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

Marques, Tomás Vieira da Graça

http://hdl.handle.net/10400.8/10364

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Authors

Marques, Tomás Vieira da Graça

Advisor(s)

Pereira, António Manuel de Jesus

Ramos, João Pedro Ferreira

Costa, Nuno Alexandre Ribeiro da

Abstract(s)

Given 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.