The Telesupervised Adaptive Ocean Sensor Fleet (TAOSF) Architecture: Coordination of Multiple Oceanic Robot Boats

Alberto Elfes, Gregg Podnar, John M. Dolan, Stephen B. Stancliff, Ellie Lin Ratliff, Jeffrey Hosler, Troy Ames, John Higinbotham, John Moisan, Tiffany Moisan, and Eric Kulczycki
Aerospace Conference, 2008 IEEE, March, 2008, pp. 1-9.


Download
  • Adobe portable document format (pdf) (7MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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 Multilevel Autonomy Robot Telesupervision Architecture (MARTA) for multi-robot science exploration, and an embodiment of the MARTA architecture in a real-world 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 the MARTA multi-level 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).

Notes
Associated Lab(s) / Group(s): Tele-Supervised Autonomous Robotics
Associated Project(s): Telesupervised Adaptive Ocean Sensor Fleet

Text Reference
Alberto Elfes, Gregg Podnar, John M. Dolan, Stephen B. Stancliff, Ellie Lin Ratliff, Jeffrey Hosler, Troy Ames, John Higinbotham, John Moisan, Tiffany Moisan, and Eric Kulczycki, "The Telesupervised Adaptive Ocean Sensor Fleet (TAOSF) Architecture: Coordination of Multiple Oceanic Robot Boats," Aerospace Conference, 2008 IEEE, March, 2008, pp. 1-9.

BibTeX Reference
@inproceedings{Elfes_2008_6099,
   author = "Alberto Elfes and Gregg Podnar and John M Dolan and Stephen B Stancliff and Ellie Lin Ratliff and Jeffrey Hosler and Troy Ames and John Higinbotham and John Moisan and Tiffany Moisan and Eric Kulczycki",
   title = "The Telesupervised Adaptive Ocean Sensor Fleet (TAOSF) Architecture: Coordination of Multiple Oceanic Robot Boats",
   booktitle = "Aerospace Conference, 2008 IEEE",
   pages = "1-9",
   month = "March",
   year = "2008",
}