A Deep Reinforcement Learning Approach for Navigation and Control of Autonomous Underwater Vehicles in Complex Environments

The comprehension of the underwater environment is recently being accelerated by technological advances in sensors, robotics and Artificial Intelligence (AI). At the forefront of this evolution, lies the Autonomous Underwater Vehicle (AUV), a sophisticated ocean exploration tool that is capable of performing underwater mapping, leveraging data obtained by onboard sensors. AUVs can navigate autonomously in unknown environments without any human interaction, while their level of autonomy is tightly linked to their path planning strategy. In this study, we perform a comparative analysis of a Deep Reinforcement Learning (DRL) method utilising two neural network models, a Linear Model (LM) that consists only of linear layers, and a Convolutional Model (CM) that consists of convolution layers for feature extraction that are merged with linear layers. Our evaluation focuses on assessing the performance of the proposed models for generating optimal paths in 3D underwater environments based on path length and obstacle avoidance. Through comprehensive simulations, we showcase the efficiency of our solution and present a comprehensive framework tailored for solving path planning problems in 3D complex underwater settings.