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2017 |
Ramirez-Atencia, Cristian; R-Moreno, Maria D; Camacho, David Handling swarm of UAVs based on evolutionary multi-objective optimization Journal Article Progress in Artificial Intelligence, In Press , pp. 1–12, 2017, ISSN: 2192-6352. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Mission Planning, Multi-Objective Genetic Algorithm, Unmanned Aerial Vehicles @article{Ramirez-Atencia2017, title = {Handling swarm of UAVs based on evolutionary multi-objective optimization}, author = {Cristian Ramirez-Atencia and Maria D R-Moreno and David Camacho}, url = {http://link.springer.com/10.1007/s13748-017-0123-7}, doi = {10.1007/s13748-017-0123-7}, issn = {2192-6352}, year = {2017}, date = {2017-01-01}, journal = {Progress in Artificial Intelligence}, volume = {In Press}, pages = {1--12}, publisher = {Springer Berlin Heidelberg}, abstract = {The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.}, keywords = {Constraint Satisfaction Problems, Mission Planning, Multi-Objective Genetic Algorithm, Unmanned Aerial Vehicles}, pubstate = {published}, tppubtype = {article} } The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime. |
Ramirez-Atencia, Cristian; Mostaghim, Sanaz; Camacho, David A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems Inproceedings Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1216–1223, ACM, Berlin, Germany, 2017, ISBN: 978-1-4503-4920-8. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, evolutionary multi-objective optimization, knee point, Mission Planning, Multi-objective Optimization, UAVs @inproceedings{Ramirez-Atencia2017b, title = {A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems}, author = {Cristian Ramirez-Atencia and Sanaz Mostaghim and David Camacho}, url = {http://doi.acm.org/10.1145/3071178.3071319}, doi = {10.1145/3071178.3071319}, isbn = {978-1-4503-4920-8}, year = {2017}, date = {2017-01-01}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}, pages = {1216--1223}, publisher = {ACM}, address = {Berlin, Germany}, series = {GECCO '17}, abstract = {The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.}, keywords = {Constraint Satisfaction Problems, evolutionary multi-objective optimization, knee point, Mission Planning, Multi-objective Optimization, UAVs}, pubstate = {published}, tppubtype = {inproceedings} } The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance. |
2016 |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems Inproceedings XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016), pp. 305–314, 2016. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Mission Planning, Muli-UAV, Multi-Objective Genetic Algorithm, Multi-objective Optimization, NSGA2, Unmanned Aerial Vehicles @inproceedings{Ramirez-Atencia2016a, title = {A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2017/03/A-Weighted-Penalty-Fitness-for-a-Hybrid-MOGA-CSP-to-solve-Mission-Planning-Problems.pdf}, year = {2016}, date = {2016-01-01}, booktitle = {XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016)}, pages = {305--314}, abstract = {Unmanned Aerial Vehicles (UAVs) are currently booming due to their high number of potential applications. In Mission Planning problems, several tasks must be performed by a team of UAVs, under the supervision of one or more Ground Control Stations (GCSs). In our approach, we have modelled the problem as a Constraint Satisfaction Problem (CSP), and solved it using a Multi-Objective Genetic Algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission such as the fuel consumption or the makespan. In addition, the fitness function takes a new consideration when solutions are not valid. It uses the number of constraints fulfilled for each solution as a weighted penalty function. In this way, the number of constraints fulfilled is maximized in the elitism phase of the MOGA. Results show that the approach outperforms the convergence with respect to previous results.}, keywords = {Constraint Satisfaction Problems, Mission Planning, Muli-UAV, Multi-Objective Genetic Algorithm, Multi-objective Optimization, NSGA2, Unmanned Aerial Vehicles}, pubstate = {published}, tppubtype = {inproceedings} } Unmanned Aerial Vehicles (UAVs) are currently booming due to their high number of potential applications. In Mission Planning problems, several tasks must be performed by a team of UAVs, under the supervision of one or more Ground Control Stations (GCSs). In our approach, we have modelled the problem as a Constraint Satisfaction Problem (CSP), and solved it using a Multi-Objective Genetic Algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission such as the fuel consumption or the makespan. In addition, the fitness function takes a new consideration when solutions are not valid. It uses the number of constraints fulfilled for each solution as a weighted penalty function. In this way, the number of constraints fulfilled is maximized in the elitism phase of the MOGA. Results show that the approach outperforms the convergence with respect to previous results. |
