mlsanityData to 14 Oct 2026

Reinforcement Learning for Gliding Projectile Guidance and Control

arXiv 2512.01066 ↗First posted 30 Nov 2025cs.RO cs.SY eess.SYcited by 0

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

Abstract (arXiv)

This paper presents the development of a control law, which is intended to be implemented on an optical guided glider. This guiding law follows an innovative approach, the reinforcement learning. This control law is used to make navigation more flexible and autonomous in a dynamic environment. The final objective is to track a target detected with the camera and then guide the glider to this point with high precision. Already applied on quad-copter drones, we wish by this study to demonstrate the applicability of reinforcement learning for fixed-wing aircraft on all of its axis.

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 3 of its 13 references that are in the map. First posted on arXiv 30 Nov 2025.

Builds on

3 of 13 references.

References · 3

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Autonomous Drone Racing with Deep Reinforcement Learningcited by 60
  3. 2021Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experimentscited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2019Flying through a narrow gap using neural network: an end-to-end planning and control approach
  2. 2025Reactive Aerobatic Flight via Reinforcement Learning
  3. 2025Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking
  4. 2026Precise Aggressive Aerial Maneuvers with Sensorimotor Policies
  5. 2024Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning
  6. 2026Reinforcement Learning for Active Perception in Autonomous Navigation
  7. 2025Reinforcement Learning for Autonomous Point-to-Point UAV Navigation
  8. 2024Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning