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Ramirez-Atencia, Cristian; Rodriguez-Fernandez, Victor; Camacho, David
A revision on multi-criteria decision making methods for multi-UAV mission planning support Journal Article
In: Expert Systems with Applications, vol. 160, pp. 113708, 2020, ISSN: 0957-4174.
@article{RAMIREZATENCIA2020113708,
title = {A revision on multi-criteria decision making methods for multi-UAV mission planning support},
author = {Cristian Ramirez-Atencia and Victor Rodriguez-Fernandez and David Camacho},
url = {https://www.sciencedirect.com/science/article/pii/S0957417420305327},
doi = {https://doi.org/10.1016/j.eswa.2020.113708},
issn = {0957-4174},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Expert Systems with Applications},
volume = {160},
pages = {113708},
abstract = {Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the mission planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variables that must be optimized simultaneously, such as the makespan, the cost of the mission or the risk. Therefore, the problem has a lot of possible optimal solutions, and the operator must select the final solution to be executed among them. In order to reduce the workload of the operator in this decision process, a Decision Support System (DSS) becomes necessary. In this work, a DSS consisting of ranking and filtering systems, which order and reduce the optimal solutions, has been designed. With regard to the ranking system, a wide range of Multi-Criteria Decision Making (MCDM) methods, including some fuzzy MCDM, are compared on a multi-UAV mission planning scenario, in order to study which method could fit better in a multi-UAV decision support system. Expert operators have evaluated the solutions returned, and the results show, on the one hand, that fuzzy methods generally achieve better average scores, and on the other, that all of the tested methods perform better when the preferences of the operators are biased towards a specific variable, and worse when their preferences are balanced. For the filtering system, a similarity function based on the proximity of the solutions has been designed, and on top of that, a threshold is tuned empirically to decide how to filter solutions without losing much of the hypervolume of the space of solutions.},
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Ramirez-Atencia, Cristian; Camacho, David
Constrained multi-objective optimization for multi-UAV planning Journal Article
In: Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 6, pp. 2467–2484, 2019, ISSN: 1868-5145.
@article{ramirez2019constrained,
title = {Constrained multi-objective optimization for multi-UAV planning},
author = {Cristian Ramirez-Atencia and David Camacho},
doi = {10.1007/s12652-018-0930-0},
issn = {1868-5145},
year = {2019},
date = {2019-06-01},
journal = {Journal of Ambient Intelligence and Humanized Computing},
volume = {10},
number = {6},
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publisher = {Springer Berlin Heidelberg},
abstract = {Over the last decade, developments in unmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or cost, among others. In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model, which is used in the fitness function of the algorithm. The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied.},
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Ramirez-Atencia, Cristian; Ser, Javier Del; Camacho, David
Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning Journal Article
In: Swarm and Evolutionary Computation, vol. 44, pp. 480–495, 2019, ISSN: 2210-6502.
@article{ramirez2019weighted,
title = {Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning},
author = {Cristian Ramirez-Atencia and Javier Del Ser and David Camacho},
doi = {10.1016/j.swevo.2018.06.005},
issn = {2210-6502},
year = {2019},
date = {2019-02-01},
journal = {Swarm and Evolutionary Computation},
volume = {44},
pages = {480--495},
publisher = {Elsevier},
abstract = {Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naïve MOEA approach.},
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Rodriguez-Fernandez, Victor
Extensions of Hidden Markov Models for supporting instructors in the analysis of training operations in an Unmanned Aircraft System Inproceedings
In: 2019 First International Conference on Societal Automation (SA), pp. 1–7, IEEE, Krakow, Poland, 2019, ISBN: 978-1-72813-345-4.
@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},
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Ramirez-Atencia, Cristian
Automated mission planning and decision support systems for multiple unmanned aerial vehicles PhD Thesis
Universidad Autónoma de Madrid, 2018.
@phdthesis{ramirez2018automated,
title = {Automated mission planning and decision support systems for multiple unmanned aerial vehicles},
author = {Cristian Ramirez-Atencia},
url = {http://hdl.handle.net/10486/686590},
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date = {2018-10-22},
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Ramirez-Atencia, Cristian; Rodriguez-Fernandez, Victor; Camacho, David
A multi-criteria decision support system for multi-UAV mission planning Incollection
In: Data Science and Knowledge Engineering for Sensing Decision Support, vol. 11, pp. 1083–1090, World Scientific, 2018, ISBN: 978-981-3273-22-1.
