Optimal UAV flights for seeability and endurance in winds.

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Guo_Wei_December2010.pdf (523.3 KB)

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https://hdl.handle.net/11299/103263

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Optimal UAV flights for seeability and endurance in winds.

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Guo, Wei

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2010-12

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Thesis or Dissertation

Abstract

Video-equipped unmanned aerial vehicles (UAVs) are highly useful in missions of surveillance, monitoring, and sensing. In these flights, the UAVs must maintain the ability to see the targets as well as to save energy for prolonged endurance. This paper examines basic patterns of UAV flights that can maximize its ability to “see” targets or seeability and/or minimize power consumptions to assist UAV mission planning. In this paper, a point-mass model is used to describe UAV motions. A seeability model is established that peaks when the UAV is flying at a certain angle from the normal vector perpendicular to the surface of the target. UAV flights are formulated as nonlinear periodic optimal control problems. The performance indices are selected to maximize the average seeability, to minimize the average power consumption, or to achieve a balance of the two. Motion constraints due to UAV performance capabilities and safety are imposed. The effects of different levels of constant wind velocities are considered. These nonlinear optimal control problems are converted into parameter optimization for numerical solutions. Extensive numerical solutions are obtained for UAV level flights with constant airspeeds and variable airspeeds, as well as three-dimensional flights. Clear tradeoffs between maximum seeability and minimum power are established.

Keywords

Video-equipped unmanned aerial vehicles (UAVs)

Surveillace

UAV flights

Point-mass model

Airspeeds

Aerospace Engineering and Mechanics

Description

University of Minnesota M.S. thesis. December 2010. Major: Aerospace Engineering and Mechanics. Advisors: Yiyuan J. Zhao, William L. Garrard. 1 computer file (PDF); xii, 40 pages.

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Guo, Wei. (2010). Optimal UAV flights for seeability and endurance in winds.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/103263.

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