Underwater Localisation Systems for Exploration of Hazardous Environments

Abstract

The primary focus of this research was to develop localisation systems for underwater vehicles operating in confined and potentially cluttered environments. The target application is the monitoring of wet nuclear reactors and storage ponds, both legacy and modern, although the technologies could be applied to other areas such as oil & gas, hull inspection or oceanic exploration. The development of these systems enables efficient exploration, characterisation and monitoring, and the potential for the use of tetherless remotely operated vehicles (tROVs). Two objective environments selected to develop the underwater localisations systems for were the Naraha Nuclear Test Facility (Japan) and the Sellafield Storage Ponds (UK). For Naraha Nuclear Test Facility, an external image based localisation system was developed which was able to estimate the position of an underwater remote operated vehicle (ROV) with high accuracy (mean absolute error of 5 mm) and precision (a standard deviation of 1 mm) in laboratory tests only using low cost, off the shelf components (the total system cost is < £100). A technical demonstration was undertaken which showed that the positioning system, combined with a short-range underwater imaging sonar, was able to identify simulated fuel debris at the bottom of a test pond and generate a 3D scan of the environment with an overall accuracy of reconstructed features of 150 mm. The system enables the use of imaging equipment to reconstruct underwater environments. For the Sellafield Storage Ponds, a system that utilises multiple autonomous underwater vehicles (AUVs) or tROVs, working in a collaborative manner was proposed and simulated. The Communication and Positioning System (CaPS) combines an absolute position system (APS) from an off the shelf 100 kHz acoustic positioning system, with peer to peer (P2P) distance estimation based on received signal strength (RSSI) of electromagnetic signals among the systems agents. The CaPS is able to increase the precision of the position estimation when two or more robots are present, from a standard deviation of 0.314 m with a single robot to 0.211 m when 4 robots are used. The precision of the system scales with the number of robots in the system. The systems fundamental technologies were experimentally characterised to generate the error statistics required for the simulation. The CaPS increases the precision of the position estimation of multiagent underwater systems using low cost off the shelf components. This means that inspection vehicles will be able to explore environments in a more efficient and robust manner.

Date of Award	31 Dec 2019
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Simon Watson (Supervisor) & Barry Lennox (Supervisor)