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arXiv · 2505.08382

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

Mirco Theile·Andres R. Zapata Rodriguez·Marco Caccamo·Alberto L. Sangiovanni-Vincentelli
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Abstract

Unmanned Aerial Vehicle (UAV) 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 UAV operations require power-efficient continuous motion planning. We formulate the UAV CPP problem in a continuous environment, minimizing power consumption while ensuring complete coverage. Our approach models the environment with variable-size axis-aligned rectangles and UAV motion with curvature-constrained B\'ezier curves. 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.

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Mirco Theile, Andres R. Zapata Rodriguez, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli. 2025-05-13. Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning. https://arxiv.org/abs/2505.08382

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