Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2309.03157v2
• Date: Thu, 7 Sep 2023 18:18:08 GMT
• Title: Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning
• Authors: Mirco Theile, Harald Bayerlein, Marco Caccamo, and Alberto L. Sangiovanni-Vincentelli
• Abstract summary: 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) We propose a novel proximal policy optimization (PPO)-based deep reinforcement learning (DRL) approach with map-based observations.
• Score: 5.475990395948956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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