Autonomous & adaptive oceanographic front tracking on board autonomous underwater vehicles
Name
Autonomous adaptive oceanographic.pdf
Size
1.17 MB
Format
Adobe PDF
Checksum (MD5)
15c4118a945a05e01d8bf9888ff59b4c
Author(s)
Petillo, Stephanie
Yoerger, Dana
Balasuriya, Arjuna
Schmidt, Henrik
Lermusiaux, Pierre
Petillo, Stephanie Marie
Date Issued
May 2015
Journal
Proceedings of IEEE OCEANS '15 Conference
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Petillo, Stephanie et al. “Autonomous & Adaptive Oceanographic Front Tracking on Board Autonomous Underwater Vehicles.” IEEE, 2015. 1–10.
Version
Author's final manuscript
Abstract
Oceanic fronts, similar to atmospheric fronts, occur at the interface of two fluid (water) masses of varying characteristics. In regions such as these where there are quantifiable physical, chemical, or biological changes in the ocean environment, it is possible - with the proper instrumentation - to track, or map, the front boundary. In this paper, the front is approximated as an isotherm that is tracked autonomously and adaptively in 2D (horizontal) and 3D space by an autonomous underwater vehicle (AUV) running MOOS-IvP autonomy. The basic, 2D (constant depth) front tracking method developed in this work has three phases: detection, classification, and tracking, and results in the AUV tracing a zigzag path along and across the front. The 3D AUV front tracking method presented here results in a helical motion around a central axis that is aligned along the front in the horizontal plane, tracing a 3D path that resembles a slinky stretched out along the front. To test and evaluate these front tracking methods (implemented as autonomy behaviors), virtual experiments were conducted with simulated AUVs in a spatiotemporally dynamic MIT MSEAS ocean model environment of the Mid-Atlantic Bight region, where a distinct temperature front is present along the shelfbreak. A number of performance metrics were developed to evaluate the performance of the AUVs running these front tracking behaviors, and the results are presented herein.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Ocean Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/OCEANS-Genova.2015.7271616
