Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments
For autonomous drone inspection in cluttered forest environments, this work demonstrates the feasibility of using an end-to-end RL controller for low-level control, but the approach is incremental as it relies on known maps and existing planners.
This paper presents a deep RL-based quadrotor controller for aerial inspection in under-canopy forests, achieving view-pose tracking and point-to-point navigation. The system, combining a TSP planner and RRT* planner with the RL policy, successfully performs five inspection scenarios.
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. To ensure safe and reliable deployment of the end-to-end RL controller in long-range missions, this article utilizes a higher navigation guidance layer comprising of a Traveling Salesman Problem planner (TSP) and a Rapidly-exploring Random Tree Star (RRT*) planner. Over a known map of a forest and a set of user-specified inspection regions, the TSP planner finds the optimal visitation sequence. Between two target regions, collision-free paths that respect the tracking limitations of the lower end-to-end RL policy are generated by an RRT* planner. Through five target inspection scenarios, this article demonstrates that an RL-based motor-level stabilizing controller, supported by a navigation guidance layer, can be used effectively as the low-level inspection execution module for under-canopy forest inspection missions.