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Hull Identification System for Marine Autonomous Robotics


This proposal will develop a multi-purpose inspection and maintenance platform with an advanced navigation system using Hall Effect and optical sensor technology for marine applications. The platform can be launched whenever the vessel is in port or at a nchor. The device will be able to complete its tasks partially whilst in one port and be re-launched at successive points to complete the task. The generic platform will offer the option of using targeted plug-in modules to perform specific inspection or maintenance tasks such as structural integrity monitoring of the ships hull or carrying out cleaning and waste recovery operations.This project offers a means to effectively and efficiently undertake hull inspection and maintenance thereby extending the safe working life of the vessel. Cleaning of the hull ensures the vessel maintains the lowest possible resistance which has an extremely large impact on the hydrocarbon fuel consumed. Therefore ensuring a clean and smooth vessel surface reduces vessel e missions and reduces operating costs. The proposed dead reckoning method will use optical CMOS technology, which tracks surface feature changes. Because platform slip and drift can occur, it is necessary to use known hull features to update the current p osition. With a combination of Hall Effect and CMOS based optical sensors, it is possible to create a map of the structure for the vessel, which can be intelligently learnt, stored and recalled. By saturating the hull by a localised magnetic field, Hall Effect sensors can detect subsurface strengthening struts and other hull structural features, which will be used as unique landmarks. These landmarks will be used to continually and accurately update the dead reckoning sytem so that any drift or platfo rm slip can be corrected. This landmark mapping of the ship's hull can provide 'teach and repeat' capability and be recalled at any time to aid the navigation of the hull.

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6 Kensington Terrace
United Kingdom

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Participants (9)