2024
Guedan-Pecker, Fernando; Ramirez-Atencia, Cristian
Airport take-off and landing optimization through genetic algorithms Journal Article
In: Expert Systems, 2024.
Abstract | Links | BibTeX | Tags: Airport operations, Constraint Satisfaction Problems, Genetic Algorithms, Planning, Pollution
@article{Guedan2024Airport,
title = {Airport take-off and landing optimization through genetic algorithms},
author = {Fernando Guedan-Pecker and Cristian Ramirez-Atencia},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13565},
doi = {https://doi.org/10.1111/exsy.13565},
year = {2024},
date = {2024-02-20},
journal = {Expert Systems},
abstract = {This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously, a novel approach not previously explored. The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airports. This algorithm uniquely integrates the optimization of both landing gates and take-off/landing runways, considering the correlation between engine operation time and pollutant levels. The approach employs advanced constraint handling techniques to manage the intricate time and resource limitations inherent in airport operations. Additionally, the study conducts a thorough sensitivity analysis of the model, with a particular emphasis on the mutation factor and the type of penalty function, to fine-tune the optimization process. This dual-focus optimization strategy represents a significant advancement in reducing environmental impact in the aviation sector, establishing a new standard for comprehensive and efficient airport operation management.},
keywords = {Airport operations, Constraint Satisfaction Problems, Genetic Algorithms, Planning, Pollution},
pubstate = {published},
tppubtype = {article}
}
This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously, a novel approach not previously explored. The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airports. This algorithm uniquely integrates the optimization of both landing gates and take-off/landing runways, considering the correlation between engine operation time and pollutant levels. The approach employs advanced constraint handling techniques to manage the intricate time and resource limitations inherent in airport operations. Additionally, the study conducts a thorough sensitivity analysis of the model, with a particular emphasis on the mutation factor and the type of penalty function, to fine-tune the optimization process. This dual-focus optimization strategy represents a significant advancement in reducing environmental impact in the aviation sector, establishing a new standard for comprehensive and efficient airport operation management.
2019
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.
Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Multi-objective Optimization, Planning, Unmanned Aircraft Systems
@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},
pages = {2467--2484},
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.},
keywords = {Constraint Satisfaction Problems, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {article}
}
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.
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.
Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Multi-objective Optimization, Planning, Unmanned Aircraft Systems
@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.},
keywords = {Constraint Satisfaction Problems, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {article}
}
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.
2018
Ramirez-Atencia, Cristian
Automated mission planning and decision support systems for multiple unmanned aerial vehicles PhD Thesis
Universidad Autónoma de Madrid, 2018.
Links | BibTeX | Tags: Constraint Satisfaction Problems, Decision Making, Multi-objective Optimization, Planning, Unmanned Aircraft Systems
@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},
year = {2018},
date = {2018-10-22},
school = {Universidad Autónoma de Madrid},
keywords = {Constraint Satisfaction Problems, Decision Making, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {phdthesis}
}
Panizo-LLedot, Ángel; Bello-Orgaz, Gema; Carnero, Mercedes; Hernández, José; Sánchez, Mabel; Camacho, David
An Artificial Bee Colony algorithm for optimizing the design of sensor networks Proceedings Article
In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 316–324, Springer 2018.
Links | BibTeX | Tags: Artificial Bee Colony, Bio-inspired Metaheuristics, Constraint Satisfaction Problems, Industry, Swarm Intelligence
@inproceedings{panizo2018artificial,
title = {An Artificial Bee Colony algorithm for optimizing the design of sensor networks},
author = {Ángel Panizo-LLedot and Gema Bello-Orgaz and Mercedes Carnero and José Hernández and Mabel Sánchez and David Camacho},
doi = {10.1007/978-3-030-03496-2_35},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {International Conference on Intelligent Data Engineering and Automated Learning},
pages = {316--324},
organization = {Springer},
keywords = {Artificial Bee Colony, Bio-inspired Metaheuristics, Constraint Satisfaction Problems, Industry, Swarm Intelligence},
pubstate = {published},
tppubtype = {inproceedings}
}
2017
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.
Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Multi-objective Optimization, Planning, Unmanned Aircraft Systems
@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 = {2017-09-01},
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.},
keywords = {Constraint Satisfaction Problems, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},
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.
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.
Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Genetic Algorithms, Multi-objective Optimization, Planning, Unmanned Aircraft Systems
@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},
issn = {1432-7643; 1433-7479},
year = {2017},
date = {2017-09-01},
urldate = {2016-01-01},
journal = {Soft Computing},
volume = {21},
issue = {17},
pages = {4883-4900},
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.},
keywords = {Constraint Satisfaction Problems, Genetic Algorithms, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},
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.
Ramirez-Atencia, Cristian; Mostaghim, Sanaz; Camacho, David
A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems Proceedings Article
In: 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, Multi-objective Optimization, Planning, Unmanned Aircraft Systems
@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},
urldate = {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, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},
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
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 Proceedings Article
In: XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016), pp. 305–314, 2016.
Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Genetic Algorithms, Multi-objective Optimization, Planning, Unmanned Aircraft Systems
@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 = {https://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},
date = {2016-01-01},
urldate = {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, Genetic Algorithms, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},
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.
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning Proceedings Article
In: 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, Genetic Algorithms, Multi-objective Optimization, Planning, 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, Genetic Algorithms, Multi-objective Optimization, Planning, 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.
