Learning-based Autonomous Underwater Vehicle Navigation Following Human Actions in Confined Environment

Navigation of autonomous underwater vehicles (AUVs) is critical for many vital tasks operated in the aquatic environment. However, the navigation problem is challenging in confined environments like caves. Since humans can usually solve such a problem with minimal effort, the main objective of this paper is to implement learning-based neural network models for AUV to learn from human actions in a confined environment. The Girona underwater dataset, acquired in a cave exploration experiment, was used for the learning task. The odometry and sonar data from the dataset were used to train two deep neural network models for AUV navigation through the underwater caves. The test data demonstrates that both models achieve relatively high accuracy for the navigation task.