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CLOUD PLATFORM FOR MULTI - UAV MANAGEMENT WITH AI - DRIVEN MODULES

2024-11-12Master thesis Restricted access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informáticapt_PT
dc.contributor.advisorPereira, António Manuel de Jesus
dc.contributor.advisorRamos, João Pedro Ferreira
dc.contributor.advisorCosta, Nuno Alexandre Ribeiro da
dc.contributor.authorMarques, Tomás Vieira da Graça
dc.date.accessioned2025-01-08T15:46:57Z
dc.date.embargo2027-11-12
dc.date.issued2024-11-12
dc.description.abstractGiven the increasing severity of wildfires worldwide, there is a pressing need to automate forest fire prevention and surveillance systems, ensuring rapid and efficient responses to potential threats. To prevent and improve response to these global fire incidents, we have developed a solution that integrates visible and infrared spectrum images captured by UAVs, overcoming the limitations of single-sensor imagery in challenging conditions such as low light, fog, or smoke. This dissertation presents a comprehensive solution leveraging Internet-of-Drones (IoD) and advanced machine learning techniques for efficient multi-UAV area mapping and forest surveillance. The IoD concept shifts drone control towards cloud-based multi-UAV solutions, exemplified by this work. We propose IoD CloudStation a cloud multi-UAV management solution that supports high-level command abstraction, modular scalable platform integration, secure, and real-time data exchange. IoD CloudStation incorporates a hybrid communication protocol optimizing connectivity between UAVs, gateway servers, and web clients, facilitating numerous applications including data logging, AR/VR, area mapping, and drone vision projects to work with the groundstation making it AI-driven. To enable computer vision from the UAV perspective, a 4-channel deep-learning computer vision model based on an enhanced version of the YOLOv5 architecture was developed, demonstrating significant improvements in precision and Mean Average Precision (mAP) metrics, especially in low-visibility scenarios. This work also presents an approach using Reinforcement Learning (RL) techniques to enable cooperative multi-UAV area mapping, enhancing mapping efficiency and increasing the overall quality of video feed prompted to the computer vision module. The flexibility of RL allows UAVs to respond on demand to changing mapping parameters, such as the number of UAVs and the size of the area to be mapped. This combination of modules, when integrated into the IoD CloudStation, enables real-time UAV monitoring, forest fire prevention, and surveillance.pt_PT
dc.identifier.tid203787048pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.8/10364
dc.language.isoengpt_PT
dc.relationDBoidS - Digital twin Boids fire prevention System
dc.subjectIncêndio florestal- deteçãopt_PT
dc.subjectVigilância florestalpt_PT
dc.subjectVeículo aéreo não tripuladopt_PT
dc.subjectIOD CloudStationpt_PT
dc.subjectRecolha de dadospt_PT
dc.subjectMapeamento de áreaspt_PT
dc.subjectComputação móvelpt_PT
dc.titleCLOUD PLATFORM FOR MULTI - UAV MANAGEMENT WITH AI - DRIVEN MODULESpt_PT
dc.typemaster thesis
dspace.entity.typePublication
oaire.awardTitleDBoidS - Digital twin Boids fire prevention System
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de Projetos IC&DT em Todos os Domínios Científicos/PTDC%2FCCI-COM%2F2416%2F2021/PT
oaire.fundingStreamConcurso de Projetos IC&DT em Todos os Domínios Científicos
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typemasterThesispt_PT
relation.isProjectOfPublication6ad11ec6-79a0-4e65-8dc6-a5215e236377
relation.isProjectOfPublication.latestForDiscovery6ad11ec6-79a0-4e65-8dc6-a5215e236377
thesis.degree.nameMestrado em Engenharia Informática - Computação Móvelpt_PT

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