Artificial intelligence models applied to the tracking of marine vehicles (and organisms) with machine learning methodologies

ICM-CRM Meeting 2023: New Bridges between Marine Sciences and Mathematics, 2-10 November 2023Reinforcement learning (RL) techniques can be used as a path-planning method to localize and track underwater targets using range-only techniques and autonomous vehicles. Topics of interest cover the optimization of RL algorithms in terms of processing and execution time in order to train agents more quickly. In order to reduce the sim-to-real gap, some strategies use a high-fidelity simulation environment that includes characteristics and dynamics of the marine environment. Then the transfer of control policies learned in this simulated environment are suitable for the real environment, guaranteeing certain control dynamics. Further information can be found at • Masmitja, I.; Gomariz, S.; Del Rio, J.; Kieft, B.; O’Reilly, T.C.; Bouvet, P.; Aguzzi, J. Optimal path shape for range-only underwater target localization using a Wave Glider. International journal of robotics research (IJRR). ISSN 0278-3649 pp. 1 – 16 (2018). • Masmitja, I.; Martin, M.; Katija, K.; Gomariz, S.; Navarro, J. A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles. IEEE 18th International Conference on Automation Science and Engineering (CASE), August 20-24, 2022. Mexico City, Mexico (2022)Peer reviewe