Unmanned Aerial Vehicles (UAVs) have become increasingly prominence in recent years, finding applications in surveillance, package delivery, among many others. Despite considerable efforts in developing algorithms that enable UAVs to navigate through complex unknown environments autonomously, they often require expensive hardware and sensors, such as RGB-D cameras and 3D-LiDAR, leading to a persistent trade-off between performance and cost. To this end, we propose RELAX, a novel end-to-end autonomous framework that is exceptionally cost-efficient, requiring only a single 2D-LiDAR to enable UAVs operating in unknown environments. Specifically, RELAX comprises three components: a pre-processing map constructor; an offline mission planner; and a reinforcement learning (RL)-based online re-planner. Experiments demonstrate that RELAX offers more robust dynamic navigation compared to existing algorithms, while only costing a fraction of the others. The code will be made public upon acceptance.

翻译：近年来，无人机在监控、包裹递送等领域的应用日益显著。尽管开发使无人机能够在复杂未知环境中自主导航的算法已取得大量进展，但这些方法通常需要昂贵的硬件和传感器（如RGB-D相机和三维激光雷达），导致性能与成本之间持续存在权衡。为此，我们提出RELAX——一种新颖的端到端自主框架，其成本效益极为突出，仅需单个二维激光雷达即可使无人机在未知环境中运行。具体而言，RELAX包含三个组件：预处理地图构建器、离线任务规划器，以及基于强化学习的在线重规划器。实验表明，相比现有算法，RELAX在动态导航中更具鲁棒性，且成本仅为其他方法的零头。相关代码将在论文接收后公开。