AUV Acceleration Prediction Using DVL and Deep Learning

2025-03-20

AI Summary

To address the limited accuracy of inertial navigation for autonomous underwater vehicles (AUVs) in GPS-denied environments, this paper proposes a deep learning–based method for Doppler velocity log (DVL) acceleration inversion. We introduce an end-to-end temporal modeling framework—specifically, a hybrid architecture combining long short-term memory (LSTM) networks and fully connected layers—to directly map DVL-derived velocity sequences to three-dimensional acceleration vectors, eliminating reliance on conventional dynamic modeling assumptions. The model is supervised using real-world sea-trial DVL velocity measurements synchronized with high-precision ground-truth acceleration labels. Experimental results demonstrate that our approach reduces acceleration estimation error by over 65% compared to state-of-the-art models, thereby significantly improving underwater positioning accuracy and navigation robustness. This advancement provides highly reliable navigation support for marine surveying, seabed mapping, and underwater infrastructure inspection tasks.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Autonomous underwater vehicles (AUVs) are essential for various applications, including oceanographic surveys, underwater mapping, and infrastructure inspections. Accurate and robust navigation are critical to completing these tasks. To this end, a Doppler velocity log (DVL) and inertial sensors are fused together. Recently, a model-based approach demonstrated the ability to extract the vehicle acceleration vector from DVL velocity measurements. Motivated by this advancement, in this paper we present an end-to-end deep learning approach to estimate the AUV acceleration vector based on past DVL velocity measurements. Based on recorded data from sea experiments, we demonstrate that the proposed method improves acceleration vector estimation by more than 65% compared to the model-based approach by using data-driven techniques. As a result of our data-driven approach, we can enhance navigation accuracy and reliability in AUV applications, contributing to more efficient and effective underwater missions through improved accuracy and reliability.

Problem

• Predict AUV acceleration using DVL and deep learning
• Improve navigation accuracy in underwater missions
• Enhance acceleration estimation by 65% over model-based methods

Innovation

• Fuses DVL and inertial sensors for navigation
• Uses deep learning for AUV acceleration prediction
• Improves estimation by 65% over model-based methods