Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning

-- Unmanned Aerial V ehicle (UA V) Coverage Path Planning (CPP) is critical for applications such as precision agriculture and search and rescue. While traditional methods rely on discrete grid-based representations, real-world UA V operations require power-efficient continuous motion planning. We formulate the UA V CPP problem in a continuous environment, minimizing power consumption while ensuring complete coverage. We train a reinforcement learning agent using an action-mapping-based Soft Actor-Critic (AM-SAC) algorithm employing a self-adaptive curriculum. Experiments on both procedurally generated and hand-crafted scenarios demonstrate the effectiveness of our method in learning energy-efficient coverage strategies. Unmanned Aerial V ehicle (UA V) Coverage Path Planning (CPP) is a challenging problem with numerous real-world applications.