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dc.contributor.advisorRoyset, Johannes O.
dc.contributor.authorNachmani, Gil.
dc.date.accessioned2012-03-14T17:37:26Z
dc.date.available2012-03-14T17:37:26Z
dc.date.issued2007-12
dc.identifier.urihttp://hdl.handle.net/10945/3150
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractFuel or battery consumption of unmanned aerial vehicles (UAVs) can be improved by utilizing or avoiding air currents. This thesis adopts a network modeling approach to formulate the problem of finding minimum energy flight paths. The relevant airspace is divided into small regions using a grid of nodes, inter-connected by arcs. A function, representing energy cost, is defined on every arc in terms of the solution of a constrained nonlinear program for the optimal local airspeed to fly in a given wind field. Then, shortest-path models are implemented on the network to find the optimal paths from an origin to a destination. Five models are studied and they correspond to cases of pre-planning of flight routes and dynamic updating of routes during the course of the flight. These models use three-dimensional grids of forecasted wind currents, produced by the Naval Research Laboratory's Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) with horizontal resolution of 1 km. One of the shortest-path models, a stochastic-dynamic model, assumes real-time measurement capabilities of the wind velocity in the vicinity of the UAV, through its GPS-INS system, and provides updated waypoints to follow after every measurement. For each model, the energy costs of the shortest-path solutions for 1000 randomized missions over a Nevada test site are simulated and compared to the energy costs of straight-line paths. For a 100 kg UAV, the dynamic model produces an average reduction of 15.1% in the energy consumption along 40 km long round trips, and an average reduction of 30.1% under windy conditions with average wind speeds larger than 15 m/s. A stochastic-dynamic model for maximum duration, solved using a heuristic algorithm, achieves an average increase of 32.2% in the flight duration for a 100 kg UAV.en_US
dc.description.urihttp://archive.org/details/minimumenergyfli109453150
dc.format.extentxxii, 61 p. ;en_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined
in Title 17, United States Code, Section 101. As such, it is in the
public domain, and under the provisions of Title 17, United States
Code, Section 105, is not copyrighted in the U.S.en_US
dc.subject.lcshAerodynamicsen_US
dc.subject.lcshWeather forecastingen_US
dc.subject.lcshDrone aircraften_US
dc.titleMinimum-energy flight paths for UAVs using mesoscale wind forecasts and approximate dynamic programmingen_US
dc.typeThesisen_US
dc.contributor.secondreaderJones, Kevin
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceIsrael Defense Forces author.en_US
dc.identifier.oclc191065381
etd.thesisdegree.nameM.S.en_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.verifiednoen_US


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