Methodology for the Implementation of an Intelligent Drone: From Hardware to Technical Deployment

Objective: This study aims to enhance the reliability and performance of autonomous drones by integrating Failure Modes and Effects Analysis (FMEA) and axiomatic design principles. The research focuses on improving flight stability, obstacle avoidance, and overall operational efficiency in critical environments. Theoretical Framework: The study builds upon axiomatic design theory and FMEA methodology, widely used in engineering to optimize system reliability. Axiomatic design provides a structured approach for modularity and scalability, while FMEA systematically identifies and mitigates potential failures at early design stages. Method: A structured methodology combining axiomatic design and FMEA was applied to define Functional Requirements (FRs) and Design Parameters (DPs). The approach includes a digital twin model and real-world simulations to evaluate performance. Key Performance Indicators (KPIs) such as control efficiency and navigation accuracy were used to validate system effectiveness. Results and Discussion: The study confirms that integrating FMEA into early development phases enhances failure detection, reduces design costs, and improves system resilience. Axiomatic design ensures subsystem independence, facilitating scalability and operational adaptability. Additionally, the use of AI-based navigation algorithms proved effective in optimizing real-time obstacle avoidance. Research Implications: The proposed methodology can be applied across various UAV applications, including aerial surveillance, disaster response, and autonomous logistics. The study contributes to UAV research by providing a replicable framework for improving drone reliability and operational safety. Originality/Value: This research addresses a literature gap by systematically integrating FMEA and axiomatic design in drone development. The approach enhances reliability, modularity, and adaptability, offering a robust foundation for future autonomous UAV advancements.