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Smart Ocean Sensing Using the Telesupervised Adaptive Ocean Sensor Fleet
| Content Provider | Semantic Scholar |
|---|---|
| Author | Dolan, John M. Podnar, Gregg Elfes, A. Stancliff, Stephen Lin, Ellie Higinbotham, John Hosler, Jeffrey C. Moisan, John Moisan, Tiffany A. |
| Copyright Year | 2008 |
| Abstract | This paper describes field test results to date using 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 Ocean-Atmosphere Sensor Integration System (OASIS) platforms, to enable in situ study of phenomena in the oceanatmosphere interface. TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a multi-level autonomy control architecture, where the operating mode of the vehicles 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 describe recent 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. Index Terms – harmful algal blooms, sensor web, ocean sensing, adaptive sampling, telesupervision, multirobot systems. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ri.cmu.edu/pub_files/2008/6/Dolan_John_A6P1.pdf |
| Alternate Webpage(s) | http://www.ri.cmu.edu/pub_files/2009/7/Dolan_John_A6P1.pdf |
| Alternate Webpage(s) | http://ri.cmu.edu/pub_files/2008/6/Dolan_John_A6P1.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
