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2017 |
Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David A study on performance metrics and clustering methods for analyzing behavior in UAV operations Journal Article Journal of Intelligent and Fuzzy Systems, 32 (2), pp. 1307–1319, 2017. @article{DBLP:journals/jifs/Rodriguez-Fernandez17, title = {A study on performance metrics and clustering methods for analyzing behavior in UAV operations}, author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho}, url = {http://dx.doi.org/10.3233/JIFS-169129}, doi = {10.3233/JIFS-169129}, year = {2017}, date = {2017-01-01}, journal = {Journal of Intelligent and Fuzzy Systems}, volume = {32}, number = {2}, pages = {1307--1319}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David Analysing temporal performance profiles of UAV operators using time series clustering Journal Article Expert Systems with Applications, 70 , pp. 103–118, 2017, ISSN: 0957-4174. Links | BibTeX | Tags: Performance measures, Simulation-based Training, Time Series Clustering, UAV operators, UAVs @article{rodriguez20171Analysing, title = {Analysing temporal performance profiles of UAV operators using time series clustering}, author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho}, url = {http://www.sciencedirect.com/science/article/pii/S0957417416305851}, doi = {http://dx.doi.org/10.1016/j.eswa.2016.10.044}, issn = {0957-4174}, year = {2017}, date = {2017-01-01}, journal = {Expert Systems with Applications}, volume = {70}, pages = {103--118}, keywords = {Performance measures, Simulation-based Training, Time Series Clustering, UAV operators, UAVs}, pubstate = {published}, tppubtype = {article} } |
Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David Automatic Procedure Following Evaluation using Petri Net-based Workflows Journal Article IEEE Transactions on Industrial Informatics, In press , 2017. BibTeX | Tags: @article{Rodriguez-Fernandez2017b, title = {Automatic Procedure Following Evaluation using Petri Net-based Workflows}, author = {Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho}, year = {2017}, date = {2017-01-01}, journal = {IEEE Transactions on Industrial Informatics}, volume = {In press}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Ramirez-Atencia, Cristian; R-Moreno, Maria D; Camacho, David Handling swarm of UAVs based on evolutionary multi-objective optimization Journal Article Progress in Artificial Intelligence, In Press , pp. 1–12, 2017, ISSN: 2192-6352. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Mission Planning, Multi-Objective Genetic Algorithm, Unmanned Aerial Vehicles @article{Ramirez-Atencia2017, title = {Handling swarm of UAVs based on evolutionary multi-objective optimization}, author = {Cristian Ramirez-Atencia and Maria D R-Moreno and David Camacho}, url = {http://link.springer.com/10.1007/s13748-017-0123-7}, doi = {10.1007/s13748-017-0123-7}, issn = {2192-6352}, year = {2017}, date = {2017-01-01}, journal = {Progress in Artificial Intelligence}, volume = {In Press}, pages = {1--12}, publisher = {Springer Berlin Heidelberg}, abstract = {The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.}, keywords = {Constraint Satisfaction Problems, Mission Planning, Multi-Objective Genetic Algorithm, Unmanned Aerial Vehicles}, pubstate = {published}, tppubtype = {article} } The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime. |
Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David Modelling Behaviour in UAV Operations Using Higher Order Double Chain Markov Models Journal Article IEEE Computational Intelligence Magazine, 12 (4), pp. 28–37, 2017. @article{rodriguez2017modellingbb, title = {Modelling Behaviour in UAV Operations Using Higher Order Double Chain Markov Models}, author = {Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho}, url = {https://doi.org/10.1109/MCI.2017.2742738}, doi = {10.1109/MCI.2017.2742738}, year = {2017}, date = {2017-01-01}, journal = {IEEE Computational Intelligence Magazine}, volume = {12}, number = {4}, pages = {28--37}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Ramírez-Atencia, Cristian; Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David New Artificial Intelligence approaches for future UAV Ground Control Stations Inproceedings 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017, pp. 2775–2782, IEEE, 2017, ISBN: 978-1-5090-4601-0. @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}, 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} } |
Martín, Alejandro; Fuentes-Hurtado, Félix; Naranjo, Valery; Camacho, David Evolving deep neural networks architectures for Android malware classification Inproceedings Evolutionary Computation (CEC), 2017 IEEE Congress on, pp. 1659–1666, IEEE 2017. BibTeX | Tags: @inproceedings{martin2017evolving, title = {Evolving deep neural networks architectures for Android malware classification}, author = {Alejandro Martín and Félix Fuentes-Hurtado and Valery Naranjo and David Camacho}, year = {2017}, date = {2017-01-01}, booktitle = {Evolutionary Computation (CEC), 2017 IEEE Congress on}, pages = {1659--1666}, organization = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Martín, Alejandro; Lara-Cabrera, Raúl; Fuentes-Hurtado, Félix; Naranjo, Valery; Camacho, David EvoDeep: a new Evolutionary approach for automatic Deep Neural Networks parametrisation Journal Article Journal of Parallel and Distributed Computing, 2017. BibTeX | Tags: @article{martin2017evodeep, title = {EvoDeep: a new Evolutionary approach for automatic Deep Neural Networks parametrisation}, author = {Alejandro Martín and Raúl Lara-Cabrera and Félix Fuentes-Hurtado and Valery Naranjo and David Camacho}, year = {2017}, date = {2017-01-01}, journal = {Journal of Parallel and Distributed Computing}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Ramirez-Atencia, Cristian; Mostaghim, Sanaz; Camacho, David A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems Inproceedings Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1216–1223, ACM, Berlin, Germany, 2017, ISBN: 978-1-4503-4920-8. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, evolutionary multi-objective optimization, knee point, Mission Planning, Multi-objective Optimization, UAVs @inproceedings{Ramirez-Atencia2017b, title = {A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems}, author = {Cristian Ramirez-Atencia and Sanaz Mostaghim and David Camacho}, url = {http://doi.acm.org/10.1145/3071178.3071319}, doi = {10.1145/3071178.3071319}, isbn = {978-1-4503-4920-8}, year = {2017}, date = {2017-01-01}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}, pages = {1216--1223}, publisher = {ACM}, address = {Berlin, Germany}, series = {GECCO '17}, abstract = {The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.}, keywords = {Constraint Satisfaction Problems, evolutionary multi-objective optimization, knee point, Mission Planning, Multi-objective Optimization, UAVs}, pubstate = {published}, tppubtype = {inproceedings} } The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance. |
