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On the Deployment Optimization of Se...

Kopanon, Andrew M.

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On the Deployment Optimization of Sensors and Coverage Path Planning for Drone Inspections.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On the Deployment Optimization of Sensors and Coverage Path Planning for Drone Inspections./
作者:	Kopanon, Andrew M.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	114 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31295345
ISBN:	9798382838076

On the Deployment Optimization of Sensors and Coverage Path Planning for Drone Inspections.
Kopanon, Andrew M.

On the Deployment Optimization of Sensors and Coverage Path Planning for Drone Inspections. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 114 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2024.

In an era marked by rapid technological advancement, the integration of sensor networks and drone technologies has significantly transformed surveillance, monitoring, and infrastructure maintenance practices. Maximizing sensor coverage and employing drones for inspection purposes emerge as pivotal areas of research and development, carrying far-reaching significance for defense, security, environmental surveillance, and infrastructure upkeep. This work presents a new collaborative sensor coverage maximization problem, a methodology for drone inspection mission coverage path planning, and an end-to-end software suite to plan drone inspection missions utilizing a large language model (LLM).Firstly, the Sensor Coverage Maximization Problem with Collaborative Coverage (SCMP-CC) is introduced to strategically deploy heterogeneous sensors in a given area to maximize coverage in an area of interest. The SCMP-CC utilizes a collaborative reward function considering sensor types, coverage capabilities, prioritized regions, and obstacles. A non-linear program was developed to model the problem, and a simulated annealing heuristic is employed to solve the problem. A specific case of the SCMP-CC was formulated as a linear model to assess the heuristic's effectiveness. The results demonstrate the heuristic's scalability and efficiency, solving large-scale problems within approximately 10 minutes.Secondly, the challenge of optimizing complete coverage boustrophedonic (lawn-mower pattern) drone routes and image collection points for building inspections is addressed, particularly accommodating complex structures with insets or protrusions while adhering to photogrammetric constraints. A Mixed Integer Program Approximation (MIPA) and a continuous space Complete Coverage Boustrophedonic (CCB) model are proposed, with the latter significantly outperforming in terms of computational speed without comprising solution quality. An end-to-end drone inspection pipeline is developed to utilize a pre-site survey image to map the insets and protrusions of the facade, and generate a route and image collection point for complete coverage in real space.Finally, this end-to-end pipeline was implemented in an open-source software suite that utilizes a LLM to aid in the mission planning and inspection route generation process. This Drone-Inspection Mission Planner (DMP) is hosted on a web-based Cesium platform, integrating OpenStreetMap (OSM) Buildings database for easy access to building dimensional data, and Google Photorealistic Tiles for life-like pre-site survey images. The LLM is used to facilitate interactive, multi-modal path planning, achieving a 93% accuracy in mission parameter handling. The DMP serves as an essential component within a broader drone command and control architecture, paving the way for further LLM integration and expanded utility in real-world inspection missions.

ISBN: 9798382838076Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Coverage

On the Deployment Optimization of Sensors and Coverage Path Planning for Drone Inspections.
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500 $a Advisor: Murray, Chase.
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520 $a In an era marked by rapid technological advancement, the integration of sensor networks and drone technologies has significantly transformed surveillance, monitoring, and infrastructure maintenance practices. Maximizing sensor coverage and employing drones for inspection purposes emerge as pivotal areas of research and development, carrying far-reaching significance for defense, security, environmental surveillance, and infrastructure upkeep. This work presents a new collaborative sensor coverage maximization problem, a methodology for drone inspection mission coverage path planning, and an end-to-end software suite to plan drone inspection missions utilizing a large language model (LLM).Firstly, the Sensor Coverage Maximization Problem with Collaborative Coverage (SCMP-CC) is introduced to strategically deploy heterogeneous sensors in a given area to maximize coverage in an area of interest. The SCMP-CC utilizes a collaborative reward function considering sensor types, coverage capabilities, prioritized regions, and obstacles. A non-linear program was developed to model the problem, and a simulated annealing heuristic is employed to solve the problem. A specific case of the SCMP-CC was formulated as a linear model to assess the heuristic's effectiveness. The results demonstrate the heuristic's scalability and efficiency, solving large-scale problems within approximately 10 minutes.Secondly, the challenge of optimizing complete coverage boustrophedonic (lawn-mower pattern) drone routes and image collection points for building inspections is addressed, particularly accommodating complex structures with insets or protrusions while adhering to photogrammetric constraints. A Mixed Integer Program Approximation (MIPA) and a continuous space Complete Coverage Boustrophedonic (CCB) model are proposed, with the latter significantly outperforming in terms of computational speed without comprising solution quality. An end-to-end drone inspection pipeline is developed to utilize a pre-site survey image to map the insets and protrusions of the facade, and generate a route and image collection point for complete coverage in real space.Finally, this end-to-end pipeline was implemented in an open-source software suite that utilizes a LLM to aid in the mission planning and inspection route generation process. This Drone-Inspection Mission Planner (DMP) is hosted on a web-based Cesium platform, integrating OpenStreetMap (OSM) Buildings database for easy access to building dimensional data, and Google Photorealistic Tiles for life-like pre-site survey images. The LLM is used to facilitate interactive, multi-modal path planning, achieving a 93% accuracy in mission parameter handling. The DMP serves as an essential component within a broader drone command and control architecture, paving the way for further LLM integration and expanded utility in real-world inspection missions.
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653 $a Path planning
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