Real, Marta, Hernández, Juan David  ORCID: https://orcid.org/0000-0002-9593-6789, Palomeras, Narcís and Carreras, Marc
2026.
GOSSIPP: Graph and observability-based signal synchronized informative path planning for active localization of benthic fauna.
IEEE Robotics and Automation Letters
11
(10)
, pp. 11094-11101.
10.1109/LRA.2026.3719196
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Abstract
A way to assess the effectiveness of marine protected areas (MPAs) is to study and analyze the fauna dynamics within them. To do so, an option is to attach acoustic pingers to some specimens and track their movement within an interval of time. Such analysis cannot be limited to presence detection, but instead requires fine-scale tracking, which can be challenging due to the specimens' unknown distribution, as well as the pingers' noisy and low-rate signals. These requirements and challenges render traditional approaches that use preplanned survey trajectories insufficient, thus requiring adaptable strategies such as informative path planning (IPP), which can adjust the trajectories based on the information obtained while conducting the survey. Most existing IPP strategies rely on continuous sensor data to replan the trajectories. However, due to the acoustic signal sparsity, these strategies cannot be applied in our case. Therefore, in this work, we propose a novel observability-based IPP approach that maps the specimens in a 3D space using an autonomous underwater vehicle (AUV), while also localizing several specimens simultaneously. We validate our approach with both an extensive simulation benchmark that considers different plausible arrangements of the benthic fauna, plus various experiments at sea. The results demonstrate that our approach enables the localization and mapping of the specimens within a specified uncertainty.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Additional Information: | RRS policy applied |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 2377-3766 |
| Date of First Compliant Deposit: | 28 July 2026 |
| Last Modified: | 02 Sep 2026 14:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/188393 |
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