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

• URL: http://arxiv.org/abs/2505.08382v1
• Date: Tue, 13 May 2025 09:29:16 GMT
• Title: Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning
• Authors: Mirco Theile, Andres R. Zapata Rodriguez, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli,
• Abstract summary: Unmanned Aerial Vehicle (UAV) Coverage Path Planning ( CPP) is critical for applications such as precision agriculture and search and rescue.<n>We formulate the UAV CPP problem in a continuous environment, minimizing power consumption while ensuring complete coverage.<n>Our approach models the environment with variable-size axis-aligned rectangles and UAV motion with curvature-constrained B'ezier curves.
• Score: 4.851013539976943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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