RELAX：面向低成本 UAV 的强化学习赋能型 2D-LiDAR 自主系统
RELAX: Reinforcement Learning Enabled 2D-LiDAR Autonomous System for Parsimonious UAVs
AI总结：
本文提出 RELAX 端到端框架，仅用单个 2D-LiDAR，并结合地图构建、离线规划与 RL 在线重规划，为低成本 UAV 在未知环境中提供更鲁棒的动态导航。
摘要：
近年来，无人机（UAVs）日益受到重视，并应用于监视、包裹递送等众多领域。尽管研究人员已投入大量努力开发使 UAV 能够在复杂未知环境中自主导航的算法，但这些算法通常需要昂贵的硬件和传感器，例如 RGB-D 相机和 3D-LiDAR，从而导致性能与成本之间长期存在权衡。为此，我们提出 RELAX，这是一种极具成本效益的新型端到端自主框架，仅需单个 2D-LiDAR 即可使 UAV 在未知环境中运行。具体而言，RELAX 由三个组件构成：预处理地图构建器、离线任务规划器，以及基于强化学习（RL）的在线重规划器。实验表明，与现有算法相比，RELAX 提供了更鲁棒的动态导航能力，而成本仅为其他方案的一小部分。代码将在论文被接收后公开。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
