Adaptive Path Planning for Tracking Ocean Fronts with an Autonomous Underwater Vehicle

Ocean fronts are productivity hot spots, supporting marine life from plankton to whales. These dynamic systems contain a vast amount of information, and have the potential to significantly expand our knowledge of aquatic ecosystems in relation to climate change. However, ocean fronts and other dynamic features cannot be studied through conventional oceanographic techniques. In this paper, we address the problem of sampling and tracking an ocean front with an Autonomous Underwater Vehicle based on predictions and/or priors provided by a heterogeneous team of assets and ocean models. Specifically, given a prior (that may not be accurate or up-to-date) we present a method for an underwater vehicle to plan a mission and adapt this mission on-the-fly to track a dynamic feature. Results from field trials are presented, and demonstrate that the vehicle is able to adapt its path to follow a desired contour.