Chemical analysis of sediments using X-Ray fluorescence on-board an Autonomous Underwater Vehicle

Advances in robotics have led to the development of autonomous platforms capable of exploring regions that are inaccessible for humans. These regions range from extra-terrestrial surfaces, such as Mars and Titan, to the bottom of our oceans. In addition to robotics there have also been advances in sensor technologies in areas such as power consumption, size, weight, and communications. The combination of field robotics with novel sensing capabilities provides exciting science opportunities. Autonomous measurement of phenomena present in the environment offers an alternative to expensive, time-consuming manual measurements. This thesis investigates the novel use of a miniaturised X-Ray Fluorescence (XRF) spectrometer sensor system as part of the scientific payload on an Autonomous Underwater Vehicle (AUV). This will allow the automated in situ semi-quantitative analysis of heavy metal contamination present in marine sediments. Heavy metal contamination of sediments is particularly important because of its potential impact on associated ecosystems and human health. To achieve this capability the XRF system has been integrated with the AUV using a custom housing that enables the sensor to be operated safely underwater. A landing behaviour has been developed for the AUV that enables the vehicle to land on the seabed, without signicantly disturbing the sediment layer, and to then remain in a stationary position for the duration of the measurement. Automated data analysis using genetic algorithms was performed on the XRF data on-board the vehicle. This would enable the AUV control system to make informed decisions based on the results of measurements facilitating adaptive sampling strategies. A total of 21 in situ measurements have been performed in the Derwent estuary region, located in south-east Tasmania, Australia. The results show significantly higher relative heavy metal concentrations in areas of industrial activity. This demonstrates the developed system can perform in situ measurements that can be used to observe spatial variations in heavy metal contamination. The resulting data from these measurements can guide further comprehensive environmental monitoring missions by supporting site selection or assisting with the remediation of contaminated sediments. The research contribution presented in this thesis has been the development of the capability to autonomously and intelligently perform in situ measurements and data analysis of marine sediments using an AUV equipped with a miniaturised XRF spectrometer. The next stage of this research will aim to increase the scientific return of measurement missions by the realtime inclusion of scientific sensor data in decision-making processes to enable adaptive sampling.