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

Date

2023-09-01

Director

Publisher

Elsevier
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

  • AEI//TED2021-131716B-C21/
  • AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PLEC2021-007997/ES/ recolecta
  • Gobierno de Navarra//0011-1411-2021-000021/
  • Gobierno de Navarra//0011-1365-2020-000078/
  • Gobierno de Navarra//0011-1411-2021-000025/
Impacto

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 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.

Licencia

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