arXiv:2606.06077v1 Announce Type: cross 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 renders them impractical for onboard use. We address this gap by integrating two conditional generati
The increasing sophistication of AI and computational fluid dynamics is enabling real-time autonomy in complex underwater environments, which was previously impractical due to computational costs.
This research provides a critical step towards more autonomous and reliable underwater operations, directly impacting defense capabilities, ocean exploration, and potentially energy infrastructure maintenance.
The ability to perform 3D underwater path planning in real-time, within highly turbulent environments, significantly enhances the operational effectiveness and safety of autonomous underwater vehicles.
- · Defence contractors
- · Oceanographers
- · Autonomous underwater vehicle manufacturers
- · Navies
- · Traditional manned underwater operations
- · Systems reliant on simplified current models
Improved efficiency and safety for autonomous underwater vehicle deployment and recovery in challenging conditions.
Expansion of autonomous underwater vehicle applications into more complex and hazardous environments, such as deep-sea mining or combat zones.
Potential for swarm-based autonomous underwater systems to operate effectively in turbulent conditions, fundamentally changing naval strategies and resource exploration.
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Read at arXiv cs.LG