2016 |
Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David Automatic profile generation for UAV operators using a simulation-based training environment Journal Article Progress in Artificial Intelligence, 5 (1), pp. 37–46, 2016, ISSN: 2192-6352. @article{Rodriguez-Fernandez2016, title = {Automatic profile generation for UAV operators using a simulation-based training environment}, author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho}, url = {http://link.springer.com/10.1007/s13748-015-0072-y}, doi = {10.1007/s13748-015-0072-y}, issn = {2192-6352}, year = {2016}, date = {2016-02-01}, journal = {Progress in Artificial Intelligence}, volume = {5}, number = {1}, pages = {37--46}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David A Method for Building Predictive HSMMs in Interactive Environments Inproceedings 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3146–3153, IEEE IEEE, 2016. @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}, booktitle = {2016 IEEE Congress on Evolutionary Computation (CEC)}, pages = {3146--3153}, publisher = {IEEE}, organization = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Lara-Cabrera, Raúl; Gutierrez-Alcoba, Alejandro; Fernández-Leiva, Antonio J A spatially-structured PCG method for content diversity in a Physics-based simulation game Inproceedings European Conference on the Applications of Evolutionary Computation, pp. 653–668, Springer International Publishing 2016. BibTeX | Tags: @inproceedings{lara2016spatially, title = {A spatially-structured PCG method for content diversity in a Physics-based simulation game}, author = {Raúl Lara-Cabrera and Alejandro Gutierrez-Alcoba and Antonio J Fernández-Leiva}, year = {2016}, date = {2016-01-01}, booktitle = {European Conference on the Applications of Evolutionary Computation}, pages = {653--668}, organization = {Springer International Publishing}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems Inproceedings XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016), pp. 305–314, 2016. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Mission Planning, Muli-UAV, Multi-Objective Genetic Algorithm, Multi-objective Optimization, NSGA2, Unmanned Aerial Vehicles @inproceedings{Ramirez-Atencia2016a, title = {A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2017/03/A-Weighted-Penalty-Fitness-for-a-Hybrid-MOGA-CSP-to-solve-Mission-Planning-Problems.pdf}, year = {2016}, date = {2016-01-01}, booktitle = {XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016)}, pages = {305--314}, abstract = {Unmanned Aerial Vehicles (UAVs) are currently booming due to their high number of potential applications. In Mission Planning problems, several tasks must be performed by a team of UAVs, under the supervision of one or more Ground Control Stations (GCSs). In our approach, we have modelled the problem as a Constraint Satisfaction Problem (CSP), and solved it using a Multi-Objective Genetic Algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission such as the fuel consumption or the makespan. In addition, the fitness function takes a new consideration when solutions are not valid. It uses the number of constraints fulfilled for each solution as a weighted penalty function. In this way, the number of constraints fulfilled is maximized in the elitism phase of the MOGA. Results show that the approach outperforms the convergence with respect to previous results.}, keywords = {Constraint Satisfaction Problems, Mission Planning, Muli-UAV, Multi-Objective Genetic Algorithm, Multi-objective Optimization, NSGA2, Unmanned Aerial Vehicles}, pubstate = {published}, tppubtype = {inproceedings} } Unmanned Aerial Vehicles (UAVs) are currently booming due to their high number of potential applications. In Mission Planning problems, several tasks must be performed by a team of UAVs, under the supervision of one or more Ground Control Stations (GCSs). In our approach, we have modelled the problem as a Constraint Satisfaction Problem (CSP), and solved it using a Multi-Objective Genetic Algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission such as the fuel consumption or the makespan. In addition, the fitness function takes a new consideration when solutions are not valid. It uses the number of constraints fulfilled for each solution as a weighted penalty function. In this way, the number of constraints fulfilled is maximized in the elitism phase of the MOGA. Results show that the approach outperforms the convergence with respect to previous results. |
Suárez, Óscar Manuel Losada; Rodríguez-Fernández, Víctor; Ramírez-Atencia, Cristian; Camacho, David Desarrollo de una plataforma basada en Unity3D para la aplicación de IA en videojuegos Inproceedings Camacho, David; Martín, Marco Antonio Gómez; Calero, Pedro Antonio González (Ed.): 3rd Congreso de la Sociedad Española para las Ciencias del Videojuego (CoSECiVi 2016), pp. 135–146, CEUR Workshop, Barcelona, Spain, 2016, ISSN: 16130073. Abstract | Links | BibTeX | Tags: Agentes inteligentes, Inteligencia artificial, Plataforma software, Unity3D, Videojuegos @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}, editor = {David Camacho and Marco Antonio Gómez Martín and Pedro Antonio González Calero}, url = {http://aida.ii.uam.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}, 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 = {Agentes inteligentes, Inteligencia artificial, Plataforma software, Unity3D, Videojuegos}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David Finding behavioral patterns of UAV operators using Multichannel Hidden Markov Models Inproceedings 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, December 6-9, 2016, pp. 1–8, IEEE, 2016, ISBN: 978-1-5090-4240-1. @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}, 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} } |
