@INPROCEEDINGS{8559107,
  author={T. {Marques} and K. {Lima} and M. {Ribeiro} and A. S. {Ferreira} and J. B. {Sousa} and R. {Mendes}},
  booktitle={2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)}, 
  title={Characterization of Highly Dynamic Coastal Environments, Employing Teams of Heterogeneous Vehicles: A Holistic Case Study}, 
  year={2018},
  volume={},
  number={},
  pages={1-8},
  abstract={Collecting, analyzing and characterizing data from highly dynamic oceanic environments can be logistically taxing and costly, specially when done over long intervals of time using traditional manned methods. This usually discourages attempts at obtaining data of high spatial and temporal resolution. Nevertheless, through the use of teams of networked autonomous vehicles, these costs can be reduced and data collection and post-processing methods can be ameliorated. The work presented follows multi-vehicle operational scenarios, in a coastal environment, during large scale joint venture exercise with the Portuguese Navy, with the intent of collecting high spatio-temporal resolution data, over the course of a 2 week campaign, yielding promising results.},
  keywords={autonomous underwater vehicles;oceanographic techniques;highly dynamic coastal environments;heterogeneous vehicles;holistic case study;collecting characterizing data;analyzing characterizing data;highly dynamic oceanic environments;traditional manned methods;high spatial resolution;networked autonomous vehicles;data collection;multivehicle operational scenarios;coastal environment;scale joint venture exercise;high spatio-temporal resolution data;dat post-processing methods;time 2.0 week;Sea measurements;Ocean temperature;Sea surface;Snow;Software;Optical surface waves},
  doi={10.1109/OCEANSKOBE.2018.8559107},
  ISSN={},
  month={May},}