X-ray Image Generation for Robotic Radiography: a Case Study on Motion Blur in Drone-Based Wind Turbine Inspections

Abstract Robotic X-ray imaging systems enable autonomous inspection of the internal integrity of critical infrastructure. However, these systems often suffer from vibrations and unwanted movements that cause motion blur in the resulting radiographs. The impact of this motion blur is often unknown until the first prototype is available and even then requires extensive experimental testing to assess. In addition, tests involving radiation are time-consuming, demand specialized equipment, and pose inherent safety risks. In this work, we propose using X-ray simulation as a tool to complement and replace real images during the development of robotic inspection systems. Our method extends an existing X-ray simulation framework (gVirtualXray) to generate motion-blurred images from any type of motion, which are then validated against experimental data. The approach is applicable to various robotic systems and we demonstrate its use for a decoupled two-drone inspection system for wind turbine blades. This is one of the most demanding applications due to the high degree of freedom of the system components and relatively long exposure times. The simulator provides insights into the motion blur sensitivity of the design, helping among others, to pinpoint the most significant degrees of freedom that affect image quality. Finally, we highlight the potential of the simulator for early estimation of performance limits, generation of training datasets for machine learning algorithms, and optimization of system design without the need for physical prototypes. Both the datasets and the software implementation are publicly available.