Reinforcement Learning for Gliding Projectile Guidance and Control

TL;DR

This paper explores using reinforcement learning to develop a control law for optical-guided gliders, aiming to enhance autonomous navigation and target tracking in dynamic environments, extending prior drone applications to fixed-wing aircraft.

Contribution

It demonstrates the applicability of reinforcement learning for fixed-wing aircraft guidance and control, expanding its use beyond quad-copter drones.

Findings

Reinforcement learning can be effectively applied to fixed-wing aircraft.

The control law improves navigation flexibility and precision.

Potential for autonomous target tracking in dynamic environments.

Abstract

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.