@incollection{ramirez2018multi,
title = {A multi-criteria decision support system for multi-UAV mission planning},
author = {Cristian Ramirez-Atencia and Victor Rodriguez-Fernandez and David Camacho},
doi = {10.1142/9789813273238_0137},
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year = {2018},
date = {2018-10-01},
booktitle = {Data Science and Knowledge Engineering for Sensing Decision Support},
volume = {11},
pages = {1083--1090},
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series = {World Scientific Proceedings Series on Computer Engineering and Information Science},
abstract = {The Multi-UAV Mission Planning problem is focused on the search of a set of solutions that satisfy several constraints on the mission scenario and has some variables to be optimized, such as the makespan, the cost of the mission or the risk. Thus, there could exist a large number of solutions to the problem. It turns a big issue for the operator to select the final solution to execute among the many obtained. In order to reduce the operator workload, this work proposes a Multi-Criteria Decision Support System, which consists of a ranking function that sorts the solutions obtained. Several ranking functions have been tested in real mission scenarios with different operator profiles. Expert operators have evaluated the solutions returned in order to compare the different ranking systems and demonstrate the usefulness of the proposed approach.},
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Rodriguez-Fernández, Victor; González-Pardo, Antonio; Camacho, David
Inteligencia Artificial aplicada al análisis de entrenamiento en operaciones con UAVs Inproceedings
In: Actas V Congreso Nacional de i+d en Defensa y Seguridad, pp. 1491–1498, Ministerio de Defensa. Secretaría General Técnica., Toledo, España, 2018, ISBN: 978-84-9091-357-4.
@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},
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address = {Toledo, España},
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Ramirez-Atencia, Cristian; Camacho, David
Extending QGroundControl for automated mission planning of UAVs Journal Article
In: Sensors, vol. 18, no. 7, pp. 2339, 2018, ISSN: 1424-8220.
@article{ramirez2018extending,
title = {Extending QGroundControl for automated mission planning of UAVs},
author = {Cristian Ramirez-Atencia and David Camacho},
url = {https://www.mdpi.com/1424-8220/18/7/2339},
doi = {10.3390/s18072339},
issn = {1424-8220},
year = {2018},
date = {2018-07-18},
journal = {Sensors},
volume = {18},
number = {7},
pages = {2339},
publisher = {MDPI},
abstract = {Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided.},
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Ramirez-Atencia, Cristian; R-Moreno, Maria D; Camacho, David
Handling swarm of UAVs based on evolutionary multi-objective optimization Journal Article
In: Progress in Artificial Intelligence, vol. 6, iss. 3, pp. 263-274, 2017, ISSN: 2192-6352.
@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},
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urldate = {2017-01-01},
journal = {Progress in Artificial Intelligence},
volume = {6},
issue = {3},
pages = {263-274},
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.},
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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
In: Soft Computing, vol. 21, iss. 17, pp. 4883-4900, 2017, ISSN: 1432-7643; 1433-7479.
@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},
doi = {10.1007/s00500-016-2376-7},
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urldate = {2016-01-01},
journal = {Soft Computing},
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issue = {17},
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publisher = {Springer Berlin Heidelberg},
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.},
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Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David
A study on performance metrics and clustering methods for analyzing behavior in UAV operations Journal Article
In: Journal of Intelligent and Fuzzy Systems, vol. 32, no. 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},
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Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David
Analysing temporal performance profiles of UAV operators using time series clustering Journal Article
In: Expert Systems with Applications, vol. 70, pp. 103–118, 2017, ISSN: 0957-4174.
@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},
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Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David
Automatic Procedure Following Evaluation using Petri Net-based Workflows Journal Article
In: IEEE Transactions on Industrial Informatics, vol. In press, 2017.
@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},
urldate = {2017-01-01},
journal = {IEEE Transactions on Industrial Informatics},
volume = {In press},
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Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David
Modelling Behaviour in UAV Operations Using Higher Order Double Chain Markov Models Journal Article
In: IEEE Computational Intelligence Magazine, vol. 12, no. 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},
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date = {2017-01-01},
urldate = {2017-01-01},
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Ramírez-Atencia, Cristian; Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David
New Artificial Intelligence approaches for future UAV Ground Control Stations Inproceedings
In: 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},
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Ramirez-Atencia, Cristian; Mostaghim, Sanaz; Camacho, David
A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems Inproceedings
In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1216–1223, ACM, Berlin, Germany, 2017, ISBN: 978-1-4503-4920-8.
@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},
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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.},
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Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David
Automatic profile generation for UAV operators using a simulation-based training environment Journal Article
In: Progress in Artificial Intelligence, vol. 5, no. 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},
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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
In: XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016), pp. 305–314, 2016.
@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.etsisi.upm.es/wp-content/uploads/2017/03/A-Weighted-Penalty-Fitness-for-a-Hybrid-MOGA-CSP-to-solve-Mission-Planning-Problems.pdf},
year = {2016},
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urldate = {2016-01-01},
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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 = {},
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}
Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David
Finding behavioral patterns of UAV operators using Multichannel Hidden Markov Models Inproceedings
In: 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},
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Bello-Orgaz, Gema; Ramirez-Atencia, Cristian; Fradera-Gil, Jaime; Camacho, David
GAMPP: Genetic Algorithm for UAV Mission Planning Problems Incollection
In: Intelligent Distributed Computing IX, pp. 167–176, Springer International Publishing, 2016.
