Preliminary estimation of reliability quantification for unmanned aerial vehicle (UAV)

Abstract

Unmanned Aerial Vehicles (UAVs) have become increasingly relevant due to their versatility in various applications. However, their reliability is a key factor affecting their adoption, particularly in civil airspace. This paper presents a comprehensive risk assessment framework for UAVs operating in civil airspaces, integrating a fault tree analysis (FTA) method with Python programming to estimate their reliability and ground risk. The study considers factors such as population density, shelter factor, and probability of fatality from impact to estimate the ground risk posed by UAVs. The proposed methodology provides a preliminary estimation of UAV reliability and associated ground risk, but further research is needed to improve accuracy. Future work may explore the use of mapping technologies and machine learning algorithms to provide more precise and timely risk assessments. Safe and efficient integration of UAVs in civil airspace is possible with further research and development. Clearance processes by regulatory bodies may potentially be automated.