Neural-Network-Based AUV Navigation for Fast-Changing Environments

Abstract

For an autonomous underwater vehicle (AUV), navigation is a key functionality. Dead-reckoning (DR) navigation is an important class among all the AUV navigation methods. In DR, the measurement errors of inertial sensors (such as gyroscopes and accelerometers) lead to accumulated errors with time, which affect navigation accuracy significantly. Especially, accumulated errors in fast-changing environments, such as waves near or on the surface, are tough to handle. In this article, we propose a neural-network-based AUV navigation method for fast-changing environments, called NN-DR. NN-DR employs the neural network to predict pitch angles accurately, which is our core contribution. In NN-DR, we smoothly integrate the Kalman filter, neural network, and velocity compensation to reduce accumulated errors. Extensive simulation experiments are conducted to test the correctness and stability of NN-DR, and the results show that NN-DR is very effective in lowering accumulated errors. For instance, at time 300 s, NN-DR achieves superior performance on accuracy for navigation, about 160 times than the state-of-the-art DR methods, demonstrating great advantage on AUV navigation for fast-changing environments.