Innovative launch and recovery of low-cost, compact underwater vehicles to form scalable ocean sensor networks

Project Rationale:
This project will develop scalable approaches to form distributed networks of sensors by launching and recovering small, low-cost autonomous underwater vehicles (AUVs) from the surface or air. This ability to rapidly form adaptable networks of sensors presents a paradigm shift for ocean monitoring.
Traditional strategies for ocean monitoring considered single platforms equipped with multiple sensors either, i) travel through an environment making multi-parameter measurements to measure their continuous variation over large spatial extents, as in the case of conventional AUVs , or ii) fixed at a point making multi-parameter measurements over long periods of time, as in the case of buoys, moorings or seafloor observatories. However, environmental conditions (e.g. hyperoxia, plankton blooms, oil spills) vary both through space and time, and marine scientists do not currently have effective strategies to monitor both aspects simultaneously. While advances in low-cost robotic platforms such as Planet Ocean’s micro and mini ecoSubs provide the potential to form distributed sensor networks, this relies on their being effective methods to rapidly launch these platforms at specific locations, and recover them to extract environmental data in a scalable way.

Methodology:
This project will investigate different strategies for the fully autonomous delivery of micro-AUVs (micro-ecoSubs) en-mass using unmanned autonomous surface vehicles (USVs), such as the UoS’s C-Cat 3, and unmanned aerial vehicles (UAVs), such as the UoS Spotter vehicle. These micro-AUVs will form rapid networks of sensors within a given target region. The PhD will develop strategies to optimize the locations at which to deploy the micro-AUVs and the sensors that each node should have to form an effective sensing network. Once deployed, each micro-AUVs will perform local sampling of the environment and wirelessly transmit this data to a centralized database to combine the measurements of multiple platforms. There is however, the need to recover these platforms for recalibration, reenergizing and reuse in an autonomous way, using USVs guided by UAVs for fine relative
This PhD will consider these aspects of planning, i.e. determining the size, shape and local sampling strategies of the network through simulations using existing data from events such as the Deep Water Horizon Oil Spill, physical delivery from surface and air to realise this network, and recovery. Prototypes for the mechanical delivery and recovery systems will be developed together with the necessary sensors and strategies to achieve repeatable recover in the presence of winds, current and wave. Experiments will be performed using a small number of platforms under controlled conditions and in field environments.


Training:
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted in the School of Engineering and Planet Ocean. Specific training will include:
Operation of Planet Ocean”s micro-ecoSub (owned by UoS) and mini-ecoSub (owned by Planet Ocean) platforms
Operation of UoS owned CCat 3
Operation of the UoS Spotter UAV