English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/216386
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:

Title

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform

AuthorsRodriguez-Ramos, Alejandro; Sampedro, Carlos; Bavle, Hriday; Puente, Paloma de la; Campoy, Pascual
KeywordsDeep reinforcement learning
UAV
Autonomous landing
Continuous control
Issue Date3-Jul-2018
PublisherKluwer Academic Publishers
CitationJournal of Intelligent and Robotic Systems 93: 351–366 (2019)
AbstractThe use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by the rapid innovation in all the technologies involved. In particular, deep learning techniques for motion control have recently taken a major qualitative step, since the successful application of Deep Q-Learning to the continuous action domain in Atari-like games. Based on these ideas, Deep Deterministic Policy Gradients (DDPG) algorithm was able to provide outstanding results with continuous state and action domains, which are a requirement in most of the robotics-related tasks. In this context, the research community is lacking the integration of realistic simulation systems with the reinforcement learning paradigm, enabling the application of deep reinforcement learning algorithms to the robotics field. In this paper, a versatile Gazebo-based reinforcement learning framework has been designed and validated with a continuous UAV landing task. The UAV landing maneuver on a moving platform has been solved by means of the novel DDPG algorithm, which has been integrated in our reinforcement learning framework. Several experiments have been performed in a wide variety of conditions for both simulated and real flights, demonstrating the generality of the approach. As an indirect result, a powerful work flow for robotics has been validated, where robots can learn in simulation and perform properly in real operation environments. To the best of the authors knowledge, this is the first work that addresses the continuous UAV landing maneuver on a moving platform by means of a state-of-the-art deep reinforcement learning algorithm, trained in simulation and tested in real flights.
Publisher version (URL)https://doi.org/10.1007/s10846-018-0891-8
URIhttp://hdl.handle.net/10261/216386
DOIhttp://dx.doi.org/10.1007/s10846-018-0891-8
ISSN0921-0296
E-ISSN1573-0409
Appears in Collections:(CAR) Artículos
Files in This Item:
File Description SizeFormat 
Acceso_Restringido.pdfArtículo restringido15,35 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.