Intelligent control system for aerial vehicles using associations and knowledge‐based explainable artificial intelligence approaches

Abstract

This dissertation presents an innovative approach to the control and management of aerial vehicles through the development and integration of graph structures and Knowledge‐based Explainable Artificial Intelligence (AI) approaches. As urban air mobility becomes a tangible reality, the need for efficient, safe, and understandable aerial vehicle control systems has never been more critical. This research explores the potential of graph structures to enhance operational efficiency and safety for unmanned aerial vehicles (UAVs), drones, and flying cars within complex urban and suburban environments. The study begins with a comprehensive literature review that sets the stage by discussing the current state of aerial vehicles, public perceptions, and the dynamic field of aerial vehicle control systems. It then delves into the technological and conceptual foundation necessary for developing a sophisticated algorithm at the heart of the proposed solution, highlighting the selection of the technology stack, the design of the graphical airspace representation, and the development of the Graph Navigator algorithm. Through simulation and testing within a custom‐built environment in Unreal Engine 5, this dissertation validates the effectiveness of the proposed graphically structured control system. The findings demonstrate the system’s ability to manage dense aerial traffic efficiently, ensuring safety and reliability even as the number of vehicles increases. This work lays a robust groundwork for future research in the field of urban air mobility, emphasizing the need for solutions that accommodate increasing aerial traffic while addressing safety, efficiency, and public acceptance concerns. By combining graph structures with explainable AI, the research introduces a scalable, dynamic, and transparent approach to airspace control, marking a significant step forward in the quest for integrating aerial vehicles into daily life and urban landscapes. Ultimately, this dissertation contributes to the broader discourse on the future of transportation and the role of artificial intelligence in managing the complexities of next‐generation aerial mobility. It offers a novel perspective on the challenges and opportunities presented by the integration of aerial vehicles into urban environments, providing a pathway for further innovations in airspace navigation and traffic management.