Bello-Orgaz, Gema; Ramirez-Atencia, Cristian; Fradera-Gil, Jaime; Camacho, David GAMPP: Genetic Algorithm for UAV Mission Planning Problems Incollection Intelligent Distributed Computing IX, pp. 167–176, Springer International Publishing, 2016. Abstract | Links | BibTeX | Tags: Genetic Algorithms, Mission Planning, Unmanned Aircraft Systems @incollection{bello2016gampp, title = {GAMPP: Genetic Algorithm for UAV Mission Planning Problems}, author = {Gema Bello-Orgaz and Cristian Ramirez-Atencia and Jaime Fradera-Gil and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2015/11/IDC15_BelloOrgazEtAl.pdf}, year = {2016}, date = {2016-01-01}, booktitle = {Intelligent Distributed Computing IX}, pages = {167--176}, publisher = {Springer International Publishing}, abstract = {Due to the rapid development of the UAVs capabilities, these are being incorporated into many fields to perform increasingly complex tasks. Some of these tasks are becoming very important because they involve a high risk to the vehicle driver, such as detecting forest fires or rescue tasks, while using UAVs avoids risking human lives. Recent researches on artificial intelligence techniques applied to these systems provide a new degree of high-level autonomy of them. Mission planning for teams of UAVs can be defined as the planning process of locations to visit (waypoints) and the vehicle actions to do (loading/dropping a load, taking videos/pictures, acquiring information), typically over a time period. Currently, UAVs are controlled remotely by human operators from ground control stations, or use rudimentary systems. This paper presents a new Genetic Algorithm for solving Mission Planning Problems (GAMPP) using a cooperative team of UAVs. The fitness function has been designed combining several measures to look for optimal solutions minimizing the fuel consumption and the mission time (or makespan). The algorithm has been experimentally tested through several missions where its complexity is incrementally modified to measure the scalability of the problem. Experimental results show that the new algorithm is able to obtain good solutions improving the runtime of a previous approach based on CSPs.}, keywords = {Genetic Algorithms, Mission Planning, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {incollection} } Due to the rapid development of the UAVs capabilities, these are being incorporated into many fields to perform increasingly complex tasks. Some of these tasks are becoming very important because they involve a high risk to the vehicle driver, such as detecting forest fires or rescue tasks, while using UAVs avoids risking human lives. Recent researches on artificial intelligence techniques applied to these systems provide a new degree of high-level autonomy of them. Mission planning for teams of UAVs can be defined as the planning process of locations to visit (waypoints) and the vehicle actions to do (loading/dropping a load, taking videos/pictures, acquiring information), typically over a time period. Currently, UAVs are controlled remotely by human operators from ground control stations, or use rudimentary systems. This paper presents a new Genetic Algorithm for solving Mission Planning Problems (GAMPP) using a cooperative team of UAVs. The fitness function has been designed combining several measures to look for optimal solutions minimizing the fuel consumption and the mission time (or makespan). The algorithm has been experimentally tested through several missions where its complexity is incrementally modified to measure the scalability of the problem. Experimental results show that the new algorithm is able to obtain good solutions improving the runtime of a previous approach based on CSPs. |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning Inproceedings 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016, ISBN: 978-1-5090-4240-1, 978-1-5090-4241-8. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Metaheuristics, Mission Planning, Multi-objective Optimization, NSGA2, Replanning, Unmanned Aircraft Systems @inproceedings{Ramirez-Atencia2016b, title = {MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho}, doi = {10.1109/SSCI.2016.7850235}, isbn = {978-1-5090-4240-1, 978-1-5090-4241-8}, year = {2016}, date = {2016-01-01}, booktitle = {2016 IEEE Symposium Series on Computational Intelligence (SSCI)}, abstract = {From the last few years the interest and repercussion on Unmanned Aerial Vehicle (UAV) technologies have been extended from pure military applications to industrial and societal applications. One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of MultiUAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to realtime Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an offlline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered.}, keywords = {Constraint Satisfaction Problems, Metaheuristics, Mission Planning, Multi-objective Optimization, NSGA2, Replanning, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } From the last few years the interest and repercussion on Unmanned Aerial Vehicle (UAV) technologies have been extended from pure military applications to industrial and societal applications. One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of MultiUAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to realtime Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an offlline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered. |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms Journal Article Soft Computing, In Press , pp. 1–18, 2016, ISSN: 1432-7643; 1433-7479. