A Flight Time Approximation Model for Unmanned Aerial Vehicles: Estimating the Effects of Path Variations and Wind

Henchey, Matthew J.; Batta, Rajan; Karwan, Mark H.; Crassidis, Agamemnon L.

doi:10.5711/1082598319151

Cited by 1 publication

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“…Next, a directed edge from node j ik to node k is added and its weight is set equal to a Dubins’ factor for distance, which comes from approximating a minimum distance path for a Dubins’ flight model. 45 Dubins’ original work led to work by Schkel and Lumelsky, 46,47 which limited the number of calculations needed to determine the minimum distance path for a Dubins’ problem, and resulted in an efficient implementation of the Dubins’ curve path model. The accuracy of the Dubins’ curve path model has been demonstrated in the literature; 48 its accuracy and ease of implementation is why we choose the Dubins’ curve path model for approximating the actual flight time between point j and point k originally coming from point i .…”

Section: Network Developmentmentioning

confidence: 99%

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A real-time network approach for including obstacles and flight dynamics in UAV route planning

Myers

Batta

Karwan

2016

Journal of Defense Modeling & Simulation

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The procedure presented within considers the problem of calculating flight time for unmanned aerial vehicles (UAVs) while incorporating a subset of important flight dynamics characteristics in an operational field that contains obstacles. These flight time calculations are parameter inputs in mission planning and dynamic reassignment problems. The addition of pseudonodes and the addition of penalties into the associated edge weights are the basis for how the network generation procedure includes flight dynamics. The procedure includes a method for handling pop-up targets or obstacles in a dynamic reassignment problem. To guarantee that the optimal path includes flight dynamics, a selective Dijkstra's algorithm computes the shortest path. A complex mission plan consisting of thirty targets and three obstacles is the largest test scenario of nine developed scenarios. Our network generation procedure, along with the shortest path calculations of all 992 node pairs of interest, solves in approximately one second. This procedure allows for the fast computation needed to generate parameters for use in a dynamic domain such as mission planning and dynamic reassignment algorithms.

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Section: Network Developmentmentioning

confidence: 99%

“…The following six equations are used to define the Dubins’ curve path model required calculations. 45 Table 3 defines the nomenclature in the equations:…”

Section: Network Developmentmentioning

confidence: 99%