Dr. Victor Rodriguez-Fernandez

@inproceedings{santamaria2023exploring,

title = {Exploring Multiple Classification Systems for Online Time Series Anomaly Detection},

author = {Inmaculada Santamaria-Valenzuela and Victor Rodriguez-Fernandez and David Camacho},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 International Conference on Network, Multimedia and Information Technology (NMITCON)},

pages = {1–6},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2022_starcon2,

title = {A deep learning-based framework for operational all-vs-all conjunction screening},

author = {Emma Stevenson and Víctor Rodríguez-Fernández and Christophe Taillan and Hodei Urrutxua and David Camacho},

year  = {2022},

date = {2022-11-07},

booktitle = {2nd International Stardust Conference (STARCON-2)},

address = {ESTEC, the Netherlands},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2022_amos,

title = {Towards graph-based machine learning for conjunction assessment},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua},

year  = {2022},

date = {2022-09-19},

urldate = {2022-09-19},

booktitle = {2022 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS)},

address = {Maui, Hawaii, USA},

abstract = {In the face of increasing space traffic, the deployment of large constellations, and a growing debris field, identifying potentially catastrophic collisions is an increasingly daunting and computationally challenging task. In this work, we present a novel graph-based machine learning approach for detecting conjunctions between catalogued space objects to aid in this task. Modelling conjunction events as edges between pairs of object nodes, we introduce a graphical representation of the all-vs-all scenario (so-called as it considers conjunction events between all catalogued objects, both active and debris) that is able to profit from recent advancements in Graph Neural Networks, and make a step towards efficient, machine learning based conjunction assessment. For this, we develop a methodology to predict the existence of upcoming conjunction links over a given screening period, which we frame as a graph-to-graph link prediction task, and present some initial findings that demonstrate the learning potential of the proposed approach.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{9870215,

title = {Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning},

author = {Emma Stevenson and Riansares Martinez and Victor Rodriguez-Fernandez and David Camacho},

doi = {10.1109/CEC55065.2022.9870215},

year  = {2022},

date = {2022-07-18},

urldate = {2022-07-18},

booktitle = {2022 IEEE Congress on Evolutionary Computation (CEC)},

pages = {1-8},

address = {Padua, Italy},

abstract = {One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2022_kepassa,

title = {Deep learning for all-vs-all conjunction detection},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and David Camacho},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

booktitle = {5th Workshop on Key Topics in Orbit Propagation Applied to Space Situational Awareness (KePASSA)},

address = {Logroño, Spain},

abstract = {This paper explores the use of different deep learning techniques for detecting conjunction events in an efficient and accurate way for improved space situational awareness. Framing the problem as a machine learning classification task, we present the performance of different data representations and model architectures on a realistic all-vs-all dataset generated using the CNES BAS3E space surveillance simulation framework, and compare the approaches to operationally used classical filters in screening performance and computational efficiency. Finally, we also investigate a novel methodology for improving the performance and generalisation ability of the models using a pre-trained orbit model, ORBERT, based on self-supervised learning techniques.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2021_iaass,

title = {Self-supervised machine learning based approach to orbit modelling applied to space traffic management},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and Vincent Morand and David Camacho},

year  = {2021},

date = {2021-10-01},

booktitle = {11th International Association for the Advancement of Space Safety Conference (IAASS)},

address = {(Virtual), Osaka, Japan},

abstract = {This paper presents a novel methodology for improving the performance of machine learning based space traffic management tasks through the use of a pre-trained orbit model. Taking inspiration from BERT-like self-supervised language models in the field of natural language processing, we introduce ORBERT, and demonstrate the ability of such a model to leverage large quantities of readily available orbit data to learn meaningful representations that can be used to aid in downstream tasks. As a proof of concept of this approach we consider the task of all vs. all conjunction screening, phrased here as a machine learning time series classification task. We show that leveraging unlabelled orbit data leads to improved performance, and that the proposed approach can be particularly beneficial for tasks where the availability of labelled data is limited.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2021_ecsd,

