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

RELAX: Reinforcement Learning Enabled 2D-LiDAR Autonomous System for Parsimonious UAVs