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Uniform Depth Channel Flow: Problem Solving

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Related Experiment Video

Updated: Jul 12, 2026

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

Uncertainty-aware estimation, planning, and control for tracking multiple drifting patches in flow fields.

Daniel O Akanji1, Krishnanand N Kaipa1, Cong Wei1

  • 1Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA, United States.

Frontiers in Robotics and AI
|July 11, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an uncertainty-aware framework for tracking marine surface patches using autonomous vehicles. The system ensures bounded uncertainty for persistent monitoring, outperforming baseline methods in simulations.

Keywords:
HF-radar replayLQRautonomous marine vehiclesboundary fusionflow-field estimationpatch trackingpersistent monitoringreceding-horizon scheduling

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Image-based Lagrangian Particle Tracking in Bed-load Experiments
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Last Updated: Jul 12, 2026

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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Published on: February 27, 2016

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10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

Area of Science:

  • Marine robotics
  • Oceanographic monitoring
  • Autonomous systems

Background:

  • Persistent tracking of dynamic marine surface phenomena is challenging due to spatiotemporal current variations.
  • Quantifying and managing uncertainty in autonomous vehicle operations is crucial for reliable data collection.

Purpose of the Study:

  • To develop an uncertainty-aware framework for persistent tracking of advected surface patches using autonomous marine vehicles.
  • To ensure bounded uncertainty for reliable monitoring of evolving marine phenomena.

Main Methods:

  • A replay-based framework combining local flow estimation, covariance-aware patch-boundary propagation, and intermittent boundary fusion.
  • Utilizing a flow-aware gain-scheduled linear quadratic regulator (LQR) for optimal vehicle speed control.
  • Implementing a duration-weighted predictive scheduler for efficient multi-patch mission planning.

Main Results:

  • Demonstrated bounded sawtooth uncertainty behavior in simulations using real-world current data.
  • The proposed scheduler outperformed nearest-patch and round-robin baselines in reducing mean patch uncertainty.
  • Achieved lower control-effort proxy compared to baseline methods in spatially separated patch configurations.

Conclusions:

  • Combining uncertainty-aware propagation with cost-aware scheduling is effective for persistent monitoring of marine surface phenomena.
  • The framework ensures bounded uncertainty under feasible revisit conditions, crucial for long-term observations.
  • The developed methods offer a viable strategy for enhancing the capabilities of autonomous marine vehicles in dynamic environments.