title = {Artificial Intelligence for All vs. All Conjunction Screening},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and Vincent Morand and David Camacho},

url = {http://oa.upm.es/67167/},

year  = {2021},

date = {2021-04-01},

booktitle = {8th European Conference on Space Debris (ECSD)},

address = {(Virtual), Darmstadt, Germany},

abstract = {This paper presents a proof of concept for the application of artificial intelligence (AI) to the problem of efficient, catalogue-wide conjunction screening. Framed as a machine learning classification task, an ensemble of tabular models were trained and deployed on a realistic all vs. all dataset, generated using the CNES BAS3E space surveillance simulation framework, and consisting of 170 million object pairs over a 7-day screening period. The approach was found to outperform classical filters such as the apogee-perigee filter and the Minimum Orbital Intersection Distance (MOID) in terms of screening capability, with the number of missed detections of the approach controlled by the operator. It was also found to be computationally efficient, thus demonstrating the capability of AI algorithms to cope and aid with the scales required for current and future operational all vs. all scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2020_iac,

title = {A deep learning approach to space weather proxy forecasting for orbital prediction},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Edmondo Minisci and David Camacho},

url = {http://oa.upm.es/64345/},

year  = {2020},

date = {2020-10-01},

booktitle = {71st International Astronautical Congress (IAC)},

address = {The CyberSpace Edition},

abstract = {The effect of atmospheric drag on spacecraft dynamics is considered one of the predominant sources of uncertainty in Low Earth Orbit. These effects are characterised in part by the atmospheric density, a quantity highly correlated to space weather. Current atmosphere models typically account for this through proxy indices such as the F10.7, but with variations in solar radio flux forecasts leading to significant orbit differences over just a few days, prediction of these quantities is a limiting factor in the accurate estimation of future drag conditions, and consequently orbital prediction. This has fundamental implications both in the short term, in the day-to-day management of operational spacecraft, and in the mid-to-long term, in determining satellite orbital lifetime. In this work, a novel deep residual architecture for univariate time series forecasting, N-BEATS, is employed for the prediction of the F10.7 solar proxy on the days-ahead timescales relevant to space operations. This untailored, pure deep learning approach has recently achieved state-of-the-art performance in time series forecasting competitions, outperforming well-established statistical, as well as statistical hybrid models, across a range of domains. The approach was found to be effective in single point forecasting up to 27-days ahead, and was additionally extended to produce forecast uncertainty estimates using deep ensembles. These forecasts were then compared to a persistence baseline and two operationally available forecasts: one statistical (provided by BGS, ESA), and one multi-flux neural network (by CLS, CNES). It was found that the N-BEATS model systematically outperformed the baseline and statistical approaches, and achieved an improved or similar performance to the multi-flux neural network approach despite only learning from a single variable},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez-fernandez_extensions_2019,

title = {Extensions of Hidden Markov Models for supporting instructors in the analysis of training operations in an Unmanned Aircraft System},

author = {Victor Rodriguez-Fernandez},

url = {https://doi.org/10.1109/SA47457.2019.8938088},

doi = {10.1109/SA47457.2019.8938088},

isbn = {978-1-72813-345-4},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {2019 First International Conference on Societal Automation (SA)},

pages = {1--7},

publisher = {IEEE},

address = {Krakow, Poland},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez-fernandez_inteligencia_2018,

title = {Inteligencia Artificial aplicada al análisis de entrenamiento en operaciones con UAVs},

author = {Victor Rodriguez-Fernández and Antonio González-Pardo and David Camacho},

isbn = {978-84-9091-357-4},

year  = {2018},

date = {2018-09-01},

urldate = {2018-09-01},

booktitle = {Actas V Congreso Nacional de i+d en Defensa y Seguridad},

pages = {1491--1498},

publisher = {Ministerio de Defensa. Secretaría General Técnica.},

address = {Toledo, España},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vasile_artificial_2018,

title = {Artificial intelligence in support to space traffic management},

author = {Massimiliano Vasile and Victor Rodriguez-Fernandez and Romain Serra and David Camacho and Annalisa Riccardi},

