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.
2020
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.
Abstract | Links | BibTeX | Tags: Decision Making, Fuzzy methods, Planning, Unmanned Aircraft Systems
@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.},
keywords = {Decision Making, Fuzzy methods, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {article}
}
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.
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}
}
Ramirez-Atencia, Cristian; Rodriguez-Fernandez, Victor; Camacho, David
A multi-criteria decision support system for multi-UAV mission planning Book Section
In: Data Science and Knowledge Engineering for Sensing Decision Support, vol. 11, pp. 1083–1090, World Scientific, 2018, ISBN: 978-981-3273-22-1.
Abstract | Links | BibTeX | Tags: Decision Making, Planning, Unmanned Aircraft Systems
@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},
isbn = {978-981-3273-22-1},
year = {2018},
date = {2018-10-01},
booktitle = {Data Science and Knowledge Engineering for Sensing Decision Support},
volume = {11},
pages = {1083--1090},
publisher = {World Scientific},
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.},
keywords = {Decision Making, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {incollection}
}
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.
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.
Abstract | Links | BibTeX | Tags: Computer-based Simulation, Human-Machine Interaction, Planning, Unmanned Aircraft Systems
@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.},
keywords = {Computer-based Simulation, Human-Machine Interaction, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {article}
}
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.
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.
Bello-Orgaz, Gema; Ramirez-Atencia, Cristian; Fradera-Gil, Jaime; Camacho, David
GAMPP: Genetic Algorithm for UAV Mission Planning Problems Book Section
In: Intelligent Distributed Computing IX, pp. 167–176, Springer International Publishing, 2016.
Abstract | Links | BibTeX | Tags: Genetic Algorithms, 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 = {https://aida.etsisi.upm.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, 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 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}
}
Rodriguez-Fernandez, Victor; Ramirez-Atencia, Cristian; Camacho, David
A multi-UAV Mission Planning videogame-based framework for player analysis Proceedings Article
In: Evolutionary Computation (CEC), 2015 IEEE Congress on, pp. 1490–1497, IEEE 2015.
Abstract | Links | BibTeX | Tags: Multi-objective Optimization, Planning, 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 = {https://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 = {Multi-objective Optimization, Planning, 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.
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.
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}
}
2010
R-Moreno, Maria D; Camacho, David; Barrero, David F; Gutierrez, Miguel
A Decision Support System for Logistics Operations Proceedings Article
In: SOCO, pp. 103-110, 2010.
BibTeX | Tags: Multi-agents systems, Planning
@inproceedings{DBLP:conf/softcomp/R-MorenoCBG10,
title = {A Decision Support System for Logistics Operations},
author = {Maria D R-Moreno and David Camacho and David F Barrero and Miguel Gutierrez},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
booktitle = {SOCO},
pages = {103-110},
crossref = {DBLP:conf/softcomp/2010},
keywords = {Multi-agents systems, Planning},
pubstate = {published},
tppubtype = {inproceedings}
}
2008
R-Moreno, Maria D; Camacho, David; Obieta, Unai
A plan-based tool for automatic eLearning courses redesign Journal Article
In: IJCSA, vol. 5, no. 1, pp. 33-48, 2008.
BibTeX | Tags: Machine Learning, Planning
@article{DBLP:journals/ijcsa/Rodriguez-MorenoCO08,
title = {A plan-based tool for automatic eLearning courses redesign},
author = {Maria D R-Moreno and David Camacho and Unai Obieta},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
journal = {IJCSA},
volume = {5},
number = {1},
pages = {33-48},
keywords = {Machine Learning, Planning},
pubstate = {published},
tppubtype = {article}
}
2007
Camacho, David; R-Moreno, Maria D; Obieta, Unai
CAMOU: A Simple Integrated eLearning and Planning Techniques Tool Proceedings Article
In: ReTIeL, 2007.
BibTeX | Tags: eLearning, Planning
@inproceedings{DBLP:conf/context/CamachoRO07,
title = {CAMOU: A Simple Integrated eLearning and Planning Techniques Tool},
author = {David Camacho and Maria D R-Moreno and Unai Obieta},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
booktitle = {ReTIeL},
crossref = {DBLP:conf/context/2007retiel},
keywords = {eLearning, Planning},
pubstate = {published},
tppubtype = {inproceedings}
}
2003
Camacho, David
Coordination of Planning Agents to Solve Problems in the Web Journal Article
In: AI Communications, The European Journal on Artificial Intelligence. Ed. by IOS Press., vol. 16, no. 4, pp. 309-311, 2003.
BibTeX | Tags: Multi-agents systems, Planning, Web Services
@article{camacho:aicomm03,
title = {Coordination of Planning Agents to Solve Problems in the Web},
author = {David Camacho},
year = {2003},
date = {2003-12-08},
journal = {AI Communications, The European Journal on Artificial Intelligence. Ed. by IOS Press.},
volume = {16},
number = {4},
pages = {309-311},
keywords = {Multi-agents systems, Planning, Web Services},
pubstate = {published},
tppubtype = {article}
}
2002
Camacho, David; Aler, Ricardo; Borrajo, Daniel; Molina, José M
A Cooperative Planning Algorithm to Improve Performance in Web Domains Proceedings Article
In: Proceedings of the IEEE Systems, Man, and Cybernectics Conference (SMC-2002), IEEE Hammamet, Tunisia, 2002.
