AUV Velocity

Accurately determining the velocity of autonomous underwater vehicles (AUVs) is crucial for precise navigation and mission success, particularly in challenging underwater environments. Current research focuses on improving velocity estimation, especially when Doppler Velocity Log (DVL) measurements are incomplete or unavailable, employing deep learning architectures like neural networks (including Set-Transformers) to either regress missing DVL beam data or directly forecast velocity in outage scenarios. These advancements leverage both simulated and real-world data to enhance AUV control and navigation capabilities, ultimately improving the reliability and efficiency of underwater operations.