Center for Embedded Network Sensing

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver one or many vehicles to high-valued locations to collect data with scientific merit. We consider the use of ocean model predictions to determine the locations to be visited by a team of AUVs, which then provides near-real time, in situ measurements back to the model to increase model skill and the accuracy of future predictions. Iterative application of this procedure determines relevant points of interest that allow the AUV fleet to monitor and track a chosen oceanographic feature. For this study, we select the ocean feature to be a freshwater plume, as their colder, nutrient-rich water promotes productivity, and may result in the formation of a Harmful Algal Bloom (HAB). Monitoring and predicting the formation and evolution of HABs is an area of active research for southern California coastal communities due to their production of harmful toxins that can affect humans and marine wildlife. The movement of the freshwater plumes is predicted by use of the Regional Ocean Modeling System (ROMS) oceanic model applied to our primary area of interest, the Southern California Bight (SCB). Based on the chosen feature, the ROMS prediction, the number of AUVs, each vehicle's operational velocity and the duration of the sampling mission, an algorithm determines waypoints (sampling locations) for the AUV(s) to visit. A trajectory for each vehicle is then generated based on the computed waypoints. We present samples of these trajectories and their implementation results for single and multiple-vehicle experiments that were conducted off the coasts of Los Angeles and Catalina Island. This research represents a first approach to an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks.