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Talk-15: Autonomous and Adaptive Front Tracking using AUVs in an MSEAS Dynamic Ocean Model
Stephanie Petillo, Laboratory for Autonomous Marine Sensing Systems, Massachusetts Institute of Technology (MIT)
The purpose of this work is to develop a MOOS-IvP behavior, BHV_FrontTrack, that allows an AUV to autonomously and adaptively track the edge of a dynamic ocean front in real time. I will discuss this concept, how I implemented it in simulation, and some results. To test BHV_FrontTrack, I have (re-)incorporated an MSEAS ocean model of the Mid-Atlantic Bight region into the LAMSS simulation environment using pOctaverMIT (a spin-off of Arjan Vermeij's pOctaver) and an MSEAS model-reading script provided by the MSEAS group. This setup replaces the iMseas setup used previously.
The MSEAS model includes a 3D dynamic temperature front that runs along the shelf break in the Mid-Atlantic Bight. I will show results from Front Tracking simulations in which the AUV tracked a static version of this temperature front at constant depth for over 55km, and preliminary results from tracking the dynamic front. The challenge of tracking and characterizing a front in 3D will also be addressed, as well as ways to improve efficiency and synopticity of sampling the front in 2D and 3D using multiple AUVs.
Categories:
- Autonomous Underwater Vehicles (AUVs)
- Oceanographic Sampling
- MOOS-IvP
- IvP Behaviors
- MSEAS
- pOctaver