@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.
@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.
