Please use this identifier to cite or link to this item: https://doi.org/10.1109/AERO.2010.5446668
DC FieldValue
dc.titleTelesupervised remote surface water quality sensing
dc.contributor.authorPodnar, G.
dc.contributor.authorDolan, J.M.
dc.contributor.authorLow, K.H.
dc.contributor.authorElfes, A.
dc.date.accessioned2013-07-04T07:57:26Z
dc.date.available2013-07-04T07:57:26Z
dc.date.issued2010
dc.identifier.citationPodnar, G.,Dolan, J.M.,Low, K.H.,Elfes, A. (2010). Telesupervised remote surface water quality sensing. IEEE Aerospace Conference Proceedings. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/AERO.2010.5446668" target="_blank">https://doi.org/10.1109/AERO.2010.5446668</a>
dc.identifier.isbn9781424438884
dc.identifier.issn1095323X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40135
dc.description.abstractWe present a fleet of autonomous Robot Sensor Boats (RSBs) developed for lake and river fresh water quality assessment and controlled by our Multilevel Autonomy Robot Telesupervision Architecture (MARTA).12 The RSBs are low cost, highly maneuverable, shallow draft sensor boats, developed as part of the Sensor Web program supported under the Advanced Information Systems Technology program of NASA's Earth Systems Technology Office. They can scan large areas of lakes, and navigate up tributaries to measure water quality near outfalls that larger research vessels cannot reach. The MARTA telesupervision architecture has been applied to a number of domains from multi-platform autonomous wide area planetary mineral prospecting, to multi-platform ocean monitoring. The RSBs are a complementary expansion of a fleet of NOAA/NASA-developed extended-deployment surface autonomous vehicles that enable in-situ study of meteorological factors of the ocean/atmosphere interface, and which have been adapted to investigate harmful algal blooms under this program. The flexibility of the MARTA telesupervision architecture was proven as it supported simultaneous operation of these heterogenous autonomous sensor platforms while geographically widely separated. Results and analysis are presented of multiple tests carried out over three months using a multi-sensor water sonde to assess water quality in a small recreational lake. Inference Grids were used to produce maps representing temperature, pH, and dissolved oxygen. The tests were performed under various water conditions (clear vs. hair algae-laden) and both before and after heavy rains. Data from each RSB was relayed to a data server in our lab in Pittsburgh, Pennsylvania, and made available over the World Wide Web where it was acquired by team members at the Jet Propulsion Laboratory of NASA in Pasadena, California who monitored the boats and their sensor readings in real time, as well as using these data to model the water quality by producing Inference Grid-based maps. ©2010 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/AERO.2010.5446668
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/AERO.2010.5446668
dc.description.sourcetitleIEEE Aerospace Conference Proceedings
dc.identifier.isiutNOT_IN_WOS
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