Cooperative Aquatic Sensing using the Telesupervised Adaptive Ocean Sensor Fleet
Abstract
Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth's climate and ecology. Typical ocean sensing is done with satellites or in situ buoys and research ships which are slow to reposition. Cloud cover inhibits study of localized transient phenomena such as Harmful Algal Blooms (HAB). A fleet of extended-deployment surface autonomous vehicles will enable in situ study of characteristics of HAB, coastal pollutants, and related phenomena. We have developed a multiplatform telesupervision architecture that supports adaptive reconfiguration based on environmental sensor inputs. Our system allows the autonomous repositioning of smart sensors for HAB study by networking a fleet of NOAA OASIS (Ocean Atmosphere Sensor Integration System) surface autonomous vehicles. In situ measurements intelligently modify the search for areas of high concentration. Inference Grid and complementary information-theoretic techniques support sensor fusion and analysis. Telesupervision supports sliding autonomy from high-level mission tasking, through vehicle and data monitoring, to teleoperation when direct human interaction is appropriate. This paper reports on experimental results from multi-platform tests conducted in the Chesapeake Bay and in Pittsburgh, Pennsylvania waters using OASIS platforms, autonomous kayaks, and multiple simulated platforms to conduct cooperative sensing of chlorophyll-a and water quality.
BibTeX
@conference{Dolan-2009-10319,author = {John M. Dolan and Gregg Podnar and Stephen B. Stancliff and Kian Hsiang Low and Alberto Elfes and John Higinbotham and Jeffrey Hosler and Tiffany Moisan and John Moisan.},
title = {Cooperative Aquatic Sensing using the Telesupervised Adaptive Ocean Sensor Fleet},
booktitle = {Proceedings of SPIE Remote Sensing of the Ocean, Sea Ice, and Large Water Regions},
year = {2009},
month = {September},
volume = {7473},
}