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Efficient UAV Fleet Operations for S...

Bahabry, Ahmed B.

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Efficient UAV Fleet Operations for Smart City Applications: A Generic Navigation and Scheduling Framework.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Efficient UAV Fleet Operations for Smart City Applications: A Generic Navigation and Scheduling Framework./
作者:	Bahabry, Ahmed B.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	108 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
標題:	Engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13886052
ISBN:	9781392240885

Efficient UAV Fleet Operations for Smart City Applications: A Generic Navigation and Scheduling Framework.
Bahabry, Ahmed B.

Efficient UAV Fleet Operations for Smart City Applications: A Generic Navigation and Scheduling Framework. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 108 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2019.

This item is not available from ProQuest Dissertations & Theses.

Multi-rotor Unmanned Aerial Vehicles (UAVs), also known as drones and commonly seen as flying Internet-of-things (IoT) devices, have witnessed a drastic usage increase in several smart city applications due to their three-dimensional (3D) mobility, flexibility, and low cost. Collectively, UAVs can be used to accomplish different short-term and long-term missions that require low altitude motion in urban areas. Therefore, there is a need to efficiently manage the operation of the fleet to leverage its use and maximize its application performances. However, the management of UAVs are subject to various challenges that handicap their operations, especially in urban areas, such as the limited storage capacity of their batteries, the risk of collision, and the trajectory planning issue. Consequently, there is a pressing need to design an effective and generic UAV scheduling and navigation solution addressing these challenges while meeting the smart city application requirements.In this dissertation, we propose several practical and low complexity techniques aiming at optimizing the operation of the fleet for different objectives such as space-time energy-efficient coverage, e.g., traffic monitoring of road networks, total flying time reduction, or fast flying delivery systems. In the first part of the dissertation, a proactive scheduling solution where multiple UAVs are employed to cover spatially and temporally distributed events in a given geographical area. The framework minimizes the number of used UAVs and their corresponding total energy consumption while guaranteeing the coverage of all the pre-planned events. The framework also considers the necessity to regularly send back the UAVs to a docking station for battery replenishment. Three iterative algorithms employing a dimensionality reduction technique are developed to find an effective UAV scheduling with a fast convergence time. In the second part, we propose to investigate the path routing problem of the fleet in the context of urban areas where obstacles with different heights exist. The objective is to find the best trajectories in this 3D environment while ensuring their collision-free navigation. The collision is prevented either by making a UAV statically hover to let its peer pass first, forcing it to fly at a different altitude, or completely changing its trajectory. Multiple charging stations are made available to allow UAVs to charge their batteries along their way to their respective destinations. Two heuristic algorithms are designed to accomplish the joint trajectory planning problem. In the last part of the thesis, we combine the scheduling and navigation frameworks into a single generalized one where the full operation of the fleet is jointly optimized. Our Simulations investigate the performance of our frameworks in realistic 3D maps and show that the designed heuristic approaches i) provide close performances to the optimal ones obtained using mixed integer linear programs, ii) achieve significant computational saving iii) are operational for large-scale scenarios, and iv) can be applicable to a variety of smart city use-cases.Author: Ahmed BahabryAdvisor(s): Yehia Massoud and Gregg VesonderDate: May 9th, 2019 Department: School of Systems and EnterprisesDegree: Doctor of Philosophy.

ISBN: 9781392240885Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Collision avoidance

Efficient UAV Fleet Operations for Smart City Applications: A Generic Navigation and Scheduling Framework.
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520 $a Multi-rotor Unmanned Aerial Vehicles (UAVs), also known as drones and commonly seen as flying Internet-of-things (IoT) devices, have witnessed a drastic usage increase in several smart city applications due to their three-dimensional (3D) mobility, flexibility, and low cost. Collectively, UAVs can be used to accomplish different short-term and long-term missions that require low altitude motion in urban areas. Therefore, there is a need to efficiently manage the operation of the fleet to leverage its use and maximize its application performances. However, the management of UAVs are subject to various challenges that handicap their operations, especially in urban areas, such as the limited storage capacity of their batteries, the risk of collision, and the trajectory planning issue. Consequently, there is a pressing need to design an effective and generic UAV scheduling and navigation solution addressing these challenges while meeting the smart city application requirements.In this dissertation, we propose several practical and low complexity techniques aiming at optimizing the operation of the fleet for different objectives such as space-time energy-efficient coverage, e.g., traffic monitoring of road networks, total flying time reduction, or fast flying delivery systems. In the first part of the dissertation, a proactive scheduling solution where multiple UAVs are employed to cover spatially and temporally distributed events in a given geographical area. The framework minimizes the number of used UAVs and their corresponding total energy consumption while guaranteeing the coverage of all the pre-planned events. The framework also considers the necessity to regularly send back the UAVs to a docking station for battery replenishment. Three iterative algorithms employing a dimensionality reduction technique are developed to find an effective UAV scheduling with a fast convergence time. In the second part, we propose to investigate the path routing problem of the fleet in the context of urban areas where obstacles with different heights exist. The objective is to find the best trajectories in this 3D environment while ensuring their collision-free navigation. The collision is prevented either by making a UAV statically hover to let its peer pass first, forcing it to fly at a different altitude, or completely changing its trajectory. Multiple charging stations are made available to allow UAVs to charge their batteries along their way to their respective destinations. Two heuristic algorithms are designed to accomplish the joint trajectory planning problem. In the last part of the thesis, we combine the scheduling and navigation frameworks into a single generalized one where the full operation of the fleet is jointly optimized. Our Simulations investigate the performance of our frameworks in realistic 3D maps and show that the designed heuristic approaches i) provide close performances to the optimal ones obtained using mixed integer linear programs, ii) achieve significant computational saving iii) are operational for large-scale scenarios, and iv) can be applicable to a variety of smart city use-cases.Author: Ahmed BahabryAdvisor(s): Yehia Massoud and Gregg VesonderDate: May 9th, 2019 Department: School of Systems and EnterprisesDegree: Doctor of Philosophy.
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