2015
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, María D; Camacho, David
A Hybrid MOGA-CSP for Multi-UAV Mission Planning Proceedings Article
In: 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, Multi-objective Optimization, Planning, 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 = {https://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 = {Constraint Satisfaction Problems, Genetic Algorithms, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramírez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
Performance Evaluation of Multi-UAV Cooperative Mission Planning Models Proceedings Article
In: 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: Constraint Satisfaction Problems, 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 Maria D R-Moreno and David Camacho},
url = {http://dx.doi.org/10.1007/978-3-319-24306-1_20
https://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 = {Constraint Satisfaction Problems, 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.
Rodriguez-Fernández, Victor; Ramirez-Atencia, Cristian; Camacho, David
A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game Proceedings Article
In: 2nd Congreso de la Sociedad Espa~nola para las Ciencias del Videojuego (CoSeCiVi 2015), 2015.
BibTeX | Tags: Constraint Satisfaction Problems, Planning, Serious Games, Simulation-based Training, Unmanned Aircraft Systems
@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 = {Constraint Satisfaction Problems, Planning, Serious Games, Simulation-based Training, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
2014
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 Proceedings Article
In: Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014), 2014.
Abstract | BibTeX | Tags: Constraint Satisfaction Problems, Genetic Algorithms, Planning, Unmanned Aircraft Systems
@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 = {Constraint Satisfaction Problems, Genetic Algorithms, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Gonzalez-Pardo, Antonio; Camacho, David
Solving Resource-Constraint Project Scheduling Problems based on ACO algorithms Conference
Ninth International Conference on Swarm Intelligence (ANTS 2014)., vol. 8667, Lecture Notes in Computer Science of Springer-Verlag, 2014.
Links | BibTeX | Tags: Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, Graph Theory, Swarm Intelligence
@conference{2014-GonzalezCamachoANTS,
title = {Solving Resource-Constraint Project Scheduling Problems based on ACO algorithms},
author = {Antonio Gonzalez-Pardo and David Camacho},
url = {https://aida.etsisi.upm.es/wp-content/uploads/2014/09/2014-ANTS-GonzalezCamacho.pdf},
year = {2014},
date = {2014-09-10},
urldate = {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, Graph Theory, Swarm Intelligence},
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.
BibTeX | Tags: Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, Graph Theory, Swarm Intelligence
@conference{2014-GonzalezCamachoCEC,
title = {A New CSP Graph-Based Representation to Resource-Constrained Project Scheduling Problem},
author = {Antonio Gonzalez-Pardo and David Camacho},
year = {2014},
date = {2014-07-07},
urldate = {2014-07-07},
booktitle = {2014 IEEE Conference on Evolutionary Computation (CEC 2014)},
pages = {344-351},
keywords = {Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, Graph Theory, Swarm Intelligence},
pubstate = {published},
tppubtype = {conference}
}
Gonzalez-Pardo, Antonio; Palero, Fernando; Camacho, David
Micro and Macro Lemmings simulations based on ants colonies Conference
EvoApp 2014, vol. In press, 2014.
BibTeX | Tags: Ant Colony Optimization, Constraint Satisfaction Problems, Graph Theory, Swarm Intelligence, 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},
urldate = {2014-04-23},
booktitle = {EvoApp 2014},
volume = {In press},
keywords = {Ant Colony Optimization, Constraint Satisfaction Problems, Graph Theory, Swarm Intelligence, 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
In: Computing and Informatics, vol. In press, 2014, ISSN: 1335-9150.
BibTeX | Tags: Ant Colony Optimization, Constraint Satisfaction Problems, Graph Theory, Swarm Intelligence, 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},
urldate = {2014-01-21},
journal = {Computing and Informatics},
volume = {In press},
keywords = {Ant Colony Optimization, Constraint Satisfaction Problems, Graph Theory, Swarm Intelligence, Videogames},
pubstate = {published},
tppubtype = {article}
}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
A simple CSP-based model for unmanned air vehicle mission planning Proceedings Article
In: 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, pp. 146–153, IEEE 2014.
BibTeX | Tags: Constraint Satisfaction Problems, Planning, 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 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 = {Constraint Satisfaction Problems, Planning, Unmanned Aircraft Systems},
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 Proceedings Article
In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 286–294, Springer, Cham 2014.
BibTeX | Tags: Constraint Satisfaction Problems, Planning, Unmanned Aircraft Systems
@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 = {Constraint Satisfaction Problems, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramirez-Atencia, Cristian
Modelling Unmanned Vehicles Mission Planning problems as Constraint Satisfaction Problems Masters Thesis
Universidad Autónoma de Madrid, 2014.
BibTeX | Tags: Constraint Satisfaction Problems, Planning, Unmanned Aircraft Systems
@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 = {Constraint Satisfaction Problems, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {mastersthesis}
}
2013
Gonzalez-Pardo, Antonio; Camacho, David
A new CSP graph-based representation for Ant Colony Optimization Conference
2013 IEEE Conference on Evolutionary Computation (CEC 2013), vol. 1, 2013.
BibTeX | Tags: Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, Graph Theory, Swarm Intelligence, Theorical
@conference{13-GonzalezCamacho-CEC,
title = {A new CSP graph-based representation for Ant Colony Optimization},
author = {Antonio Gonzalez-Pardo and David Camacho},
year = {2013},
date = {2013-05-13},
urldate = {2013-05-13},
booktitle = {2013 IEEE Conference on Evolutionary Computation (CEC 2013)},
volume = {1},
pages = {689--696},
keywords = {Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, Graph Theory, Swarm Intelligence, Theorical},
pubstate = {published},
tppubtype = {conference}
}