Constrained multi-objective optimization for multi-UAV planning
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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.
KeywordsUnmanned air vehicles Mission planning Multi-objective optimization Constraint satisfaction problems
This work has been supported by the next research projects: EphemeCH (TIN2014-56494-C4-4-P) and DeepBio (TIN2017-85727-C4-3-P) by Spanish Ministry of Economy and Competitivity (MINECO), both under the European Regional Development Fund FEDER, and by Airbus Defence & Space (FUAM-076914 and FUAM-076915). The authors would like to acknowledge the support obtained from Airbus Defence & Space, specially from Savier Open Innovation project members: José Insenser, César Castro, Gemma Blasco and Inés Moreno.
This study was funded by Spanish Ministry of Science and Education and Competitivity and European Regional Development Fund FEDER (TIN2014-56494-C4-4-P and TIN2017-85727-C4-3-P), Comunidad Autónoma de Madrid (CIBERDINE S2013/ICE-3095) and Airbus Defence & Space under Savier Project (FUAM-076915).
Compliance with ethical standards
Conflict of interest
The Authors: Cristian Ramirez-Atencia and David Camacho declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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