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Please use this identifier to cite or link to this item: http://hdl.handle.net/2014/41665

Title: The Telesupervised Adaptive Ocean Sensor Fleet (TAOSF) Architecture: coordination of multiple oceanic robot boats.
Authors: Elfes, Alberto
Podnar, Gregg W.
Dolan, John M.
Stancliff, Stephen
Lin, Ellie
Hosler, Jeffrey C.
Ames, Troy J.
Higinbotham, John
Moisan, John R.
Moisan, Tiffany A.
Kulczycki, Eric A.
Keywords: Telesupervised Adaptive Ocean Sensor Fleet (TAOSF) Architecture
telesupervised architecture
robot boats
harmful algal blooms (HABs)
OASIS robot vessels
sensor web
ocean sensing
multirobot systems
OASIS Mission Operations Environment (MOE)
Issue Date: 1-Mar-2008
Publisher: Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2008.
Citation: IEEE Aerospace Conference, Big Sky Montana, March 1, 2008.
Abstract: Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth`s climate and ecology. Ocean sensing is typically done with satellites, buoys, and crewed research ships. The limitations of these systems include the fact that satellites are often blocked by cloud cover, and buoys and ships have spatial coverage limitations. This paper describes a multi-robot science exploration software architecture and system called the Telesupervised Adaptive Ocean Sensor Fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the OASIS platforms, to enable in-situ study of phenomena in the ocean/atmosphere interface, as well as on the ocean surface and sub-surface. The OASIS platforms are extended deployment autonomous ocean surface vehicles, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a sliding autonomy control architecture, where the operating mode of the vessels ranges from autonomous control to teleoperated human control. TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We discuss the overall TAOSF architecture, describe field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.
URI: http://hdl.handle.net/2014/41665
Appears in Collections:JPL TRS 1992+

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