@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},
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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.},
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Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning Inproceedings
In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016, ISBN: 978-1-5090-4240-1, 978-1-5090-4241-8.
@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 = {},
pubstate = {published},
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}
Rodríguez-Fernández, Víctor; Gonzalez-Pardo, Antonio; Camacho, David
Modeling the Behavior of Unskilled Users in a Multi-UAV Simulation Environment Incollection
In: Intelligent Data Engineering and Automated Learning--IDEAL 2015, pp. 441–448, Springer International Publishing, 2015.
@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.etsisi.upm.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 = {},
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}
Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David
Diseño de un Simulador de Bajo Coste para Vehículos Aéreos no Tripulados Inproceedings
In: Actas del X Congreso español sobre metaheurísticas, algoritmos evolutivos y bioinspirados: MAEB, pp. 447-454, Mérida, Cáceres, 2015, ISBN: 978-84-697-2150-6.
@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.},
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Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, María D; Camacho, David
A Hybrid MOGA-CSP for Multi-UAV Mission Planning Inproceedings
In: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pp. 1205–1208, ACM 2015.
@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.etsisi.upm.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 = {},
pubstate = {published},
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Rodriguez-Fernandez, Victor; Ramirez-Atencia, Cristian; Camacho, David
A multi-UAV Mission Planning videogame-based framework for player analysis Inproceedings
In: Evolutionary Computation (CEC), 2015 IEEE Congress on, pp. 1490–1497, IEEE 2015.
@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}
}
Rodriguez-Fernandez, Victor; Menendez, Hector D; Camacho, David
Design and development of a lightweight multi-UAV simulator Inproceedings
In: Cybernetics (CYBCONF), 2015 IEEE 2nd International Conference on, pp. 255–260, IEEE 2015.
@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}
}
Rodriguez-Fernandez, Victor
Development of a Multi-UAV Simulator to Analyze the Behavior of Operators in Coastal Surveillance Missions Masters Thesis
2015.
@mastersthesis{fernandez2015development,
title = {Development of a Multi-UAV Simulator to Analyze the Behavior of Operators in Coastal Surveillance Missions},
author = {Victor Rodriguez-Fernandez},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Ramírez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
Performance Evaluation of Multi-UAV Cooperative Mission Planning Models Inproceedings
In: Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II, pp. 203–212, 2015.
@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 Maria D R-Moreno and David Camacho},
url = {http://dx.doi.org/10.1007/978-3-319-24306-1_20
http://aida.etsisi.upm.es/wp-content/uploads/2015/09/ramirez-atenciaPerformance.pdf},
year = {2015},
date = {2015-01-01},
urldate = {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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David
User Profile Analysis for UAV Operators in a Simulation Environment Inproceedings
In: Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015. Proceedings, Part I, pp. 338–347, 2015.
@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}
}
Rodriguez-Fernández, Victor; Ramirez-Atencia, Cristian; Camacho, David
A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game Inproceedings
In: 2nd Congreso de la Sociedad Espa~nola para las Ciencias del Videojuego (CoSeCiVi 2015), 2015.
@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},
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Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
Solving UAV Mission Planning based on Temporal Constaint Satisfaction Problem using Genetic Algorithms Inproceedings
In: Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014), 2014.
@inproceedings{ramirez2014solving,
title = {Solving UAV Mission Planning based on Temporal Constaint Satisfaction Problem using Genetic Algorithms},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
year = {2014},
date = {2014-09-12},
urldate = {2014-09-12},
booktitle = {Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014)},
abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to 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. 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 different sensors and characteristics located in different 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.},
keywords = {},
pubstate = {published},
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}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
A simple CSP-based model for unmanned air vehicle mission planning Inproceedings
In: 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, pp. 146–153, IEEE 2014.
@inproceedings{ramirez2014simple,
title = {A simple CSP-based model for unmanned air vehicle mission planning},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings},
pages = {146--153},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
Branching to find feasible solutions in unmanned air vehicle mission planning Inproceedings
In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 286–294, Springer, Cham 2014.
@inproceedings{ramirez2014branching,
title = {Branching to find feasible solutions in unmanned air vehicle mission planning},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {International Conference on Intelligent Data Engineering and Automated Learning},
pages = {286--294},
organization = {Springer, Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramirez-Atencia, Cristian
Modelling Unmanned Vehicles Mission Planning problems as Constraint Satisfaction Problems Masters Thesis
Universidad Autónoma de Madrid, 2014.
@mastersthesis{ramirez2014modelling,
title = {Modelling Unmanned Vehicles Mission Planning problems as Constraint Satisfaction Problems},
author = {Cristian Ramirez-Atencia },
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
school = {Universidad Autónoma de Madrid},
keywords = {},
pubstate = {published},
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}