Document Type

Article

Publication Date

2026

DOI

10.3389/frobt.2026.1842384

Publication Title

Frontiers in Robotics and AI

Volume

13

Pages

1842384

Abstract

In this study, we present a replay-based framework for uncertainty-aware persistent tracking of multiple advected surface patches using an autonomous marine vehicle operating in spatiotemporal-varying currents. The method combines three components: local flow estimation, covariance-aware patch-boundary propagation with intermittent boundary fusion, and mission-level scheduling over multiple patches. Each patch is represented by a polygonal boundary, whose vertices are propagated through the estimated flow field while carrying per-vertex covariance, thereby quantifying uncertainty growth during advection. A flow-aware gain-scheduled linear quadratic regulator (LQR) was designed to shape the desired surge speed to take advantage of favorable currents. When the vehicle services a patch, boundary detections are fused to reduce the active patch uncertainty, and optional local map-covariance refinement is used to reduce subsequent uncertainty regrowth in the surrounding flow field. A boundedness analysis shows that if each patch is revisited within a prescribed maximum interval, then the corresponding patch uncertainty remains uniformly bounded; a companion feasibility condition relates the allowable revisit interval to vehicle speed, service time, and tour length over the patch set. To validate the result, a data replay simulation using HF-radar currents from the San Francisco Bay region was used to demonstrate the expected bounded sawtooth uncertainty behavior under feasible revisit conditions. In addition, our proposed duration-weighted predictive scheduler outperforms nearest-patch and round-robin baselines and, in spatially separated patch configurations, achieves lower mean patch uncertainty and lower control-effort proxy than a highest-J baseline. These results indicate that combining uncertainty-aware propagation with cost-aware scheduling is a viable strategy for persistent monitoring of evolving marine surface phenomena.

Rights

© 2026 Akanji, Kaipa and Wei.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). The use, distribution or reproduction in other forums is permitted, provided the original authors and the copyright owners are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Data Availability

Article states: "Publicly available datasets were analyzed in this study. These data can be found here: https://coastwatch.pfeg.noaa.gov/erddap/griddap/ucsdHfrW500.html."

Original Publication Citation

Akanji, D., Kaipa, K., & Wei, C. (2026). Uncertainty-aware estimation, planning, and control for tracking multiple drifting patches in flow fields. Frontiers in Robotics and AI, 13, Article 1842384. https://doi.org/10.3389/frobt.2026.1842384

ORCID

0000-0002-8095-938X (Kaipa), 0000-0002-6552-7239 (Wei)

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