Bello-Orgaz, Gema; Ramirez-Atencia, Cristian; Fradera-Gil, Jaime; Camacho, David GAMPP: Genetic Algorithm for UAV Mission Planning Problems Incollection Intelligent Distributed Computing IX, pp. 167–176, Springer International Publishing, 2016. Abstract | Links | BibTeX | Tags: Genetic Algorithms, Mission Planning, Unmanned Aircraft Systems @incollection{bello2016gampp, title = {GAMPP: Genetic Algorithm for UAV Mission Planning Problems}, author = {Gema Bello-Orgaz and Cristian Ramirez-Atencia and Jaime Fradera-Gil and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2015/11/IDC15_BelloOrgazEtAl.pdf}, year = {2016}, date = {2016-01-01}, booktitle = {Intelligent Distributed Computing IX}, pages = {167--176}, publisher = {Springer International Publishing}, abstract = {Due to the rapid development of the UAVs capabilities, these are being incorporated into many fields to perform increasingly complex tasks. Some of these tasks are becoming very important because they involve a high risk to the vehicle driver, such as detecting forest fires or rescue tasks, while using UAVs avoids risking human lives. Recent researches on artificial intelligence techniques applied to these systems provide a new degree of high-level autonomy of them. Mission planning for teams of UAVs can be defined as the planning process of locations to visit (waypoints) and the vehicle actions to do (loading/dropping a load, taking videos/pictures, acquiring information), typically over a time period. Currently, UAVs are controlled remotely by human operators from ground control stations, or use rudimentary systems. This paper presents a new Genetic Algorithm for solving Mission Planning Problems (GAMPP) using a cooperative team of UAVs. The fitness function has been designed combining several measures to look for optimal solutions minimizing the fuel consumption and the mission time (or makespan). The algorithm has been experimentally tested through several missions where its complexity is incrementally modified to measure the scalability of the problem. Experimental results show that the new algorithm is able to obtain good solutions improving the runtime of a previous approach based on CSPs.}, keywords = {Genetic Algorithms, Mission Planning, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {incollection} } Due to the rapid development of the UAVs capabilities, these are being incorporated into many fields to perform increasingly complex tasks. Some of these tasks are becoming very important because they involve a high risk to the vehicle driver, such as detecting forest fires or rescue tasks, while using UAVs avoids risking human lives. Recent researches on artificial intelligence techniques applied to these systems provide a new degree of high-level autonomy of them. Mission planning for teams of UAVs can be defined as the planning process of locations to visit (waypoints) and the vehicle actions to do (loading/dropping a load, taking videos/pictures, acquiring information), typically over a time period. Currently, UAVs are controlled remotely by human operators from ground control stations, or use rudimentary systems. This paper presents a new Genetic Algorithm for solving Mission Planning Problems (GAMPP) using a cooperative team of UAVs. The fitness function has been designed combining several measures to look for optimal solutions minimizing the fuel consumption and the mission time (or makespan). The algorithm has been experimentally tested through several missions where its complexity is incrementally modified to measure the scalability of the problem. Experimental results show that the new algorithm is able to obtain good solutions improving the runtime of a previous approach based on CSPs. |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning Inproceedings 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016, ISBN: 978-1-5090-4240-1, 978-1-5090-4241-8. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Metaheuristics, Mission Planning, Multi-objective Optimization, NSGA2, Replanning, Unmanned Aircraft Systems @inproceedings{Ramirez-Atencia2016b, title = {MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho}, doi = {10.1109/SSCI.2016.7850235}, isbn = {978-1-5090-4240-1, 978-1-5090-4241-8}, year = {2016}, date = {2016-01-01}, booktitle = {2016 IEEE Symposium Series on Computational Intelligence (SSCI)}, abstract = {From the last few years the interest and repercussion on Unmanned Aerial Vehicle (UAV) technologies have been extended from pure military applications to industrial and societal applications. One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of MultiUAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to realtime Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an offlline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered.}, keywords = {Constraint Satisfaction Problems, Metaheuristics, Mission Planning, Multi-objective Optimization, NSGA2, Replanning, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } From the last few years the interest and repercussion on Unmanned Aerial Vehicle (UAV) technologies have been extended from pure military applications to industrial and societal applications. One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of MultiUAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to realtime Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an offlline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered. |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms Journal Article Soft Computing, In Press , pp. 1–18, 2016, ISSN: 1432-7643; 1433-7479. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, NSGA2, Unmanned air vehicles @article{Ramirez-Atencia2016c, title = {Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2017/03/RamirezEtAl.pdf}, doi = {10.1007/s00500-016-2376-7}, issn = {1432-7643; 1433-7479}, year = {2016}, date = {2016-01-01}, journal = {Soft Computing}, volume = {In Press}, pages = {1--18}, publisher = {Springer Verlag}, abstract = {Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find.}, keywords = {Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, NSGA2, Unmanned air vehicles}, pubstate = {published}, tppubtype = {article} } Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find. |