isbn = {978-1-5108-5537-3},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {Proceedings of the International Astronautical Congress, IAC},

volume = {1},

pages = {3843--3856},

publisher = {International Astronautical Federation},

address = {Adelaide, Australia},

abstract = {This paper presents an Artificial Intelligence-based decision support system to assist ground operators to plan and implement collision avoidance manoeuvres. When a new conjunction is expected, the system provides the operator with an optimal manoeuvre and an analysis of the possible outcomes. Machine learning techniques are combined with uncertainty quantification and orbital mechanics calculations to support an optimal and reliable management of space traffic. A dataset of collision avoidance manoeuvres has been created by simulating a range of scenarios in which optimal manoeuvres (in the sense of optimal control) are applied to reduce the collision probability between pairs of objects. The consequences of the execution of a manoeuvre are evaluated to assess its benefits against its cost. Consequences are quantified in terms of the need for additional manoeuvres to avoid subsequent collisions. By using this dataset, we train predictive models that forecast the risk of avoiding new collisions, and use them to recommend alternative manoeuvres that may be globally better for the space environment.},

note = {Publisher: International Astronautical Federation (IAF)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/cec/Ramirez-Atencia17,

title = {New Artificial Intelligence approaches for future UAV Ground Control Stations},

author = {Cristian Ramírez-Atencia and Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho},

url = {https://doi.org/10.1109/CEC.2017.7969645},

doi = {10.1109/CEC.2017.7969645},

isbn = {978-1-5090-4601-0},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017},

pages = {2775--2782},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2016method,

title = {A Method for Building Predictive HSMMs in Interactive Environments},

author = {Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho},

doi = {10.1109/CEC.2016.7744187},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {2016 IEEE Congress on Evolutionary Computation (CEC)},

pages = {3146--3153},

publisher = {IEEE},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{LosadaSuarez2016,

title = {Desarrollo de una plataforma basada en Unity3D para la aplicación de IA en videojuegos},

author = {Óscar Manuel Losada Suárez and Víctor Rodríguez-Fernández and Cristian Ramírez-Atencia and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2017/03/Desarrollo-de-una-plataforma-basada-en-Unity3D-para-la-aplicación-de-IA-en-videojuegos.pdf},

issn = {16130073},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {3rd Congreso de la Sociedad Española para las Ciencias del Videojuego (CoSECiVi 2016)},

volume = {1682},

pages = {135--146},

publisher = {CEUR Workshop},

address = {Barcelona, Spain},

abstract = {La utilización intensiva de diferentes técnicas relacionadas con la Inteligencia Artificial (IA) en el área de los videojuegos ha demostrado ser una necesidad para el campo. El uso de estas técnicas permite dotar de una mayor flexibilidad y adaptabilidad a los juegos que es muy apreciada por los jugadores. Temas como la generación procedimental de contenido, la creación de agentes que puedan jugar a un videojuego de forma competente, o de agentes cuya conducta sea indistinguible de la de un jugador humano atraen a una cantidad creciente de investigadores. El objetivo de este trabajo es la presentación de una plataforma basada en el motor Unity3D que permita de manera simple la integración y prueba de algoritmos de IA. La plataforma ofrecerá como nuevas características, adicionales a las ya disponibles en la actualidad, la utilización de un entorno 3D, el desarrollo de un juego innovador (basado en múltiples agentes), y la exploración de aspectos de juego como el análisis del terreno, la cooperación entre agentes independientes y heterogéneos, la comunicación de información entre los mismos y la formación de jerarquías.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2016Finding,

title = {Finding behavioral patterns of UAV operators using Multichannel Hidden Markov Models},

author = {Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho},

url = {http://dx.doi.org/10.1109/SSCI.2016.7850101},

doi = {10.1109/SSCI.2016.7850101},

isbn = {978-1-5090-4240-1},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, December 6-9, 2016},

pages = {1--8},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{@inproceedings,

title = {Diseño de un Simulador de Bajo Coste para Vehículos Aéreos no Tripulados},

