Autonomous Drone-Based System for Multi-Sensor Aircraft Surface Inspection and Defect Detection

Abstract: Manual visual inspections are the standard for traditional aircraft maintenance; however, this method is time-consuming, hazardous to personnel, and prone to human error. This paper outlines the design and development of an autonomous robotic system to address these limitations. The proposed solution is a drone equipped with a multi-sensor inspection payload, including a high-resolution AI camera for visual inspection, a LiDAR sensor for 3D mapping of structural damage, and a thermal camera to detect anomalies such as ice or degraded materials. The physical design was created using CAD software, and aerodynamic performance was validated through flow simulations. Using a novel observable sensor fusion technique, a custom AI model was developed to fuse and analyze payload data, yielding greater analytical depth than single-sensor methods. The objective is to achieve a defect detection accuracy of at least 95% and reduce inspection time by 50%. While currently in the simulation phase, this work represents a significant step toward improving the safety, efficiency, and accuracy of aircraft maintenance.