A simple CSP-based model for unmanned air vehicle mission planning
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
Institute of Electrical and Electronics EngineersFecha de edición
2014Cita
10.1109/INISTA.2014.6873611
2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings. IEEE, 2014. 146 - 153
ISBN
978-1-4799-3019-7DOI
10.1109/INISTA.2014.6873611Financiado por
This work is supported by the Spanish Ministry of Science and Education under Project Code TIN2010-19872 and Savier Project (Airbus Defence & Space, FUAM-076915). The authors would like to acknowledge the support obtained from Airbus Defence & Space, specially from Savier Open Innovation project members: Jose Insenser, C ´ esar Castro and ´ Gemma Blasco.Versión del editor
http://dx.doi.org/10.1109/INISTA.2014.6873611Materias
Backtracking; Mission Planning; Temporal Constraint Satisfaction Problems; Unmanned Aircraft Systems; InformáticaNota
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. C. Ramírez-Atencia, G. Bello-Orgaz, M. D. R.-Moreno, and D. Camacho, "A simple CSP-based model for Unmanned Air Vehicle Mission Planning", in 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014, pp. 146 - 153Derechos
© 2014 IEEEResumen
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.
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Google Scholar:Ramírez Atencia, Cristian Oliver
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Bello Orgaz, Gema
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R-Moreno, María Dolores
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Camacho, David
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