Carnegie Mellon University
Telesupervised Remote Surface Water Quality Sensing

Gregg Podnar, John M. Dolan, Kian Hsiang Low, and Alberto Elfes
2010 IEEE Aerospace Conference, March, 2010, pp. 1-9.

  • Adobe portable document format (pdf) (3MB)
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.

We 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). 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.

sensor network, autonomous vehicles, water quality, telesupervision

Associated Lab(s) / Group(s): Tele-Supervised Autonomous Robotics
Associated Project(s): Robot Sensor Boat
Number of pages: 9

Text Reference
Gregg Podnar, John M. Dolan, Kian Hsiang Low, and Alberto Elfes, "Telesupervised Remote Surface Water Quality Sensing," 2010 IEEE Aerospace Conference, March, 2010, pp. 1-9.

BibTeX Reference
   author = "Gregg Podnar and John M Dolan and Kian Hsiang Low and Alberto Elfes",
   title = "Telesupervised Remote Surface Water Quality Sensing",
   booktitle = "2010 IEEE Aerospace Conference",
   pages = "1-9",
   month = "March",
   year = "2010",