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.

通过利用基于地图的观测、动作掩码和折扣因子调度的近端策略优化（PPO）深度强化学习（DRL）方法，尤其考虑充电航程的整体覆盖策略中复杂长期决策的问题，本研究提出了一种解决无人机有限电池的电量限制覆盖路径规划（CPP）问题的新方法，提供了处理由充电能力引起的紧急状态循环的位置历史，超越了基线启发式算法，并对长期问题的 DRL 算法设计提供了有价值的见解，并为 CPP 问题提供了一个可公开使用的软件框架。