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The University of Southampton
NEXUSS - Next Generation Unmanned Systems Science

Mission-aware smart payloads for real-time optimisation of data collected using marine autonomous systems

Supervisors: Blair Thornton (lead, Engineering & the Environment, UoS), Leigh Marsh (Ocean & Earth Science, UoS), Maaten Furlong (NOC), Alexander Phillips (NOC), Daniel Jones (NOC)

Rationale:

Autonomy is a key aspect of technology necessary to achieve cost-effect scalable observations in the ocean. While the application of autonomous underwater vehicles (AUVs) equipped with advanced sensor payloads is becoming increasingly routine in ocean science, the functionality of these systems is typically limited to the execution of predefined sets of instructions. For applications such as seafloor imaging, the quality of observational data is sensitive to environmental conditions such as seawater turbidity and performance characteristics such as the altitude and velocity of the AUV. Furthermore, the observational footprints of visual surveys are relatively small and so localisation uncertainties can have a significant impact on temporal studies or multi-resolution studies that require specific locations to be revisited and observed. The lack of real-time awareness regarding data quality and mission objectives limits the effectiveness of marine autonomous systems to reliably deliver high-quality data products to the scientific community.

This research will develop smart-payload systems that will address problems related to seafloor imaging through increased real-time mission and data awareness. The capabilities will enable autonomous systems to assess their performance during data acquisition and optimise measurement parameters and vehicle trajectories in order to increase the likelihood of capturing high quality science data.

Methodology:

This PhD will combine aspects of ocean engineering and computer vision. The student will work as part of a multi-disciplinary group of engineers and scientists and apply advanced data processing techniques to solve real-world problems associated with seafloor imaging for science including:

Training:

The NEXUSS CDT provides state-of-the-art, highly experiential training in the application and development of cutting-edge Smart and Autonomous Observing Systems for the environmental sciences, alongside comprehensive personal and professional development. There will be extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial / government / policy partners. The student will be registered at the University of Southampton, and hosted at Fluid Structure Interaction group of the School of Engineering Science. Specific training will include:

Background Reading:

Thornton, B., et al., (2016), Biometric Assessment of Deep-sea Vent Megabenthic Communities using Multi-Resolution 3D image reconstructions, Deep-Sea Research Part 1, (in press)

Bodenmann, A., et al., (2016), Generation of High-Resolution 3D Reconstructions of the Seafloor in Colour Using a Single Camera and Structured Light, Journal of Field Robotics, (in press)

McPhail, S., (2009), Autosub6000: A Deep Diving Long Range AUV, Journal of Bionic Engineering 6, 55-62

Eligibility and how to apply:

To apply for this project, use the: apply for a NEXUSS CDT studentship

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