Reinforcement Learning for Gliding Projectile Guidance and Control

Fuente: arXiv
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Hauptverfasser: Cahn, Joel, Thomas, Antonin, Pastor, Philippe
Format: Preprint
Veröffentlicht: 2025
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author Cahn, Joel
Thomas, Antonin
Pastor, Philippe
author_facet Cahn, Joel
Thomas, Antonin
Pastor, Philippe
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2512_01066
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Reinforcement Learning for Gliding Projectile Guidance and Control
Cahn, Joel
Thomas, Antonin
Pastor, Philippe
Robotics
Systems and Control
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.
title Reinforcement Learning for Gliding Projectile Guidance and Control
topic Robotics
Systems and Control
url https://arxiv.org/abs/2512.01066