- 1CERENA/DER, Instituto Superior Técnico (IST), Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
- 2Underwater Systems and Technology Laboratory (LSTS), Faculdade de Engenharia, Universidade do Porto (FEUP), Porto, Portugal
- 3Associate Laboratory for Energy, Transports and Aerospace (LAETA), Faculdade de Engenharia, Universidade do Porto (FEUP), Porto, Portugal
- 4+ATLANTIC CoLAB, Museu das Comunicações, Rua do Instituto Industrial 16, 1200-225, Lisbon, Portugal
Autonomous Underwater Vehicle (AUV) trajectory planning for oceanographic surveys should ensure comprehensive data collection for enhanced mission success. By strategically navigating and targeting high-value data points, the AUV can operate longer and gather more essential information for ocean modelling. Here, we propose a geostatistical modelling workflow to predict ocean temperature with spatial uncertainty maps, representing regions with limited knowledge about the ocean properties from where navigation paths can be devised.
A real autonomous oceanographic survey performed off W. Portugal illustrates the proposed modelling workflow. To spatially predict ocean temperature and uncertainty for the ‘day after’, we use Direct Sequential Simulation[1]. We also use the CMEMS[2] product of Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast as experimental data to constrain the spatial predictions. During the survey, the daily updated numerical ocean model is downloaded to accommodate new information, and the AUV data is assimilated and used in new geostatistical predictions.
At the beginning of the survey, we predict the ‘day after’ based on the previous 14 days, a spatiotemporal covariance matrix and the CMEMS[2] product as experimental data without uncertainty. The pointwise median model of an ensemble of geostatistical realizations is used as the most likely model, while the pointwise standard deviation model is used as an uncertainty measurement. This uncertainty map is used to devise the navigation strategy using a prize-collecting vehicle routing problem solver. At the end of each day, the data acquired by the AUV is assimilated to contain the prediction of the ocean temperature for the following day along with the updated CMEMS[2] ocean model.
The results show that the proposed methodology efficiently predicts daily ocean temperature and its spatial uncertainty and assimilates data from different sources. The AUV was able to sample ocean regions associated with higher uncertainty (i.e., variability).
References
[1] Soares, A., 2001, Direct sequential simulation and cosimulation: Mathematical Geology, 33, 911–926, doi: 10.1023/A:1012246006212.
[2] Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00027 (Accessed between 14 to 25-Oct-2024)
How to cite: Duarte, A. F., Bernacchi, L., Mendes, R., Borges de Sousa, J., and Azevedo, L.: Uncertainty maps as a tool for efficient AUV data collection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-971, https://doi.org/10.5194/egusphere-egu25-971, 2025.