Martín, Alejandro; Menéndez, Héctor D; Camacho, David MOCDroid: multi-objective evolutionary classifier for Android malware detection Journal Article Soft Computing, pp. 1–11, 2016. BibTeX | Tags: @article{martin2016mocdroid, title = {MOCDroid: multi-objective evolutionary classifier for Android malware detection}, author = {Alejandro Martín and Héctor D Menéndez and David Camacho}, year = {2016}, date = {2016-01-01}, journal = {Soft Computing}, pages = {1--11}, publisher = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Martín, Alejandro; Menéndez, Héctor D; Camacho, David Studying the Influence of Static API Calls for Hiding Malware Inproceedings Conference of the Spanish Association for Artificial Intelligence, pp. 363–372, Springer 2016. BibTeX | Tags: @inproceedings{martin2016studying, title = {Studying the Influence of Static API Calls for Hiding Malware}, author = {Alejandro Martín and Héctor D Menéndez and David Camacho}, year = {2016}, date = {2016-01-01}, booktitle = {Conference of the Spanish Association for Artificial Intelligence}, pages = {363--372}, organization = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Martín, Alejandro; Menéndez, Héctor D; Camacho, David String-based malware detection for android environments Inproceedings International Symposium on Intelligent and Distributed Computing, pp. 99–108, Springer International Publishing 2016. BibTeX | Tags: @inproceedings{martin2016string, title = {String-based malware detection for android environments}, author = {Alejandro Martín and Héctor D Menéndez and David Camacho}, year = {2016}, date = {2016-01-01}, booktitle = {International Symposium on Intelligent and Distributed Computing}, pages = {99--108}, organization = {Springer International Publishing}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Martín, Alejandro; Menéndez, Héctor D; Camacho, David Genetic boosting classification for malware detection Inproceedings Evolutionary Computation (CEC), 2016 IEEE Congress on, pp. 1030–1037, IEEE 2016. BibTeX | Tags: @inproceedings{martin2016genetic, title = {Genetic boosting classification for malware detection}, author = {Alejandro Martín and Héctor D Menéndez and David Camacho}, year = {2016}, date = {2016-01-01}, booktitle = {Evolutionary Computation (CEC), 2016 IEEE Congress on}, pages = {1030--1037}, organization = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Martín, Alejandro; Calleja, Alejandro; Menéndez, Héctor D; Tapiador, Juan; Camacho, David ADROIT: Android malware detection using meta-information Inproceedings Computational Intelligence (SSCI), 2016 IEEE Symposium Series on, pp. 1–8, IEEE 2016. BibTeX | Tags: @inproceedings{martin2016adroit, title = {ADROIT: Android malware detection using meta-information}, author = {Alejandro Martín and Alejandro Calleja and Héctor D Menéndez and Juan Tapiador and David Camacho}, year = {2016}, date = {2016-01-01}, booktitle = {Computational Intelligence (SSCI), 2016 IEEE Symposium Series on}, pages = {1--8}, organization = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2015 |
Lara-Cabrera, Raúl Generación Automática de Contenido para Juegos de Estrategia en Tiempo Real PhD Thesis 2015. BibTeX | Tags: @phdthesis{lara2015generacion, title = {Generación Automática de Contenido para Juegos de Estrategia en Tiempo Real}, author = {Raúl Lara-Cabrera}, year = {2015}, date = {2015-12-15}, publisher = {Servicio de Publicaciones y Divulgación Científica}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } |
Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David Modeling the Behavior of Unskilled Users in a Multi-UAV Simulation Environment Incollection Intelligent Data Engineering and Automated Learning--IDEAL 2015, pp. 441–448, Springer International Publishing, 2015. Abstract | Links | BibTeX | Tags: behavioral patterns, Hidden Markov Model, Human-Machine Interaction, UAV @incollection{rodriguez2015modeling, title = {Modeling the Behavior of Unskilled Users in a Multi-UAV Simulation Environment}, author = {Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2016/03/rodriguez2015modeling.pdf}, year = {2015}, date = {2015-10-14}, booktitle = {Intelligent Data Engineering and Automated Learning--IDEAL 2015}, pages = {441--448}, publisher = {Springer International Publishing}, abstract = {The use of Unmanned Aerial Vehicles (UAVs) has been growing over the last few years. The accelerated evolution of these systems is generating a high demand of qualified operators, which requires to redesign the training process and focus on a wider range of candidates, including inexperienced users in the field, in order to detect skilled-potential operators. This paper uses data from the interactions of multiple unskilled users in a simple multi-UAV simulator to create a behavioral model through the use of Hidden Markov Models (HMMs). An optimal HMM is validated and analyzed to extract common behavioral patterns among these users, so that it is proven that the model represents correctly the novelty of the users and may be used to detect and predict behaviors in multi-UAV systems.}, keywords = {behavioral patterns, Hidden Markov Model, Human-Machine Interaction, UAV}, pubstate = {published}, tppubtype = {incollection} } The use of Unmanned Aerial Vehicles (UAVs) has been growing over the last few years. The accelerated evolution of these systems is generating a high demand of qualified operators, which requires to redesign the training process and focus on a wider range of candidates, including inexperienced users in the field, in order to detect skilled-potential operators. This paper uses data from the interactions of multiple unskilled users in a simple multi-UAV simulator to create a behavioral model through the use of Hidden Markov Models (HMMs). An optimal HMM is validated and analyzed to extract common behavioral patterns among these users, so that it is proven that the model represents correctly the novelty of the users and may be used to detect and predict behaviors in multi-UAV systems. |
Martín, Alejandro; González, José Carlos; Pulido, José Carlos; García-Olaya, Ángel; Fernández, Fernando; Suárez-Mejías, Cristina Therapy Monitoring and Patient Evaluation with Social Robots Conference 3rd Workshop on ICTs for improving Patients Rehabilitation Research Techniques, REHAB 2015, 2015. Abstract | Links | BibTeX | Tags: Cerebral Palsy, QUEST, Social Robot, Therapy @conference{2015-MartinEtAl, title = {Therapy Monitoring and Patient Evaluation with Social Robots}, author = {Alejandro Martín and José Carlos González and José Carlos Pulido and Ángel García-Olaya and Fernando Fernández and Cristina Suárez-Mejías}, url = {http://www.plg.inf.uc3m.es/~jcpulido/files/2015-REHAB-Therapy-Monitoring-and-Patient-Evaluation-with-Social-Robots.pdf}, year = {2015}, date = {2015-09-18}, booktitle = {3rd Workshop on ICTs for improving Patients Rehabilitation Research Techniques, REHAB 2015}, abstract = {Social robots have a great potential. With high movement capabilities and large computational capacity, they allow to perform varied tasks that were usually conducted by humans. One of these tasks are physical therapies, where a therapist guides a patient through the realisation of a set of exercises. A robot, equipped with a sophisticated artificial vision system, can conduct these therapies and evaluate the patient movements. In this paper, we present a system that allows the therapist to design a complete therapy to be carried out by the robot, to start each session with the robot, to evaluate the patient condition over the therapy and to generate reports at the end of a session}, keywords = {Cerebral Palsy, QUEST, Social Robot, Therapy}, pubstate = {published}, tppubtype = {conference} } Social robots have a great potential. With high movement capabilities and large computational capacity, they allow to perform varied tasks that were usually conducted by humans. One of these tasks are physical therapies, where a therapist guides a patient through the realisation of a set of exercises. A robot, equipped with a sophisticated artificial vision system, can conduct these therapies and evaluate the patient movements. In this paper, we present a system that allows the therapist to design a complete therapy to be carried out by the robot, to start each session with the robot, to evaluate the patient condition over the therapy and to generate reports at the end of a session |
