@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.
@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.
@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.