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, NSGA2, Unmanned air vehicles @article{Ramirez-Atencia2016c, title = {Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2017/03/RamirezEtAl.pdf}, doi = {10.1007/s00500-016-2376-7}, issn = {1432-7643; 1433-7479}, year = {2016}, date = {2016-01-01}, journal = {Soft Computing}, volume = {In Press}, pages = {1--18}, publisher = {Springer Verlag}, abstract = {Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find.}, keywords = {Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, NSGA2, Unmanned air vehicles}, pubstate = {published}, tppubtype = {article} } Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find. |
2015 |
RodrÍguez-Fernández, Víctor; Ramirez-Atencia, Cristian; Camacho, David A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game Inproceedings 2st Congreso de la Sociedad Española para las Ciencias del Videojuego, pp. 186–191, 2015. Abstract | Links | BibTeX | Tags: Mission Planning, Muli-UAV, Multi-objective Optimization, Player Assessment, Serious Games, Unmanned Aircraft Systems @inproceedings{rodriguezsummary, title = {A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game}, author = {Víctor RodrÍguez-Fernández and Cristian Ramirez-Atencia and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2015/09/rodriguezsummary.pdf}, year = {2015}, date = {2015-07-31}, booktitle = {2st Congreso de la Sociedad Española para las Ciencias del Videojuego}, journal = {2st Congreso de la Sociedad Española para las Ciencias del Videojuego}, volume = {1394}, pages = {186--191}, abstract = {Mission Planning for a large number of Unmanned Aerial Vehicles (UAVs) involves a set of locations to visit in different time intervals, and the actions that a vehicle must perform depending on its features and sensors. Analyzing how humans solve this problem is sometimes hard due to the complexity of the problem and the lack of data available. This paper presents a summary of a serious videogame-based framework created to assess the quality of the mission plans designed by players, comparing them against the optimal solutions obtained by a Multi-Objective Optimization algorithm.}, keywords = {Mission Planning, Muli-UAV, Multi-objective Optimization, Player Assessment, Serious Games, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } Mission Planning for a large number of Unmanned Aerial Vehicles (UAVs) involves a set of locations to visit in different time intervals, and the actions that a vehicle must perform depending on its features and sensors. Analyzing how humans solve this problem is sometimes hard due to the complexity of the problem and the lack of data available. This paper presents a summary of a serious videogame-based framework created to assess the quality of the mission plans designed by players, comparing them against the optimal solutions obtained by a Multi-Objective Optimization algorithm. |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, María D; Camacho, David A Hybrid MOGA-CSP for Multi-UAV Mission Planning Inproceedings Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pp. 1205–1208, ACM 2015. Links | BibTeX | Tags: Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, Unmanned Aircraft Systems @inproceedings{ramirez2015hybrid, title = {A Hybrid MOGA-CSP for Multi-UAV Mission Planning}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and María D R-Moreno and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2015/09/ramirez-atenciaHybrid.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference}, pages = {1205--1208}, organization = {ACM}, keywords = {Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } |
Rodriguez-Fernandez, Victor; Ramirez-Atencia, Cristian; Camacho, David A multi-UAV Mission Planning videogame-based framework for player analysis Inproceedings Evolutionary Computation (CEC), 2015 IEEE Congress on, pp. 1490–1497, IEEE 2015. Abstract | Links | BibTeX | Tags: Mission Planning, Multi-objective Optimization, Player Assessment, Serious Games, Unmanned Aircraft Systems @inproceedings{rodriguez2015multi, title = {A multi-UAV Mission Planning videogame-based framework for player analysis}, author = {Victor Rodriguez-Fernandez and Cristian Ramirez-Atencia and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2015/09/07257064.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Evolutionary Computation (CEC), 2015 IEEE Congress on}, pages = {1490--1497}, organization = {IEEE}, abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner.}, keywords = {Mission Planning, Multi-objective Optimization, Player Assessment, Serious Games, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner. |