author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/05/RodriguezMenendezCamacho.pdf},

isbn = {978-84-697-2150-6},

year  = {2015},

date = {2015-02-04},

booktitle = {Actas del X Congreso español sobre metaheurísticas, algoritmos evolutivos y bioinspirados: MAEB},

pages = {447-454},

address = {Mérida, Cáceres},

abstract = {La utilización de vehículos aéreos no tripulados, o Unmanned Aircraft Vehicles (UAVs), se ha popularizado enormemente en los últimos tiempos. Estos nuevos sistemas, introducidos en su mayoría por Google, han ganado una importante relevancia, dado que podrían suponer una mejora destacable en la seguridad ciudadana y tienen muchísimas aplicaciones a niveles profesionales dentro de campos muy variados, como la agricultura o el envío postal de paquetes. Sin embargo, el coste a la hora de realizar pruebas en entornos reales con estos dispositivos es muy elevado, por eso se crean simuladores capaces de poner a prueba distintas estrategias a la hora de planificar misiones en estos dispositivos. Este trabajo presenta un simulador orientado a este tipo de sistemas. Este simulador pretende ser una aproximación de bajo coste y fácilmente distribuible, que ayude a simular misiones llevadas a cabo por múltiples drones. Para evaluar su eficacia, se le ha sometido a pruebas de estrés con miles de usuarios, donde ha demostrado tener una respuesta potencialmente competitiva.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2015multi,

title = {A multi-UAV Mission Planning videogame-based framework for player analysis},

author = {Victor Rodriguez-Fernandez and Cristian Ramirez-Atencia and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/07257064.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Evolutionary Computation (CEC), 2015 IEEE Congress on},

pages = {1490--1497},

organization = {IEEE},

abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2015design,

title = {Design and development of a lightweight multi-UAV simulator},

author = {Victor Rodriguez-Fernandez and Hector D Menendez and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/cybconf2015.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Cybernetics (CYBCONF), 2015 IEEE 2nd International Conference on},

pages = {255--260},

organization = {IEEE},

abstract = {UAVs have become enormously popular over the last few years. These new systems could make a remarkable improvement in public safety and have many applications in a variety of fields such as agriculture or postage of packages. In order to test UAV capacities and train UAV mission operators, several simulators are used, and the use of them usually entails high costs. 

This work presents a low-cost and easily distributable simulator, focused on simulating missions carried out by multiple UAVs and extracting data from them. To evaluate its effectiveness, it has been subjected to stress testing with thousands of virtual users, proving to have a potentially competitive response.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/iccci/Rodriguez-Fernandez15,

title = {User Profile Analysis for UAV Operators in a Simulation Environment},

author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/iccci2015.pdfhttp://dx.doi.org/10.1007/978-3-319-24069-5_32},

year  = {2015},

date = {2015-01-01},

booktitle = {Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015. Proceedings, Part I},

pages = {338--347},

crossref = {DBLP:conf/iccci/2015-1},

abstract = {Unmanned Aerial Vehicles have been a growing field of study over the last few years. The use of unmanned systems require a strong human supervision of one or many human operators, responsible for monitoring the mission status and avoiding possible incidents that might alter the execution and success of the operation. The accelerated evolution of these systems is generating a high demand of qualified operators, which requires to redesign the training process to deal with it. This work aims to present an evaluation methodology for inexperienced users. A multi-UAV simulation environment is used to carry out an experiment focused on the extraction of performance profiles, which can be used to evaluate the behavior and learning process of the users. A set of performance metrics is designed to define the profile of a user, and those profiles are discriminated using clustering algorithms. The results are analyzed to extract behavioral patterns that distinguish the users in the experiment, allowing the identification and selection of potential expert operators.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2015summary,

title = {A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game},

author = {Victor Rodriguez-Fernández and Cristian Ramirez-Atencia and David Camacho},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

booktitle = {2nd Congreso de la Sociedad Espa~nola para las Ciencias del Videojuego (CoSeCiVi 2015)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}