Autonomous underwater vehicles (AUVs) path planning based on Deep Reinforcement Learning

Abstract

For autonomous underwater vehicles (AUVs), autonomous navigation in an unknown underwater environment is still a difficult problem. In recent years, people have proposed some machine learning-based methods to solve this problem, but the existing methods still cannot meet the complex and changeable underwater environment. This paper conducts technical research on the path planning of autonomous underwater vehicles, combines deep learning and reinforcement learning, uses WL interpolation surface to model the seabed, and proposes a path planning model for autonomous underwater vehicles based on deep reinforcement learning. And train the path planning model in the simulation environment, and finally achieve the goal of path planning for the underwater robot in the complex and changeable underwater environment.