RodrÍguez-Fernández, Víctor; Ramirez-Atencia, Cristian; Camacho, David A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game Inproceedings 2st Congreso de la Sociedad Española para las Ciencias del Videojuego, pp. 186–191, 2015. Abstract | Links | BibTeX | Tags: Mission Planning, Muli-UAV, Multi-objective Optimization, Player Assessment, Serious Games, Unmanned Aircraft Systems @inproceedings{rodriguezsummary, title = {A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game}, author = {Víctor RodrÍguez-Fernández and Cristian Ramirez-Atencia and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2015/09/rodriguezsummary.pdf}, year = {2015}, date = {2015-07-31}, booktitle = {2st Congreso de la Sociedad Española para las Ciencias del Videojuego}, journal = {2st Congreso de la Sociedad Española para las Ciencias del Videojuego}, volume = {1394}, pages = {186--191}, abstract = {Mission Planning for a large number of Unmanned Aerial Vehicles (UAVs) involves a set of locations to visit in different time intervals, and the actions that a vehicle must perform depending on its features and sensors. Analyzing how humans solve this problem is sometimes hard due to the complexity of the problem and the lack of data available. This paper presents a summary of a serious videogame-based framework created to assess the quality of the mission plans designed by players, comparing them against the optimal solutions obtained by a Multi-Objective Optimization algorithm.}, keywords = {Mission Planning, Muli-UAV, Multi-objective Optimization, Player Assessment, Serious Games, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } Mission Planning for a large number of Unmanned Aerial Vehicles (UAVs) involves a set of locations to visit in different time intervals, and the actions that a vehicle must perform depending on its features and sensors. Analyzing how humans solve this problem is sometimes hard due to the complexity of the problem and the lack of data available. This paper presents a summary of a serious videogame-based framework created to assess the quality of the mission plans designed by players, comparing them against the optimal solutions obtained by a Multi-Objective Optimization algorithm. |
Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David Diseño de un Simulador de Bajo Coste para Vehículos Aéreos no Tripulados Inproceedings Actas del X Congreso español sobre metaheurísticas, algoritmos evolutivos y bioinspirados: MAEB, pp. 447-454, Mérida, Cáceres, 2015, ISBN: 978-84-697-2150-6. Abstract | Links | BibTeX | Tags: Computer-based Simulation, Unmanned Aircraft Systems, Web Application Development @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.ii.uam.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 = {Computer-based Simulation, Unmanned Aircraft Systems, Web Application Development}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, María D; Camacho, David A Hybrid MOGA-CSP for Multi-UAV Mission Planning Inproceedings Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pp. 1205–1208, ACM 2015. Links | BibTeX | Tags: Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, Unmanned Aircraft Systems @inproceedings{ramirez2015hybrid, title = {A Hybrid MOGA-CSP for Multi-UAV Mission Planning}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and María D R-Moreno and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2015/09/ramirez-atenciaHybrid.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference}, pages = {1205--1208}, organization = {ACM}, keywords = {Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } |
Rodriguez-Fernandez, Victor; Ramirez-Atencia, Cristian; Camacho, David A multi-UAV Mission Planning videogame-based framework for player analysis Inproceedings Evolutionary Computation (CEC), 2015 IEEE Congress on, pp. 1490–1497, IEEE 2015. Abstract | Links | BibTeX | Tags: Mission Planning, Multi-objective Optimization, Player Assessment, Serious Games, Unmanned Aircraft Systems @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.ii.uam.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 = {Mission Planning, Multi-objective Optimization, Player Assessment, Serious Games, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
Bello-Orgaz, Gema; Hernandez-Castro, Julio; Camacho, David A Survey of Social Web Mining Applications for Disease Outbreak Detection Incollection Intelligent Distributed Computing VIII, pp. 345–356, Springer International Publishing, 2015. Links | BibTeX | Tags: Epidemic Intelligence, Outbreak Detection, Social Networks, Web Data Mining @incollection{bello2015survey, title = {A Survey of Social Web Mining Applications for Disease Outbreak Detection}, author = {Gema Bello-Orgaz and Julio Hernandez-Castro and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2014/12/IDC14-Bello-OrgazEtAl.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Intelligent Distributed Computing VIII}, pages = {345--356}, publisher = {Springer International Publishing}, keywords = {Epidemic Intelligence, Outbreak Detection, Social Networks, Web Data Mining}, pubstate = {published}, tppubtype = {incollection} } |
Rodriguez-Fernandez, Victor; Menendez, Hector D; Camacho, David Design and development of a lightweight multi-UAV simulator Inproceedings Cybernetics (CYBCONF), 2015 IEEE 2nd International Conference on, pp. 255–260, IEEE 2015. Abstract | Links | BibTeX | Tags: Computer-based Simulation, Human-Machine Interaction, UAV, web @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.ii.uam.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 = {Computer-based Simulation, Human-Machine Interaction, UAV, web}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
