Sampling over a large area is a common task for an Autonomous Underwater Vehicle (AUV) and a lawnmower survey is often the best approach to tasks such as mapping ocean bathymetry. However, monitoring a rapidly evolving feature, for example a Harmful Algal Bloom (HAB), requires an array of sensing technology including satellite, aircraft, ship and mooring based sensors to achieve the spatial and temporal resolution required. Alternatively, an AUV swarm, consisting of 20 or more small, simple, low cost AUVs, can be deployed to monitor a HAB, reducing the financial risk, requiring almost no existing infrastructure and improving data quality.
Once the AUV swarm is deployed, a coverage control algorithm allows the distribution of AUVs over an area to be controlled and hence a desired sampling resolution achieved. Furthermore, an intelligent coverage control algorithm allows the AUVs to learn the distribution of sensory information in the environment and hence autonomously adapt their distribution to focus on areas of high interest, improving data quality. My research aims to implement an intelligent coverage control algorithm, adapted to be robust to the challenges faced in the ocean environment, in an AUV swarm.