mlsanityData to 14 Oct 2026

Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments

arXiv 2605.19202 ↗First posted 19 May 2026cs.RO cs.AI math.OCcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 17 references that are in the map. First posted on arXiv 19 May 2026.

Builds on

10 of 17 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Semantically-driven Deep Reinforcement Learning for Inspection Path Planning20 May
  2. 2024Multi-Task Reinforcement Learning for Quadrotors17 Dec
  3. 2024MAVRL: Learn to Fly in Cluttered Environments with Varying Speed13 Feb

Other references · 7

  1. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  2. 2017Control of a Quadrotor with Reinforcement Learningcited by 92
  3. 2021Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Controlcited by 77
  4. 2023Learning to Fly in Secondscited by 31
  5. 2022Learning a Single Near-hover Position Controller for Vastly Different Quadcopterscited by 21
  6. 2024Learning Speed Adaptation for Flight in Cluttercited by 14
  7. 2023Multi-Agent Reinforcement Learning for the Low-Level Control of a Quadrotor UAVcited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning for Autonomous Point-to-Point UAV Navigation
  2. 2024Tangled Program Graphs as an alternative to DRL-based control algorithms for UAVs
  3. 2025Flying on Point Clouds with Reinforcement Learning
  4. 2025Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking
  5. 2026High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning
  6. 2025Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs
  7. 2026Learning-Based Dynamic Obstacle Avoidance for a UAV Using Only Three Range Sensors
  8. 2024RESC: A Reinforcement Learning Based Search-to-Control Framework for Quadrotor Local Planning in Dense Environments