i, V{i}ctor Rodr Development of a Multi-UAV Simulator to Analyze the Behavior of Operators in Coastal Surveillance Missions Journal Article 2015. @article{fernandez2015development, title = {Development of a Multi-UAV Simulator to Analyze the Behavior of Operators in Coastal Surveillance Missions}, author = {V{i}ctor Rodr{i}guez Fernández}, year = {2015}, date = {2015-01-01}, keywords = {Human}, pubstate = {published}, tppubtype = {article} } |
Lara-Cabrera, Raúl; Collazo, Mariela Nogueira; Cotta, Carlos; Leiva, Antonio José Fernández Game Artificial Intelligence: Challenges for the Scientific Community. Inproceedings CoSECivi, pp. 1–12, 2015. BibTeX | Tags: @inproceedings{lara2015game, title = {Game Artificial Intelligence: Challenges for the Scientific Community.}, author = {Raúl Lara-Cabrera and Mariela Nogueira Collazo and Carlos Cotta and Antonio José Fernández Leiva}, year = {2015}, date = {2015-01-01}, booktitle = {CoSECivi}, pages = {1--12}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Palero, Fernando; Ramirez-Atencia, Cristian; Camacho, David Online Gamers Classification Using K-means Incollection Intelligent Distributed Computing VIII, pp. 201–208, Springer International Publishing, 2015. Abstract | Links | BibTeX | Tags: K-Means, Player Strategies, Real Time Strategy Game, Sliding Windows, Video Games @incollection{palero2015online, title = {Online Gamers Classification Using K-means}, author = {Fernando Palero and Cristian Ramirez-Atencia and David Camacho}, url = {http://link.springer.com/chapter/10.1007/978-3-319-10422-5_22}, year = {2015}, date = {2015-01-01}, booktitle = {Intelligent Distributed Computing VIII}, pages = {201--208}, publisher = {Springer International Publishing}, abstract = {In order to achieve flow and increase player retention, it is important that games difficulty matches player skills. Being able to evaluate how people play a game is a crucial component for detecting gamers strategies in video-games. One of themain problems in player strategy detection is whether attributes selected to define strategies correctly detect the actions of the player. In this paper, we will study a Real Time Strategy (RTS) game. In RTS the participants make use of units and structures to secure areas of a map and/or destroy the opponents resources. We will extract real-time information about the players strategies at several gameplays through a Web Platform. After gathering enough information, the model will be evaluated in terms of unsupervised learning (concretely, K-Means). Finally, we will study the similitude between several gameplays where players use different strategies.}, keywords = {K-Means, Player Strategies, Real Time Strategy Game, Sliding Windows, Video Games}, pubstate = {published}, tppubtype = {incollection} } In order to achieve flow and increase player retention, it is important that games difficulty matches player skills. Being able to evaluate how people play a game is a crucial component for detecting gamers strategies in video-games. One of themain problems in player strategy detection is whether attributes selected to define strategies correctly detect the actions of the player. In this paper, we will study a Real Time Strategy (RTS) game. In RTS the participants make use of units and structures to secure areas of a map and/or destroy the opponents resources. We will extract real-time information about the players strategies at several gameplays through a Web Platform. After gathering enough information, the model will be evaluated in terms of unsupervised learning (concretely, K-Means). Finally, we will study the similitude between several gameplays where players use different strategies. |
Lara-Cabrera, Raúl; Collazo, Mariela Nogueira; Cotta, Carlos; Leiva, Antonio Fernández J Optimización en videojuegos: retos para la comunidad científica Journal Article 2015. BibTeX | Tags: @article{lara2015optimizacion, title = {Optimización en videojuegos: retos para la comunidad científica}, author = {Raúl Lara-Cabrera and Mariela Nogueira Collazo and Carlos Cotta and Antonio Fernández J Leiva}, year = {2015}, date = {2015-01-01}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Ramírez-Atencia, Cristian; Orgaz, Gema Bello; Rodríguez-Moreno, María Dolores; Camacho, David Performance Evaluation of Multi-UAV Cooperative Mission Planning Models Inproceedings Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II, pp. 203–212, 2015. Abstract | Links | BibTeX | Tags: branch and bound, Constraint Satisfaction Problems, Mission Planning, Unmanned Aircraft Systems @inproceedings{DBLP:conf/iccci/Ramirez-Atencia15, title = {Performance Evaluation of Multi-UAV Cooperative Mission Planning Models}, author = {Cristian Ramírez-Atencia and Gema Bello Orgaz and María Dolores Rodríguez-Moreno and David Camacho}, url = {http://dx.doi.org/10.1007/978-3-319-24306-1_20 http://aida.ii.uam.es/wp-content/uploads/2015/09/ramirez-atenciaPerformance.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II}, pages = {203--212}, crossref = {DBLP:conf/iccci/2015-2}, abstract = {The Multi-UAV Cooperative Mission Planning Problem (MCMPP) is a complex problem which can be represented with a lower or higher level of complexity. In this paper we present a MCMPP which is modelled as a Constraint Satisfaction Problem (CSP) with 5 increasing levels of complexity. Each level adds additional variables and constraints to the problem. Using previous models, we solve the problem using a Branch and Bound search designed to minimize the fuel consumption and number of UAVs employed in the mission, and the results show how runtime increases as the level of complexity increases in most cases, as expected, but there are some cases where the opposite happens.}, keywords = {branch and bound, Constraint Satisfaction Problems, Mission Planning, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } The Multi-UAV Cooperative Mission Planning Problem (MCMPP) is a complex problem which can be represented with a lower or higher level of complexity. In this paper we present a MCMPP which is modelled as a Constraint Satisfaction Problem (CSP) with 5 increasing levels of complexity. Each level adds additional variables and constraints to the problem. Using previous models, we solve the problem using a Branch and Bound search designed to minimize the fuel consumption and number of UAVs employed in the mission, and the results show how runtime increases as the level of complexity increases in most cases, as expected, but there are some cases where the opposite happens. |
