X-ray Image Generation for Robotic Radiography: a Case Study on Motion Blur in Drone-Based Wind Turbine Inspections
Written by: Bas Meere, Sander Doodeman, Franck P. Vidal, Paula Chanfreut, Elena Torta and Duarte Antunes
Published on: Summary
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.
Award
Best paper award for tomography outreach tools - Vidal, F. P., Afshari, S., Ahmed, S., Atkins, C., Béchet, É., Corbí Bellot, A., ... Tugwell-Allsup, J. (2024). Müller, B., & Wang, G. (Eds.). "X-ray simulations with gVXR as a useful tool for education, data analysis, set-up of CT scans, and scanner development". Developments in X-Ray Tomography XV, SPIE Optics & Photonics 2024. DOI: 10.1117/12.3025315
Publications
- Vidal, F. P. et al (2024, Nov). X-ray simulations with gVXR as a useful tool for education, data analysis, set-up of CT scans, and scanner development. Developments in X-Ray Tomography XV, SPIE Optics & Photonics 2024. doi: 10.1117/12.3025315 .
- Vidal, F. P. et al (2025, Nov). X-ray simulations with gVXR in education, digital twining, experiment planning, and data analysis. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. doi: 10.1016/j.nimb.2025.165804 .
- Meere, B., Doodeman, S., Vidal, F. P., Chanfreut, P., Torta, E., & Antunes, D. (2025, Oct). X-ray Image Generation for Robotic Radiography: a Case Study on Motion Blur in Drone-Based Wind Turbine Inspections. Journal of Nondestructive Evaluation. doi: 10.1007/s10921-025-01279-6 .
Citations
@article{Meere2025JNDE,
author = {Bas Meere and Sander Doodeman and Franck P. Vidal and
Paula Chanfreut and Elena Torta and Duarte Antunes},
doi = {10.1007/s10921-025-01279-6},
issn = {1573-4862},
journal = {Journal of Nondestructive Evaluation},
keywords = {Non-destructive testing; Robotic Digital radiography;
X-ray simulator; Autonomous inspection; System Design; Wind turbine},
pages = {139},
title = {{X-ray Image Generation for Robotic Radiography: a Case Study on
Motion Blur in Drone-Based Wind Turbine Inspections}},
volume = {44},
year = {2025}
}
@article{Vidal2025NIMB,
author = {Vidal, Franck P. and Afshari, Shaghayegh and Ahmed, Sharif and
Albiol, Alberto and Albiol, Francisco and B{\'e}chet, {\'E}ric and
Bellot, Alberto Corb{\'\i} and Bosse, Stefan and Burkhard, Simon and
Chahid, Younes and Chou, Cheng-Ying and Culver, Robert and
Desbarats, Pascal and Dixon, Lewis and Friemann, Johan and
Garbout, Amin and Garc{\'\i}a-Lorenzo, Marcos and
Giovannelli, Jean-Fran{\c c}ois and Hanna, Ross and
Hatton, Cl{\'e}mentine and Henry, Audrey and Kelly, Graham and
Leblanc, Christophe and Leonardi, Alberto and L{\'e}tang, Jean Michel and
Lipscomb, Harry and Manchester, Tristan and Meere, Bas and
Michelet, Claire and Middleburgh, Simon and Mihail, Radu P. and
Mitchell, Iwan and Perera, Liam and Puig, Mart{\'\i} and Racy, Malek and
Rouwane, Ali and Seznec, Herv{\'e} and S{\'u}jar, Aaron and
Tugwell-Allsup, Jenna and Villard, Pierre-Fr{\'e}d{\'e}ric},
doi = {10.1016/j.nimb.2025.165804},
issn = {0168-583X},
journal = {Nuclear Instruments and Methods in Physics Research Section B:
Beam Interactions with Materials and Atoms},
keywords = {X-ray imaging; Computed tomography; Simulation;
GPU programming; Digital twinning; Registration; Machine learning},
pages = {165804},
title = {{X-ray simulations with gVXR in education, digital twining,
experiment planning, and data analysis}},
volume = 568,
year = 2025,
}
@inproceedings{Vidal2024SPIE,
author = {Franck P. Vidal and Shaghayegh Afshari and Sharif Ahmed and
Carolyn Atkins and {\'E}ric B{\'e}chet and Alberto Corb{\'i} Bellot and
Stefan Bosse and Younes Chahid and Cheng-Ying Chou and Robert Culver and
Lewis Dixon and Johan Friemann and Amin Garbout and Cl{\'e}mentine Hatton and
Audrey Henry and Christophe Leblanc and Alberto Leonardi and
Jean Michel L{\'e}tang and Harry Lipscom and Tristan Manchester and
Bas Meere and Simon Middleburgh and Iwan Mitchell and Liam Perera and
Mart{\'i} Puig and Jenna Tugwell-Allsup},
title = {{X-ray simulations with gVXR as a useful tool for education,
data analysis, set-up of CT scans, and scanner development}},
volume = {13152},
booktitle = {Developments in X-Ray Tomography XV},
editor = {Bert M{\"u}ller and Ge Wang},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {131520W},
keywords = {X-ray imaging, Computed tomography (CT), Simulation,
Digital twinning, Graphics processor unit (GPU) programming,
Image registration, Digitally reconstructed radiograph (DRR),
Machine learning},
year = {2024},
doi = {10.1117/12.3025315},
}