Unmanned Aerial Vehicles (UAVs) have gained significant prominence in recent years for areas including surveillance, search, rescue, and package delivery. One key aspect in UAV operations shared across all these tasks is the autonomous path planning, which enables UAV to navigate through complex, unknown, and dynamic environments while avoiding obstacles without human control. Despite countless efforts having been devoted to this subject, new challenges are constantly arisen due to the persistent trade-off between performance and cost. And new studies are more urgently needed to develop autonomous system for UAVs with parsimonious sensor setup, which is a major need for wider adoptions. To this end, we propose an end-to-end autonomous framework to enable UAVs with only one single 2D-LiDAR sensor to operate in unknown dynamic environments. More specifically, we break our approach into three stages: a pre-processing Map Constructor; an offline Mission Planner; and an online reinforcement learning (RL)-based Dynamic Obstacle Handler. Experiments show that our approach provides robust and reliable dynamic path planning and obstacle avoidance with only 1/10 of the cost in sensor configuration. The code will be made public upon acceptance.

翻译：近年来，无人机在监视、搜索救援和包裹配送等领域受到广泛关注。所有此类任务中无人机的核心操作是自主路径规划——在无需人工干预的情况下，使无人机能够在复杂、未知的动态环境中避障导航。尽管已有大量相关研究，但性能与成本之间的持续权衡不断催生新的挑战。当前更迫切需要开发适用于低成本传感器配置的无人机自主系统，以满足大规模应用需求。为此，我们提出端到端自主框架，使仅搭载单个二维激光雷达传感器的无人机能在未知动态环境中运行。具体而言，该方法分为三个阶段：预处理地图构建模块、离线任务规划器以及基于在线强化学习的动态障碍处理模块。实验表明，本方法仅以1/10的传感器配置成本即可实现稳健可靠的动态路径规划与避障。相关代码将在论文接收后开源。