Redefining Optimal Coverage Path Planning for FLS‐Equipped AUVs With Deep Reinforcement Learning

ABSTRACT 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.