AI RESEARCH
Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments
arXiv CS.AI
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ArXi:2605.19202v1 Announce Type: cross This paper addresses the problem of using a deep Reinforcement Learning (RL)-based low-level Quadrotor controller within an autonomous Quadrotor navigation stack for aerial inspection missions in under-canopy forest environments. Specifically, the article presents an end-to-end (mapping states to RPMs) Quadrotor control policy that achieves inspection view-pose tracking (simultaneous position and yaw reference tracking), which is crucial for various target inspection behaviors and point-to-point navigation in forests.