Ramírez-Atencia, Cristian; Orgaz, Gema Bello; Rodríguez-Moreno, María Dolores; Camacho, David Performance Evaluation of Multi-UAV Cooperative Mission Planning Models Inproceedings Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II, pp. 203–212, 2015. Abstract | Links | BibTeX | Tags: branch and bound, Constraint Satisfaction Problems, Mission Planning, Unmanned Aircraft Systems @inproceedings{DBLP:conf/iccci/Ramirez-Atencia15, title = {Performance Evaluation of Multi-UAV Cooperative Mission Planning Models}, author = {Cristian Ramírez-Atencia and Gema Bello Orgaz and María Dolores Rodríguez-Moreno and David Camacho}, url = {http://dx.doi.org/10.1007/978-3-319-24306-1_20 http://aida.ii.uam.es/wp-content/uploads/2015/09/ramirez-atenciaPerformance.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II}, pages = {203--212}, crossref = {DBLP:conf/iccci/2015-2}, abstract = {The Multi-UAV Cooperative Mission Planning Problem (MCMPP) is a complex problem which can be represented with a lower or higher level of complexity. In this paper we present a MCMPP which is modelled as a Constraint Satisfaction Problem (CSP) with 5 increasing levels of complexity. Each level adds additional variables and constraints to the problem. Using previous models, we solve the problem using a Branch and Bound search designed to minimize the fuel consumption and number of UAVs employed in the mission, and the results show how runtime increases as the level of complexity increases in most cases, as expected, but there are some cases where the opposite happens.}, keywords = {branch and bound, Constraint Satisfaction Problems, Mission Planning, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } The Multi-UAV Cooperative Mission Planning Problem (MCMPP) is a complex problem which can be represented with a lower or higher level of complexity. In this paper we present a MCMPP which is modelled as a Constraint Satisfaction Problem (CSP) with 5 increasing levels of complexity. Each level adds additional variables and constraints to the problem. Using previous models, we solve the problem using a Branch and Bound search designed to minimize the fuel consumption and number of UAVs employed in the mission, and the results show how runtime increases as the level of complexity increases in most cases, as expected, but there are some cases where the opposite happens. |
2014 |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; Camacho, David; others, Solving UAV Mission Planning based on Temporal Constaint Satisfaction Problem using Genetic Algorithms Miscellaneous Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014), 2014. Abstract | Links | BibTeX | Tags: Genetic Algorithms, Mission Planning, Temporal Constraint Satisfaction Problems, Unmanned Aircraft Systems @misc{ramirez2014solving, title = {Solving UAV Mission Planning based on Temporal Constaint Satisfaction Problem using Genetic Algorithms}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and David Camacho and others}, url = {http://aida.ii.uam.es/wp-content/uploads/2014/12/RamirezEtAl.pdf}, year = {2014}, date = {2014-09-12}, abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to visit in di erent time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. We study how this problem can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). This problem contains several constraints assuring UAVs are assigned to tasks they have enough characteristics to perform, and soft-constraints for optimizing the time and fuel spent in the process. Our goal is to implement this model and then try to solve it using Genetic Algorithms (GAs). For this purpose, we will carry out a mission simulation containing m UAVs with di erent sensors and characteristics located in di erent waypoints, and n requested tasks varying mission priorities. The GA will match the model constraints and use a multi-objective function in order to minimize the cost.}, howpublished = {Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014)}, keywords = {Genetic Algorithms, Mission Planning, Temporal Constraint Satisfaction Problems, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {misc} } The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to visit in di erent time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. We study how this problem can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). This problem contains several constraints assuring UAVs are assigned to tasks they have enough characteristics to perform, and soft-constraints for optimizing the time and fuel spent in the process. Our goal is to implement this model and then try to solve it using Genetic Algorithms (GAs). For this purpose, we will carry out a mission simulation containing m UAVs with di erent sensors and characteristics located in di erent waypoints, and n requested tasks varying mission priorities. The GA will match the model constraints and use a multi-objective function in order to minimize the cost. |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; Camacho, David; others, A simple CSP-based model for Unmanned Air Vehicle Mission Planning Inproceedings Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on, pp. 146–153, IEEE 2014. Abstract | Links | BibTeX | Tags: Backtracking, Mission Planning, Temporal Constraint Satisfaction Problems, Unmanned Aircraft Systems @inproceedings{ramirez2014simple, title = {A simple CSP-based model for Unmanned Air Vehicle Mission Planning}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and David Camacho and others}, url = {http://aida.ii.uam.es/wp-content/uploads/2014/12/Ramirez-AtenciaEtAl.