Abstract
Autonomous vehicles (AVs) are commonly used in oceanic and more recently estuarine and riverine environments because they are small, versatile, efficient, moving platforms equipped with a suite of instruments for measuring environmental conditions. However, moving vessel observations, particularly those associated with Acoustic Doppler Current Profiler (ADCP) measurements, can be problematic owing to instrument noise, flow fluctuations, and spatial variability. A range of ADCPs manufactured by different companies were integrated on to an Unmanned Surface Vehicle (USV), an Unmanned Underwater Vehicle (UUV), and some additional stationary platforms and were deployed in a number of natural riverine and estuarine environments to evaluate the quality of the velocity profile over the depth, minimum averaging time interval requirements, and AV mission planning considerations. Measurements were obtained at fixed locations to eliminate any spatial variations in the mean flow characteristics. The USV has the unique capability to station-keep to within 1 m owing to its dual-propeller design, providing the best setup for spatially mapping velocity profiles. Single-propeller UUVs can perform a quasi-station-keeping (< 10 m) operation, but are designed for traveling underwater at speeds > 1 m/s. An appropriate averaging window, T *, was determined using the Kalman Algorithm with a Kalman gain equal to 1%. T * was found to be independent of depth, flow velocity, and environment. There was no correlation (R 2 = 0.18) for T * between flow magnitude and direction. Results from all measurements had a similar T * of approximately 3 min. Based on this, an averaging window of 4 min is conservatively suggested to obtain a statistically confident measure of the mean velocity profile.
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Acknowledgments
JB was supported by the NSF (OCE 0728324), ONR (N0001410WX21049) and the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. CT was supported by ONR (N0001410WX21049) and the US Navy. JM and AR were supported by ONR (N0001410WX21049, N000141010379). The USV was purchased using JM’s NPS startup funds and the UUV was supported by ONR DURIP (N0001409WR20268). We appreciate the technical support from SeaRobotic’s team (Don Darling, Hal Dewar, Rich Musco), the YSI/Oceanserver team (Ben Clarke, Tony DiSalvo, Daniel Osiecki), RDInstruments (Paul Devine, Peter Spain), NortekUSA (Judah Goldberg), and Naval Undersea Warfare Center UUV researchers (Mike Incze, Scott Sideleau). A special thanks to the Kootenai field group—Bill Swick, Ed Thornton, Will Ashley, Patrick Rynne, Todd Holland, and Tom Drake. We appreciate Nathaniel Plant’s suggestion for the Kalman Algorithm and Ed Thornton’s manuscript recommendations.
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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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Brown, J., Tuggle, C., MacMahan, J. et al. The use of autonomous vehicles for spatially measuring mean velocity profiles in rivers and estuaries. Intel Serv Robotics 4, 233–244 (2011). https://doi.org/10.1007/s11370-011-0095-6
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DOI: https://doi.org/10.1007/s11370-011-0095-6