UAV Flight Coordination for Communication Networks: Genetic Algorithms versus Game Theory

Giagkos Alexandros, Elio Tuci, Myra Wilson, Phil Charlesworth

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The autonomous coordinated flying for groups of unmanned aerial vehicles that maximise network coverage to mobile ground-based units by efficiently utilising the available on-board power is a complex problem. Their coordination involves the fulfilment of multiple objectives that are directly dependent on dynamic, unpredictable and uncontrollable phenomena. In this paper, two systems are presented and compared based on their ability to reposition fixed-wing unmanned aerial vehicles to maintain a useful airborne wireless network topology. Genetic algorithms and non-cooperative games are employed for the generation of optimal flying solutions. The two methods consider realistic kinematics for hydrocarbon-powered medium-altitude, long-endurance aircrafts. Coupled with a communication model that addresses environmental conditions, they optimise flying to maximising the number of supported ground-based units. Results of large-scale scenarios highlight the ability of genetic algorithms to evolve flexible sets of manoeuvres that keep the flying vehicles separated and provide optimal solutions over shorter settling times. In comparison, game theory is found to identify strategies of predefined manoeuvres that maximise coverage but require more time to converge.
langue originaleAnglais
Pages (de - à)9483-9503
journalSoft Computing
Date de mise en ligne précoce15 mai 2021
Les DOIs
Etat de la publicationPublié - juil. 2021

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