BibTeX | Tags: Planning, Web Services
@inproceedings{camacho:smc02a,
title = {A Cooperative Planning Algorithm to Improve Performance in Web Domains},
author = {David Camacho and Ricardo Aler and Daniel Borrajo and José M Molina},
year = {2002},
date = {2002-12-17},
booktitle = {Proceedings of the IEEE Systems, Man, and Cybernectics Conference (SMC-2002)},
address = {Hammamet, Tunisia},
organization = {IEEE},
keywords = {Planning, Web Services},
pubstate = {published},
tppubtype = {inproceedings}
}
Camacho, David; Molina, José M; Borrajo, Daniel; Aler, Ricardo
MAPWEB: Cooperation between Planning Agents and Web Agents Journal Article
In: Information & Security: An International Journal. Special issue on Agent-based Technologies, vol. 8, no. 2, pp. 209–238, 2002.
BibTeX | Tags: Multi-agents systems, Planning, Semantic Web
@article{camacho:mapweb,
title = {MAPWEB: Cooperation between Planning Agents and Web Agents},
author = {David Camacho and José M Molina and Daniel Borrajo and Ricardo Aler},
year = {2002},
date = {2002-01-01},
urldate = {2002-01-01},
journal = {Information & Security: An International Journal. Special issue on Agent-based Technologies},
volume = {8},
number = {2},
pages = {209--238},
keywords = {Multi-agents systems, Planning, Semantic Web},
pubstate = {published},
tppubtype = {article}
}
Camacho, David; Molina, José M; Borrajo, Daniel; Aler, Ricardo
Solving Travel Problems by Integrating WEB Information with Planning Proceedings Article
In: ISMIS, pp. 482-490, 2002.
BibTeX | Tags: Data Mining, Planning
@inproceedings{DBLP:conf/ismis/CamachoMBA02,
title = {Solving Travel Problems by Integrating WEB Information with Planning},
author = {David Camacho and José M Molina and Daniel Borrajo and Ricardo Aler},
year = {2002},
date = {2002-01-01},
booktitle = {ISMIS},
pages = {482-490},
crossref = {DBLP:conf/ismis/2002},
keywords = {Data Mining, Planning},
pubstate = {published},
tppubtype = {inproceedings}
}
2001
Camacho, David
Coordinación de Agentes de Planificación para la Resolución de Problemas en Web PhD Thesis
Departamento de Informática. Universidad Carlos III de Madrid, 2001.
BibTeX | Tags: Multi-agents systems, Planning, Web Services
@phdthesis{camacho:tesis,
title = {Coordinación de Agentes de Planificación para la Resolución de Problemas en Web},
author = {David Camacho},
year = {2001},
date = {2001-12-06},
urldate = {2001-12-06},
school = {Departamento de Informática. Universidad Carlos III de Madrid},
keywords = {Multi-agents systems, Planning, Web Services},
pubstate = {published},
tppubtype = {phdthesis}
}
Camacho, David; Borrajo, Daniel; Molina, José M; Aler, Ricardo
Abstract Planning in Dynamic Environments Proceedings Article
In: Proceedings of the IEEE Systems, Man, and Cybernectics Conference (SMC-2001), IEEE Tucson (AZ), USA, 2001.
@inproceedings{camacho:smc01a,
title = {Abstract Planning in Dynamic Environments},
author = {David Camacho and Daniel Borrajo and José M Molina and Ricardo Aler},
year = {2001},
date = {2001-12-04},
booktitle = {Proceedings of the IEEE Systems, Man, and Cybernectics Conference (SMC-2001)},
address = {Tucson (AZ), USA},
organization = {IEEE},
keywords = {Planning},
pubstate = {published},
tppubtype = {inproceedings}
}
Camacho, David; Borrajo, Daniel; Molina, José M; Aler, Ricardo
Flexible Integration of Planning and Information Gathering Proceedings Article
In: Proceedings of the European Conference on Planning (ECP-01), Springer-Verlag. Series LNAI, Toledo, Spain, 2001.
BibTeX | Tags: Information Extraction, Planning
@inproceedings{camacho:ecp2001,
title = {Flexible Integration of Planning and Information Gathering},
author = {David Camacho and Daniel Borrajo and José M Molina and Ricardo Aler},
year = {2001},
date = {2001-12-04},
urldate = {2001-12-04},
booktitle = {Proceedings of the European Conference on Planning (ECP-01)},
publisher = {Springer-Verlag. Series LNAI},
address = {Toledo, Spain},
keywords = {Information Extraction, Planning},
pubstate = {published},
tppubtype = {inproceedings}
}
Camacho, David; Borrajo, Daniel; Molina, José M
Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain Journal Article
In: Autonomous Agents and Multi-Agent Systems, vol. 4, no. 4, pp. 387-392, 2001.
BibTeX | Tags: Multi-agents systems, Planning
@article{DBLP:journals/aamas/CamachoBM01,
title = {Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain},
author = {David Camacho and Daniel Borrajo and José M Molina},
year = {2001},
date = {2001-01-01},
journal = {Autonomous Agents and Multi-Agent Systems},
volume = {4},
number = {4},
pages = {387-392},
keywords = {Multi-agents systems, Planning},
pubstate = {published},
tppubtype = {article}
}
2000
Camacho, David; Molina, José M; Borrajo, Daniel
A Multiagent Approach for Electronic Travel Planning Proceedings Article
In: Proceedings of the Second International Bi-Conference Workshop on Agent-Oriented Information Systems (AOIS-2000), AAAI Austin, TX (USA), 2000.
BibTeX | Tags: Multi-agents systems, Planning
@inproceedings{camacho:aaai2000,
title = {A Multiagent Approach for Electronic Travel Planning},
author = {David Camacho and José M Molina and Daniel Borrajo},
year = {2000},
date = {2000-12-05},
urldate = {2000-12-05},
booktitle = {Proceedings of the Second International Bi-Conference Workshop on Agent-Oriented Information Systems (AOIS-2000)},
address = {Austin, TX (USA)},
organization = {AAAI},
keywords = {Multi-agents systems, Planning},
pubstate = {published},
tppubtype = {inproceedings}
}