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on}, pages = {146--153}, organization = {IEEE}, abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). It consists on a set of locations that should visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. In this paper, a temporal constraint model is implemented and tested by performing Backtracking search in several missions where its complexity has been incrementally modified. The experimental phase consists on two different phases. On the one hand, several mission simulations containing (n) UAVs using different sensors and characteristics located in different waypoints, and (m) requested tasks varying mission priorities have been carried out. On the other hand, the second experimental phase uses a backtracking algorithm to look through the whole solutions space to measure the scalability of the problem. This scalability has been measured as a relation between the number of tasks to be performed in the mission and the number of UAVs needed to perform it.}, keywords = {Backtracking, Mission Planning, Temporal Constraint Satisfaction Problems, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). It consists on a set of locations that should visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. In this paper, a temporal constraint model is implemented and tested by performing Backtracking search in several missions where its complexity has been incrementally modified. The experimental phase consists on two different phases. On the one hand, several mission simulations containing (n) UAVs using different sensors and characteristics located in different waypoints, and (m) requested tasks varying mission priorities have been carried out. On the other hand, the second experimental phase uses a backtracking algorithm to look through the whole solutions space to measure the scalability of the problem. This scalability has been measured as a relation between the number of tasks to be performed in the mission and the number of UAVs needed to perform it. |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David Branching to Find Feasible Solutions in Unmanned Air Vehicle Mission Planning Incollection Intelligent Data Engineering and Automated Learning--IDEAL 2014, 8669 , pp. 286–294, Springer International Publishing, 2014. Abstract | Links | BibTeX | Tags: branch and bound, Mission Planning, Temporal Constraint Satisfaction Problems, Unmanned Aircraft Systems @incollection{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}, url = {http://aida.ii.uam.es/wp-content/uploads/2014/12/Ramirez-AtenciaEtAl1.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {Intelligent Data Engineering and Automated Learning--IDEAL 2014}, volume = {8669}, pages = {286--294}, publisher = {Springer International Publishing}, abstract = {Mission Planning is a classical problem that has been traditionally studied in several cases from Robotics to Space missions. This kind of problems can be extremely difficult in real and dynamic scenarios. This paper provides a first analysis for mission planning to Unmanned Air Vehicles (UAVs), where sensors and other equipment of UAVs to perform a task are modelled based on Temporal Constraint Satisfaction Problems (TCSPs). In this model, a set of resources and temporal constraints are designed to represent the main characteristics (task time, fuel consumption, ...) of this kind of aircrafts. Using this simplified TCSP model, and a Branch and Bound (B&B) search algorithm, a set of feasible solutions will be found trying to minimize the fuel cost, flight time spent and the number of UAVs used in the mission. Finally, some experiments will be carried out to validate both the quality of the solutions found and the spent runtime to found them.}, keywords = {branch and bound, Mission Planning, Temporal Constraint Satisfaction Problems, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {incollection} } Mission Planning is a classical problem that has been traditionally studied in several cases from Robotics to Space missions. This kind of problems can be extremely difficult in real and dynamic scenarios. This paper provides a first analysis for mission planning to Unmanned Air Vehicles (UAVs), where sensors and other equipment of UAVs to perform a task are modelled based on Temporal Constraint Satisfaction Problems (TCSPs). In this model, a set of resources and temporal constraints are designed to represent the main characteristics (task time, fuel consumption, ...) of this kind of aircrafts. Using this simplified TCSP model, and a Branch and Bound (B&B) search algorithm, a set of feasible solutions will be found trying to minimize the fuel cost, flight time spent and the number of UAVs used in the mission. Finally, some experiments will be carried out to validate both the quality of the solutions found and the spent runtime to found them. |