Enhancing target search efficiency of centralized and distributed intelligence in UAV swarm operations

This dissertation tackles the challenge of improving the efficiency of search and track maneuvers for static targets in multi-Unmanned Aerial Vehicle (UAV) systems. Key objectives include developing advanced control algorithms, implementing autonomous dynamics using the steering force method, integr...

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Main Author: Xu, Xiaotian
Other Authors: Chau Yuen
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
Subjects:
UAV
Online Access:https://hdl.handle.net/10356/175437
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1754372024-04-26T16:00:34Z Enhancing target search efficiency of centralized and distributed intelligence in UAV swarm operations Xu, Xiaotian Chau Yuen School of Electrical and Electronic Engineering chau.yuen@ntu.edu.sg Engineering UAV Swarm intelligence Tracking and seeking Centralized intelligence Distributed intelligence This dissertation tackles the challenge of improving the efficiency of search and track maneuvers for static targets in multi-Unmanned Aerial Vehicle (UAV) systems. Key objectives include developing advanced control algorithms, implementing autonomous dynamics using the steering force method, integrating collision prevention mechanisms via a potential field model, and adapting formation control in dynamic environments. Focused enhancements of the Rowscan algorithm, including Distributed Search (DS), Centralized Search (CS), Distributed Map-known Search (DMS), Centralized Map-known Search (CMS), and known-Drones’ Positions (DP) algorithms, are explored along with evaluating shared exploration areas, considering known map sizes, and establishing mutual orientation information among drones. The methodology combines distributed and centralized decision-making frameworks to empower UAVs with enhanced autonomy during search missions. Through rigorous algorithm design, simulation validation, and comparative analysis, this research demonstrates significant improvements in search efficiency and target localization. The findings contribute to robotics research, addressing contemporary challenges in multirobot and swarm systems with implications for search and rescue, environmental monitoring, and surveillance applications. Master's degree 2024-04-23T12:55:40Z 2024-04-23T12:55:40Z 2024 Thesis-Master by Coursework Xu, X. (2024). Enhancing target search efficiency of centralized and distributed intelligence in UAV swarm operations. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175437 https://hdl.handle.net/10356/175437 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
UAV
Swarm intelligence
Tracking and seeking
Centralized intelligence
Distributed intelligence
spellingShingle Engineering
UAV
Swarm intelligence
Tracking and seeking
Centralized intelligence
Distributed intelligence
Xu, Xiaotian
Enhancing target search efficiency of centralized and distributed intelligence in UAV swarm operations
description This dissertation tackles the challenge of improving the efficiency of search and track maneuvers for static targets in multi-Unmanned Aerial Vehicle (UAV) systems. Key objectives include developing advanced control algorithms, implementing autonomous dynamics using the steering force method, integrating collision prevention mechanisms via a potential field model, and adapting formation control in dynamic environments. Focused enhancements of the Rowscan algorithm, including Distributed Search (DS), Centralized Search (CS), Distributed Map-known Search (DMS), Centralized Map-known Search (CMS), and known-Drones’ Positions (DP) algorithms, are explored along with evaluating shared exploration areas, considering known map sizes, and establishing mutual orientation information among drones. The methodology combines distributed and centralized decision-making frameworks to empower UAVs with enhanced autonomy during search missions. Through rigorous algorithm design, simulation validation, and comparative analysis, this research demonstrates significant improvements in search efficiency and target localization. The findings contribute to robotics research, addressing contemporary challenges in multirobot and swarm systems with implications for search and rescue, environmental monitoring, and surveillance applications.
author2 Chau Yuen
author_facet Chau Yuen
Xu, Xiaotian
format Thesis-Master by Coursework
author Xu, Xiaotian
author_sort Xu, Xiaotian
title Enhancing target search efficiency of centralized and distributed intelligence in UAV swarm operations
title_short Enhancing target search efficiency of centralized and distributed intelligence in UAV swarm operations
title_full Enhancing target search efficiency of centralized and distributed intelligence in UAV swarm operations
title_fullStr Enhancing target search efficiency of centralized and distributed intelligence in UAV swarm operations
title_full_unstemmed Enhancing target search efficiency of centralized and distributed intelligence in UAV swarm operations
title_sort enhancing target search efficiency of centralized and distributed intelligence in uav swarm operations
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175437
_version_ 1800916398216577024