments and positioning sensors is made available by using a publishing mechanism and a remote data server. Users with Internet or intranet access can subscribe to any real-time data field being published and receive updates each time the information changes, allowing them to monitor and log the events of the underwater job at the same time they are taking place. The use of advanced cueing techniques and multi-resolution rendering make it possible to achieve interactive frame rates without sacrificing accuracy and realism. The concept of 3D modules will also be introduced. These powerful modules can be linked to live data and attached to any element in the virtual environment including dynamic ones. This flexibility allows the users to monitor the data, not only as it changes, but also within the spatial context that makes the most sense. This paper focuses on the visualization components of the proposed solution, while providing a general description of the other two components (data acquisition, data distribution), and describes the Ehime Maru recovery mission as an example of an underwater job that benefited by using this technology. Introduction Any scenario that deprives or diminishes our senses will always produce challenges because our ability to make good decisions lessens with poor perception. The ocean is without a doubt a perfect example of such a place. Depths of only a few hundred feet already pose serious challenges for any kind of operation. The drastic loss of visibility associated with depth, combined with the enormous pressures and low temperatures makes it a place where only tele-operated robots can function. These robots provide limited feedback to the people that operate them, making underwater construction a very expensive and time-consuming process. There are several factors responsible for the lack of useful feedback, many of which are bound by the laws of physics. Communication technologies that thrive in air simply fail to work in water (e.g., radio waves). Position technologies such as GPS or laser tracking cannot be used underwater. Light can only travel a limited distance in water. As a result, the sensors currently available provide limited accuracy and frequency. The cameras available today can only provide an image of the immediate vicinity even under good visibility conditions. To complicate things even further, the data collected by all these sensors and cameras is often scattered across many systems, making its perception and analysis very difficult.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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