Evolutionary Coordination System for Fixed-Wing Communications Unmanned Aerial Vehicles

Giagkos, Alexandros; Tuci, Elio; Wilson, M.; Charlesworth, Philip B.

doi:10.1007/978-3-319-10401-0_5

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…The system tolerates the error in predicted positions, 1 based not only on the effect of the delay in evolving solutions but also on the realistic fact that the mobiles might change direction unexpectedly. It is found that even when mobiles move with high speeds, this method allows the UAVs to pace their flying according to the mobility pattern of the supported mobiles (Giagkos et al 2014). The NCG requires that all the UAVs simultaneously decide on their next move by solving the same game, and synchronously move to their respective next locations corresponding to the unique NE of that game.…”

Section: Proposed Approaches To Coordinate the Uav Movementsmentioning

confidence: 99%

UAV flight coordination for communication networks: genetic algorithms versus game theory

et al. 2021

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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.

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Section: Proposed Approaches To Coordinate the Uav Movementsmentioning

confidence: 99%

UAV flight coordination for communication networks: genetic algorithms versus game theory

et al. 2021

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“…1a. For further details on computing slant range values, the reader is encouraged to consult Giagkos et al [8].…”

Section: Problem Descriptionmentioning

confidence: 99%

Network-Aware Genetic Algorithms for the Coordination of MALE UAV Networks

Giagkos

Wilson²,

Bancroft³

2021

Towards Autonomous Robotic Systems

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