Cabrera, Raúl Lara; Collazo, Mariela Nogueira; Porras, Carlos Cotta; Leiva, Antonio José Fernández Procedural content generation for real-time strategy games Journal Article IJIMAI, 3 (2), pp. 40–48, 2015. BibTeX | Tags: @article{cabrera2015procedural, title = {Procedural content generation for real-time strategy games}, author = {Raúl Lara Cabrera and Mariela Nogueira Collazo and Carlos Cotta Porras and Antonio José Fernández Leiva}, year = {2015}, date = {2015-01-01}, journal = {IJIMAI}, volume = {3}, number = {2}, pages = {40--48}, publisher = {Universidad Internacional de La Rioja}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Palero, Fernando; Palero, Antonio Gonzalez-Pardo Fernando; Camacho, David Simple gamer interaction analysis through tower defense games Conference 6th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2014), Lecture Notes in Artificial Intelligence of Springer-Verlag, 2015. BibTeX | Tags: Data Analysis, Data Mining, Videogames @conference{2015-PaleroEtAl, title = {Simple gamer interaction analysis through tower defense games}, author = {Fernando Palero and Antonio Gonzalez-Pardo Fernando Palero and David Camacho}, year = {2015}, date = {2015-01-01}, booktitle = {6th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2014)}, pages = {185-194}, publisher = {Lecture Notes in Artificial Intelligence of Springer-Verlag}, keywords = {Data Analysis, Data Mining, Videogames}, pubstate = {published}, tppubtype = {conference} } |
Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David User Profile Analysis for UAV Operators in a Simulation Environment Inproceedings Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015. Proceedings, Part I, pp. 338–347, 2015. Abstract | Links | BibTeX | Tags: behavioral patterns, Clustering, Computer-based Simulation, Human-Machine Interaction, Performance Metrics, UAV, Videogames @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.ii.uam.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 = {behavioral patterns, Clustering, Computer-based Simulation, Human-Machine Interaction, Performance Metrics, UAV, Videogames}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
2014 |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; Camacho, David; others, Solving UAV Mission Planning based on Temporal Constaint Satisfaction Problem using Genetic Algorithms Miscellaneous Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014), 2014. Abstract | Links | BibTeX | Tags: Genetic Algorithms, Mission Planning, Temporal Constraint Satisfaction Problems, Unmanned Aircraft Systems @misc{ramirez2014solving, title = {Solving UAV Mission Planning based on Temporal Constaint Satisfaction Problem using Genetic Algorithms}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and David Camacho and others}, url = {http://aida.ii.uam.es/wp-content/uploads/2014/12/RamirezEtAl.pdf}, year = {2014}, date = {2014-09-12}, abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to visit in di erent time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. We study how this problem can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). This problem contains several constraints assuring UAVs are assigned to tasks they have enough characteristics to perform, and soft-constraints for optimizing the time and fuel spent in the process. Our goal is to implement this model and then try to solve it using Genetic Algorithms (GAs). For this purpose, we will carry out a mission simulation containing m UAVs with di erent sensors and characteristics located in di erent waypoints, and n requested tasks varying mission priorities. The GA will match the model constraints and use a multi-objective function in order to minimize the cost.}, howpublished = {Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014)}, keywords = {Genetic Algorithms, Mission Planning, Temporal Constraint Satisfaction Problems, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {misc} } The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to visit in di erent time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. We study how this problem can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). This problem contains several constraints assuring UAVs are assigned to tasks they have enough characteristics to perform, and soft-constraints for optimizing the time and fuel spent in the process. Our goal is to implement this model and then try to solve it using Genetic Algorithms (GAs). For this purpose, we will carry out a mission simulation containing m UAVs with di erent sensors and characteristics located in di erent waypoints, and n requested tasks varying mission priorities. The GA will match the model constraints and use a multi-objective function in order to minimize the cost. |
Gonzalez-Pardo, Antonio; Camacho, David Solving Resource-Constraint Project Scheduling Problems based on ACO algorithms Conference Ninth International Conference on Swarm Intelligence (ANTS 2014)., 8667 , Lecture Notes in Computer Science of Springer-Verlag, 2014. Links | BibTeX | Tags: Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, CSP-graph based representation @conference{2014-GonzalezCamachoANTS, title = {Solving Resource-Constraint Project Scheduling Problems based on ACO algorithms}, author = {Antonio Gonzalez-Pardo and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2014/09/2014-ANTS-GonzalezCamacho.pdf}, year = {2014}, date = {2014-09-10}, booktitle = {Ninth International Conference on Swarm Intelligence (ANTS 2014).}, volume = {8667}, pages = {290-291}, publisher = {Lecture Notes in Computer Science of Springer-Verlag}, keywords = {Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, CSP-graph based representation}, pubstate = {published}, tppubtype = {conference} } |
Gonzalez-Pardo, Antonio; Camacho, David A New CSP Graph-Based Representation to Resource-Constrained Project Scheduling Problem Conference 2014 IEEE Conference on Evolutionary Computation (CEC 2014), 2014. Links | BibTeX | Tags: Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, CSP-graph based representation @conference{2014-GonzalezCamachoCEC, title = {A New CSP Graph-Based Representation to Resource-Constrained Project Scheduling Problem}, author = {Antonio Gonzalez-Pardo and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2014/09/2014-CEC-GonzalezCamacho.pdf}, year = {2014}, date = {2014-07-07}, booktitle = {2014 IEEE Conference on Evolutionary Computation (CEC 2014)}, pages = {344-351}, keywords = {Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, CSP-graph based representation}, pubstate = {published}, tppubtype = {conference} } |
