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- W4386554612 abstract "Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest. This work addresses the power-constrained CPP problem with recharge for battery-limited unmanned aerial vehicles (UAVs). In this problem, a notable challenge emerges from integrating recharge journeys into the overall coverage strategy, highlighting the intricate task of making strategic, long-term decisions. We propose a novel proximal policy optimization (PPO)-based deep reinforcement learning (DRL) approach with map-based observations, utilizing action masking and discount factor scheduling to optimize coverage trajectories over the entire mission horizon. We further provide the agent with a position history to handle emergent state loops caused by the recharge capability. Our approach outperforms a baseline heuristic, generalizes to different target zones and maps, with limited generalization to unseen maps. We offer valuable insights into DRL algorithm design for long-horizon problems and provide a publicly available software framework for the CPP problem." @default.
- W4386554612 created "2023-09-09" @default.
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- W4386554612 date "2023-09-06" @default.
- W4386554612 modified "2023-09-29" @default.
- W4386554612 title "Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning" @default.
- W4386554612 doi "https://doi.org/10.48550/arxiv.2309.03157" @default.
- W4386554612 hasPublicationYear "2023" @default.
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