Browsing by Author "Nesmachnow, Sergio"

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    A parallel multilevel data decomposition algorithm for orientation estimation of unmanned aerial vehicles
    (Springer-Verlag Berlin Heidelberg, 2014) Paz, Claudio; Nesmachnow, Sergio; Toloza, Hugo
    Fast orientation estimation of unmanned aerial vehicles is important for maintain stable flight as well as to perform more complex task like obstacle avoidance, search, mapping, etc. The orientation estimation can be performed by means of the fusion of differentsensors like accelerometers, gyroscopes and magnetometers, however magnetometers suffer from high distortion in indoor flights, therefore information from cameras can be used as a replacement. This article presents a multilevel decomposition method to process images sent from an unmanned aerial vehicle to a ground station composed by an heterogeneous set of desktop computers. The multilevel decomposition is performed using an alter native hierarchy called Master/Taskmaster/Slaves in order to minimize the network latency. Results shows that using this hierarchy the speed of traditional Master/Slave can be doubled
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    Multiobjective evolutionary flight planning of autonomous unmanned aerial vehicles for exploration and surveillence
    (Universidad Tecnológica Nacional Regional Córdoba., 2020) Nesmachnow, Sergio; Paz, Claudio; Toutouh, Jamal; Tchernykh, Andrei
    This article presents a multiobjective evolutionary approach for computing flight plans for a fleet of unmanned aerial vehicles to perform exploration and surveillance missions. The static off-line planning subproblem is addressed, which is useful to determine initial flight routes to maximize the explored area and the surveillance of points of interest in the zone. A specific flight planning solution is developed, to be applied in low-cost commercial Bebop 2. The experimental analysis is performed in realistic instances of the surveillance problem. Results indicate that the proposed multiobjective evolutionary algorithm is able to compute accurate flight plans, significantly outperforming a previous evolutionary method applying the linear aggregation approach