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Trevor Harrison Senior Research Engineer twharr@uw.edu Phone 206-543-1371 |
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Publications |
2000-present and while at APL-UW |
A modular control aid for profiling floats with a Gulf Stream case study Tolone, J., T. Harrison, T. Curtin, Z. Szuts, and D.A. Paley, "A modular control aid for profiling floats with a Gulf Stream case study," In Proc., OCEANS 2025 Great Lakes, Chicago, 29 September 2 October 2025, doi:10.23919/OCEANS59106.2025.11245129 (IEEE, 2025). |
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25 Nov 2025 |
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This work presents a conceptual framework, called FloatCast, for the control of a small fleet of buoyancy-controlled ocean profiling floats. The control objective is to maximize sampling coverage in a given region of interest. The framework optimizes park depth and park duration commands for each float in the fleet. FloatCast uses an Echo State Network to make a sea level anomaly forecast, which is converted into a surface flow forecast. This flow forecast informs a Lagrangian particle model of drifting vehicle dynamics. The state-space model of the float dynamics uses candidate sets of commands to predict float trajectories, which are evaluated using a mapping error scoring metric. Stochastic analysis illustrates a risk-reward tradeoff between uncertainty and potential coverage for candidate float commands. This paper introduces each of these components of FloatCast and presents initial simulation results using float data from a deployment in the Gulf Stream from July 2024. |
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Robust underwater localization of buoyancy driven μFloats using acoustic time-of-flight measurements Abrar, M.M., and T.W. Harrison, "Robust underwater localization of buoyancy driven μFloats using acoustic time-of-flight measurements," Proc., OCEANS Great Lakes, 29 September - 2 October, Chicago, doi:10.23919/OCEANS59106.2025.11245003 (IEEE, 2025). |
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25 Nov 2025 |
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Accurate underwater localization remains a challenge for inexpensive autonomous platforms that require high-frequency position updates. In this paper, we present a robust, low-cost localization pipeline for buoyancy-driven μFloats operating in coastal waters. We build upon previous work by introducing a bidirectional acoustic Time-of-Flight (ToF) localization framework, which incorporates both float-to-buoy and buoy-to-float transmissions, thereby increasing the number of usable measurements. The method integrates nonlinear trilateration with a filtering of computed position estimates based on geometric cost and CramérRao Lower Bounds (CRLB). This approach removes outliers caused by multipath effects and other acoustic errors from the ToF estimation and improves localization robustness without relying on heavy smoothing. We validate the framework in two field deployments in Puget Sound, Washington, USA. The localization pipeline achieves median positioning errors below 4 m relative to GPS positions. The filtering technique shows a reduction in mean error from 139.29 m to 12.07 m, and improved alignment of trajectories with GPS paths. Additionally, we demonstrate a Time-Difference-of-Arrival (TDoA) localization for unrecovered floats that were transmitting during the experiment. Range-based acoustic localization techniques are widely used and generally agnostic to hardware-this work aims to maximize their utility by improving positioning frequency and robustness through careful algorithmic design. |
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FlowPilot: Shoreside autonomy for profiling floats Szuts, Z., T. Harrison, T. Curtin, B. Kirby, and B. Ma, "FlowPilot: Shoreside autonomy for profiling floats," Proc., OCEANS, 25-28 September, Biloxi, MS, doi:10.23919/OCEANS52994.2023.10337384 (MTS/IEEE, 2023). |
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11 Dec 2023 |
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Over the last twenty years, profiling floats have revolutionized ocean observations with globally distributed Lagrangian arrays performing fixed vertical sampling cycles. Here we investigate adaptive sampling with an array of inter-dependent floats guided by a software package called FlowPilot, which uses all available float measurements to select park depths that provide favorable drifts based on sampling goals. Drift predictions are performed with multiple prediction methods, including methods that use float data (drift velocity, geostrophic velocity calculations) or from external sources like numerical ocean forecast models. A skill-based weight is assigned to each method based on how accurately it predicts recent drifts. With this generalized approach to prediction, disparate methods can be combined numerically to permit multi-method optimization. The emergent skill of FlowPilot is tested and quantified by numerical simulations that minimize dispersion by keeping a grid of floats close to the center of the deployment box. |
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