用强化学习让固定翼无人机高效覆盖大片区域，省电又精准。
Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning
- 用可变大小矩形建模环境，贝塞尔曲线规划平滑飞行路径。
- 在生成和手绘场景中均实现低能耗全覆盖，效果优于传统方法。
- 适合需要长续航、大范围作业的无人机应用，如农业巡检。
无人飞行器（UAV）覆盖路径规划（CPP）在精准农业和搜救等任务中至关重要。传统方法依赖离散网格表示，但真实飞行需考虑连续运动与节能需求。本文将UAV CPP问题建模为连续空间中的优化问题，以最小化能耗并保证完全覆盖为目标。环境采用可变尺寸轴对齐矩形表示，无人机运动则使用曲率受限的贝塞尔曲线建模。通过基于动作映射的软演员-评论家（AM-SAC）算法，并结合自适应课程训练策略，训练强化学习智能体。在程序生成和人工设计的多种场景中验证，该方法能有效学习出节能高效的覆盖策略，显著提升路径规划性能。
原文摘要 · Abstract (English)
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 Bézier 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。