Gonzalez-Pardo, Antonio; Palero, Fernando; Camacho, David Micro and Macro Lemmings simulations based on ants colonies Conference EvoApp 2014, In press , 2014. BibTeX | Tags: Ant Colony Optimization, Constraint Satisfaction Problems, CSP-graph based representation, Videogames @conference{14-GonzalezEtAl-EvoApp, title = {Micro and Macro Lemmings simulations based on ants colonies}, author = {Antonio Gonzalez-Pardo and Fernando Palero and David Camacho}, year = {2014}, date = {2014-04-23}, booktitle = {EvoApp 2014}, volume = {In press}, keywords = {Ant Colony Optimization, Constraint Satisfaction Problems, CSP-graph based representation, Videogames}, pubstate = {published}, tppubtype = {conference} } |
Gonzalez-Pardo, Antonio; Palero, Fernando; Camacho, David An empirical study on collective intelligence algorithms for video games problem-solving Journal Article Computing and Informatics, In press , 2014, ISSN: 1335-9150. BibTeX | Tags: Ant Colony Optimization, Constraint Satisfaction Problems, CSP-graph based representation, Videogames @article{14-GonzalezEtAl-CAI, title = {An empirical study on collective intelligence algorithms for video games problem-solving}, author = {Antonio Gonzalez-Pardo and Fernando Palero and David Camacho}, issn = {1335-9150}, year = {2014}, date = {2014-01-21}, journal = {Computing and Informatics}, volume = {In press}, keywords = {Ant Colony Optimization, Constraint Satisfaction Problems, CSP-graph based representation, Videogames}, pubstate = {published}, tppubtype = {article} } |
Menendez, Hector D; Barrero, David F; Camacho, David A Co-Evolutionary Multi-Objective approach for a K-adaptive graph-based clustering algorithm Inproceedings Evolutionary Computation (CEC), 2014 IEEE Congress on, pp. 2724–2731, IEEE 2014. BibTeX | Tags: Clustering, Genetic Algorithms, Graph Theory, Multi-objective Algorithms @inproceedings{menendez2014co, title = {A Co-Evolutionary Multi-Objective approach for a K-adaptive graph-based clustering algorithm}, author = {Hector D Menendez and David F Barrero and David Camacho}, year = {2014}, date = {2014-01-01}, booktitle = {Evolutionary Computation (CEC), 2014 IEEE Congress on}, pages = {2724--2731}, organization = {IEEE}, keywords = {Clustering, Genetic Algorithms, Graph Theory, Multi-objective Algorithms}, pubstate = {published}, tppubtype = {inproceedings} } |
Menéndez, Héctor D; Barrero, David F; Camacho, David A Genetic Graph-based Approach for Partitional Clustering Journal Article International journal of neural systems, 24 (03), 2014. BibTeX | Tags: Clustering, Genetic Algorithms, Graph Theory @article{menendez2014genetic, title = {A Genetic Graph-based Approach for Partitional Clustering}, author = {Héctor D Menéndez and David F Barrero and David Camacho}, year = {2014}, date = {2014-01-01}, journal = {International journal of neural systems}, volume = {24}, number = {03}, publisher = {World Scientific Publishing Company}, keywords = {Clustering, Genetic Algorithms, Graph Theory}, pubstate = {published}, tppubtype = {article} } |
Menendez, Hector D; Camacho, David A Multi-Objective Graph-based Genetic Algorithm for Image Segmentation Inproceedings Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on, pp. 234–241, IEEE 2014. BibTeX | Tags: Clustering, Genetic Algorithms, Graph Theory, Multi-objective Algorithms @inproceedings{menendez2014multi, title = {A Multi-Objective Graph-based Genetic Algorithm for Image Segmentation}, author = {Hector D Menendez and David Camacho}, year = {2014}, date = {2014-01-01}, booktitle = {Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on}, pages = {234--241}, organization = {IEEE}, keywords = {Clustering, Genetic Algorithms, Graph Theory, Multi-objective Algorithms}, pubstate = {published}, tppubtype = {inproceedings} } |
Lara-Cabrera, Raúl; Cotta, Carlos; Fernández-Leiva, Antonio J A self-adaptive evolutionary approach to the evolution of aesthetic maps for a RTS game Journal Article 2014. BibTeX | Tags: @article{lara2014self, title = {A self-adaptive evolutionary approach to the evolution of aesthetic maps for a RTS game}, author = {Raúl Lara-Cabrera and Carlos Cotta and Antonio J Fernández-Leiva}, year = {2014}, date = {2014-01-01}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; Camacho, David; others, A simple CSP-based model for Unmanned Air Vehicle Mission Planning Inproceedings Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on, pp. 146–153, IEEE 2014. Abstract | Links | BibTeX | Tags: Backtracking, Mission Planning, Temporal Constraint Satisfaction Problems, Unmanned Aircraft Systems @inproceedings{ramirez2014simple, title = {A simple CSP-based model for Unmanned Air Vehicle Mission Planning}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and David Camacho and others}, url = {http://aida.ii.uam.es/wp-content/uploads/2014/12/Ramirez-AtenciaEtAl.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on}, pages = {146--153}, organization = {IEEE}, abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). It consists on a set of locations that should visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. In this paper, a temporal constraint model is implemented and tested by performing Backtracking search in several missions where its complexity has been incrementally modified. The experimental phase consists on two different phases. On the one hand, several mission simulations containing (n) UAVs using different sensors and characteristics located in different waypoints, and (m) requested tasks varying mission priorities have been carried out. On the other hand, the second experimental phase uses a backtracking algorithm to look through the whole solutions space to measure the scalability of the problem. This scalability has been measured as a relation between the number of tasks to be performed in the mission and the number of UAVs needed to perform it.}, keywords = {Backtracking, Mission Planning, Temporal Constraint Satisfaction Problems, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). It consists on a set of locations that should visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. In this paper, a temporal constraint model is implemented and tested by performing Backtracking search in several missions where its complexity has been incrementally modified. The experimental phase consists on two different phases. On the one hand, several mission simulations containing (n) UAVs using different sensors and characteristics located in different waypoints, and (m) requested tasks varying mission priorities have been carried out. On the other hand, the second experimental phase uses a backtracking algorithm to look through the whole solutions space to measure the scalability of the problem. This scalability has been measured as a relation between the number of tasks to be performed in the mission and the number of UAVs needed to perform it. |