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Data-Enabled Predictive Control with Predictive Adaptive Line-of-Sight Guidance for 3-D Path Following of Autonomous Underwater Vehicles
arXiv:2510.25309v3 Announce Type: replace Abstract: This paper presents a fully data-driven 3-D path-following framework for autonomous underwater vehicles (AUVs), a representative class of underwater field robotics, based on Data-Enabled Predictive Control (DeePC). The approach eliminates explicit hydrodynamic modeling by exploiting measured input-output trajectories to predict and optimize future system behavior. Classic DeePC is employed for heading control, while a cascaded DeePC...
US, UK and Australia to develop underwater drones through defence pact
Grace the GREYSHARK Autonomous underwater vehicle (AUV) or underwater drone, developed by EUROATLAS undergoing undersea testing in the Baltic Sea in Damp, Northern German, Friday, Nov. 21, 2025. (AP Photo/Ebrahim Noroozi)
3D Underwater Path Planning via Generative Flow Field Surrogates
arXiv:2606.06077v1 Announce Type: new Abstract: Autonomous underwater vehicle (AUV) launch and recovery (LAR) into the hull of an advancing host platform requires traversal of a complex, three-dimensional propeller wake whose hydrodynamic structure cannot be characterised by a uniform current model. High-fidelity Reynolds-Averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) simulations resolve this structure with sufficient accuracy for path planning, but their computational cost...
Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
arXiv:2606.08513v1 Announce Type: new Abstract: Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end-to-end Deep Reinforcement Learning (DRL) approach that maps raw sensor data directly to thruster commands, reducing manual engineering. We propose a hierarchical reinforcement learning (HRL) architecture splitting the problem into two Markov Decision...