國立虎尾科技大學 |

Altitude Selection Framework for UAV Swarm Efficiency in Winds.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Altitude Selection Framework for UAV Swarm Efficiency in Winds./
作者:	Johnston, D. Landon.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	48 p.
附註:	Source: Masters Abstracts International, Volume: 84-05.
Contained By:	Masters Abstracts International84-05.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29998706
ISBN:	9798357579898

Altitude Selection Framework for UAV Swarm Efficiency in Winds.
Johnston, D. Landon.

Altitude Selection Framework for UAV Swarm Efficiency in Winds. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 48 p.

Source: Masters Abstracts International, Volume: 84-05.

Thesis (M.S.)--Southeastern Louisiana University, 2022.

This item must not be sold to any third party vendors.

In this thesis we propose a UAV (unmanned aerial vehicle) swarm altitude selection algorithm intended to increase flight efficiency along routes with unknown wind conditions. To perform this research a simulation was developed that would calculate UAV and UAV swarm energy use in given wind conditions. The algorithm considers the energy consumption as well as the wind condition to calculate the best altitude to navigate. The energy consumption model considers the changes in airspeed required by each UAV to maintain position relative to the swarm in various head and crosswind conditions. Testing this novel solution in a simulated environment first provides the opportunity to get quicker, more cost-effective results to provide early insights to the potential efficiency improvements before committing to the heavier investment required to test real world swarm performance. The algorithm is designed to leverage a swarm’s ability to distribute work to sample available altitudes in search of favorable wind conditions. Favorable wind conditions in this context mean that a swarm would try to position itself in tail winds and avoid head winds relative to swarm heading.Performance metrics were generated from simulating groups of swarms utilizing the flight behavior being tested as well as groups of swarms that flew along the same route at a constant, random altitude; the performance of the swarm tested our framework was then compared to the control swarms. It was found that the approach of changing the swarm’s target elevation based on in-flight acquired wind data noticeably improved the swarm’s flight efficiency. It was also found that swarms using the test behavior almost alwaysoutperformed swarms flying at a fixed altitude for the entire route. Only relatively short routes saw comparable results in performance, though in route energy consumption concerns are understandably lessened for shorter travel distances. These results lead us to believe that the benefits of this algorithm cover a significant array of present and future swarm applications.

ISBN: 9798357579898Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Drones

Altitude Selection Framework for UAV Swarm Efficiency in Winds.
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500 $a Source: Masters Abstracts International, Volume: 84-05.
500 $a Advisor: Regis, Paulo A.
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506 $a This item must not be sold to any third party vendors.
520 $a In this thesis we propose a UAV (unmanned aerial vehicle) swarm altitude selection algorithm intended to increase flight efficiency along routes with unknown wind conditions. To perform this research a simulation was developed that would calculate UAV and UAV swarm energy use in given wind conditions. The algorithm considers the energy consumption as well as the wind condition to calculate the best altitude to navigate. The energy consumption model considers the changes in airspeed required by each UAV to maintain position relative to the swarm in various head and crosswind conditions. Testing this novel solution in a simulated environment first provides the opportunity to get quicker, more cost-effective results to provide early insights to the potential efficiency improvements before committing to the heavier investment required to test real world swarm performance. The algorithm is designed to leverage a swarm’s ability to distribute work to sample available altitudes in search of favorable wind conditions. Favorable wind conditions in this context mean that a swarm would try to position itself in tail winds and avoid head winds relative to swarm heading.Performance metrics were generated from simulating groups of swarms utilizing the flight behavior being tested as well as groups of swarms that flew along the same route at a constant, random altitude; the performance of the swarm tested our framework was then compared to the control swarms. It was found that the approach of changing the swarm’s target elevation based on in-flight acquired wind data noticeably improved the swarm’s flight efficiency. It was also found that swarms using the test behavior almost alwaysoutperformed swarms flying at a fixed altitude for the entire route. Only relatively short routes saw comparable results in performance, though in route energy consumption concerns are understandably lessened for shorter travel distances. These results lead us to believe that the benefits of this algorithm cover a significant array of present and future swarm applications.
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