Résumé:
In an era marked by unprecedented technological advancements, the integration of machine learning, computer vision, and unmanned aerial vehicles has ushered in a new era of possibilities across various domains. The utilization of UAVs, commonly known as drones, has transcended the realm of recreational gadgets and has emerged as a transformative tool in fields ranging from surveillance and agriculture to disaster management. This work embarks on a journey into the intersection of these cutting-edgetechnologies, aiming to address a pressing issue of our times: wildfire detection and management.
Within this context, various proposed systems seek to enhance wildfire detection using drones. This work aims to further develop these systems by harnessing the potential of drone swarms and addressing the challenges posed by restricted outdoor drone utilization in our country. The research endeavors to create an indoor positioning system to replace GPS-based position estimation, facilitating the development process within indoor environments. Additionally, the thesis initiates an attempt to establish a two-drone leader-follower drone formation,representing a pivotal step towards the realization of swarm-based wildfire detection and management solutions.
