Exploiting Adaptive and Collaborative AUV Autonomy for Detection and Characterization of Internal Waves
Author(s)Petillo, Stephanie; Schmidt, Henrik
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Advances in the fields of autonomy software and environmental sampling techniques for autonomous underwater vehicles (AUVs) have recently allowed for the merging of oceanographic data collection with the testing of emerging marine technology. The Massachusetts Institute of Technology (MIT) Laboratory for Autonomous Marine Sensing Systems (LAMSS) group conducted an Internal Wave Detection Experiment in August 2010 with these advances in mind. The goal was to have multiple AUVs collaborate autonomously through onboard autonomy software and real-time underwater acoustic communication to monitor for the presence of internal waves by adapting to changes in the environment (specifically the temperature variations near the thermocline/pycnocline depth). The experimental setup, implementation, data, deployment results, and internal wave detection and quantification results are presented in this paper.
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering; Woods Hole Oceanographic Institution
IEEE Journal of Oceanic Engineering
Institute of Electrical and Electronics Engineers (IEEE)
Petillo, Stephanie, and Henrik Schmidt. “Exploiting Adaptive and Collaborative AUV Autonomy for Detection and Characterization of Internal Waves.” IEEE J. Oceanic Eng. 39, no. 1 (January 2014): 150–164.