Navigation of underwater vehicles Decentralized estimation Distributed sensor networks Scalable navigation algorithms for multiple robotic vehicles
Dr. Webster earned a B.S. in Mechanical Engineering from MIT in 2000, after which she worked at Woods Hole Oceanographic Institution in the Deep Submergence Laboratory. While there she designed excavation tools for the remotely operated vehicle (ROV) Hercules to carry out archaeological excavations of shipwrecks, and was part of the ROV Jason operations team.
She returned to graduate school at Johns Hopkins University in 2004, where she led the design of an acoustic communication system for combined communication and navigation on underwater vehicles, earning an M.S. (2007) and Ph.D. (2010), both in Mechanical Engineering.
Dr. Webster spent a year and a half working as a systems engineer on the Ocean Observatories Initiative (OOI) at the Consortium for Ocean Leadership in Washington, DC. before moving to the Applied Physics Laboratory at the University of Washington, where she is currently a Senior Research Engineer, working on long-range navigation, glider-based navigation, and autonomy, particularly for Arctic applications.
B.S. Mechanical Engineering, Massachusetts Institute of Technology, 2000
M.S. Mechanical Engineering, Johns Hopkins University, 2007
Ph.D. Mechanical Engineering, Johns Hopkins University, 2010
Persistent Ocean Observations by Robot
Successful tests of accurate acoustic navigation systems on Seaglider promise continuous, long-endurance observations of the deep ocean interior.
6 Apr 2021
2000-present and while at APL-UW
Towards real-time under-ice acoustic navigation at mesoscale ranges
Webster, S.E., L.E. Freitag, C.M. Lee, and J.I. Gobat, "Towards real-time under-ice acoustic navigation at mesoscale ranges," Proc. IEEE International Conference on Robotics and Automation, 26-30 May, Seattle, WA, 537-544, doi:10.1109/ICRA.2015.7139231 (IEEE, 2015).
26 May 2015
This paper describes an acoustic navigation system that provides mesoscale coverage (hundreds of kilometers) under the ice and presents results from the first multi-month deployment in the Arctic. The hardware consists of ice-tethered acoustic navigation beacons transmitting at 900 Hz that broadcast their latitude and longitude plus several bytes of optional control data. The real-time under-ice navigation algorithm, based on a Kalman filter, uses time-of-flight measurements from these sources to simultaneously estimate vehicle position and depth-averaged local currents. The algorithm described herein was implemented on Seagliders, a type of autonomous underwater glider (AUG), but the underlying theory is applicable to other autonomous underwater vehicles (AUVs). As part of an extensive field campaign from March to September 2014, eleven acoustic sources and four Seagliders were deployed to monitor the seasonal melt of the marginal ice zone (MIZ) in the Beaufort and northern Chukchi Seas. Beacon-to-beacon performance was excellent due to a sound duct at 100 m depth where the transmitters were positioned; the travel-time error at 200 km has a standard deviation of 40 m when sound-speed is known, and ranges in excess of 400 km were obtained. Results with the Seagliders, which were not regularly within the duct, showed reliable acoustic ranges up to 100 km and more sparse but repeatable range measurements to over 400 km. Navigation results are reported for the real-time algorithm run in post-processing mode, using data from a 295-hour segment with significant time spent under ice.
Preliminary results in under-ice acoustic navigation for Seagliders in Davis Strait
Webster, S.E., C.M. Lee, and J.I. Gobat, "Preliminary results in under-ice acoustic navigation for Seagliders in Davis Strait," Proc., OCEANS 2014, 14-19 September, St. John's Newfoundland, doi:10.1109/OCEANS.2014.7003070 (IEEE, 2014).
14 Sep 2014
This paper presents an under-ice acoustic navigation system developed for Seaglider, a buoyancy-driven autonomous underwater vehicle (AUV), and post-processed navigation results from one of fourteen glider deployments between 2006 and 2014 in Davis Strait. Seagliders typically receive all geolocation information from global positioning system (GPS) signals received while they are at the surface, and perform dead reckoning while underwater. Extended under-ice deployments, where access to GPS is denied due to the inability of the glider to surface, require an alternative source of geolocation information. In the deployments described herein, geolocation information is provided by range measurements from mooring-mounted acoustic navigation sources at fixed, known locations. In this paper we describe the navigation system used in Davis Strait and present navigation results from a six degree-of-freedom Kalman filter using post-processed navigation data.
Decentralized extended information filter for single-beacon cooperative acoustic navigation: Theory and experiments
Webster, S.E., J.M. Walls, L.L. Whitcomb, and R.M. Eustice, "Decentralized extended information filter for single-beacon cooperative acoustic navigation: Theory and experiments," IEEE Trans. Robot., 29, 957-974, doi:10.1109/TRO.2013.2252857, 2013.
1 Aug 2013
We report a decentralized extended information filter (DEIF) algorithm designed for single-beacon cooperative acoustic navigation of one or more client underwater vehicles. In single-beacon cooperative acoustic navigation, ranges and state information from a single reference source (the server) are used to improve localization and navigation of an underwater vehicle (the client). The ranges and state information are obtained using underwater acoustic modems and a synchronous-clock time-of-flight paradigm. Apart from the server's acoustic data broadcasts, the client has no access to the server's position or sensor measurements. We show that at the instance of each range measurement update, the DEIF algorithm yields identical results for the current vehicle state estimate as the corresponding centralized extended information filter (CEIF), which fully tracks the joint probability distribution between the server and client. We compare the state estimation results of the DEIF algorithm with that of a CEIF and three other filters reported in the literature. The evaluation is performed using both simulated data and an experimental dataset comprised of one surface craft and two autonomous underwater vehicles.