Publication:
Quadcopter neural controller for take-off and landing in windy environments

Consultable a partir de

2025-09-01

Date

2023

Director

Publisher

Elsevier
Acceso embargado / Sarbidea bahitua dago
Artículo / Artikulua
Versión aceptada / Onetsi den bertsioa

Project identifier

AEI//TED2021-131716B-C21
AEI//PLEC2021-007997
Gobierno de Navarra//0011-1411-2021-000021
Gobierno de Navarra//0011-1365-2020-000078
Gobierno de Navarra//0011-1411-2021-000025

Abstract

This paper proposes the design of a quadcopter neural controller based on Reinforcement Learning (RL) for controlling the complete maneuvers of landing and take-off, even in variable windy conditions. To facilitate RL training, a wind model is designed, and two RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), are adapted and compared. The first phases of the learning process consider extended exploration states as a warm-up, and a novel neural network controller architecture is proposed with the addition of an adaptation layer. The neural network’s output is defined as the forces and momentum desired for the UAV, and the adaptation layer transforms forces and momentum into motor velocities. By decoupling attitude from motor velocities, the adaptation layer enhances a more straightforward interpretation of the neural network output and helps refine the rewards. The successful neural controller training has been tested up to 36 km/h wind speed.

Description

Keywords

Quadcopter, Take-off, Landing, Deep reinforcement learning, Wind, PPO, DDPG

Department

Estadística, Informática y Matemáticas / Estatistika, Informatika eta Matematika / Institute of Smart Cities - ISC

Faculty/School

Degree

Doctorate program

item.page.cita

Olaz, X., Alaez, D., Prieto, M., Villadangos, J., Astrain, J. J. (2023) Quadcopter neural controller for take-off and landing in windy environments. Expert Systems with Applications, 225, 1-15. https://doi.org/10.1016/j.eswa.2023.120146.

item.page.rights

© 2023 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0

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