Redefining Optimal Coverage Path Planning for FLS-Equipped AUVs with Deep Reinforcement Learning

Autonomous Underwater Vehicles (AUVs) have emerged as indispensable tools for a variety of subsea tasks, from habitat monitoring and seabed mapping to infrastructure inspection and mine countermeasures. A fundamental challenge in this field is Coverage Path Planning (CPP), the problem of ensuring complete and efficient area coverage. Within this research activity, we propose a Deep Reinforcement Learning (DRL)-based framework for CPP in underwater environments using a Forward-Looking Sonar (FLS). We validate the proposed methodology through simulation experiments comparing it with the classical lawnmower path and a state-of-the-art sampling-based algorithm. Results demonstrate that our DRL-based solution outperforms these baseline approaches in terms of coverage time per unit area and path length. Additionally, we present on-field deployment outcomes on FeelHippo AUV, showcasing the feasibility and practicality of our framework in real-world underwater missions.