  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.
