Cruise Speed Model Based on Self-Attention Mechanism for Autonomous Underwater Vehicle Navigation

Abstract

This study proposes a cruise speed model based on the Self-Attention mechanism for speed estimation in Autonomous Underwater Vehicle (AUV) navigation systems. By utilizing variables such as acceleration, angle, angular velocity, and propeller speed as inputs, the Self-Attention mechanism is constructed using Long Short-Term Memory (LSTM) for handling the above information, enhancing the model’s accuracy during persistent bottom-track velocity failures. Additionally, this study introduces the water-track velocity information to enhance the generalization capability of the network and improve its speed estimation accuracy. The sea trial experiment results indicate that compared to traditional methods, this model demonstrates higher accuracy and reliability with both position error and velocity error analysis when the used Pathfinder DVL fails, providing an effective solution for AUV combined navigation systems.

Keywords

• AUV
• navigation
• deep learning
• speed estimation
• self-attention mechanism