Illustration of Guided Waves Simulations

This joint project with Prof. Nico Declercq aims at developing a smart embeddable sensor for the detection of defects (mainly corrosion and cracks) in metal-plate structures using guided waves, opto-acoustics and machine learning. By the end of the project, the resulting system will be expected to be integrated onto a mobile tetherless robotic system, hence allowing structural inspection of ship hulls and storage tanks with unprecedented resolution. To this day, the individual technologies have been demonstrated on large-scale systems that cannot be deployed on an autonomous mobile system because of both their weight, volume and energy requirements, and the complexity of the signal interpretation. Bringing them together into a small-sized tetherless smart system is precisely the purpose of this research. This raises significant challenges requiring an integrated view of multiple fields of research.

Simultaneous Localization and Mapping

Determining the sensor position on a large structure is critical for a subsequent acoustic imaging task. It is envisioned that the boundaries of the individual metal plates may serve as structure-bound landmarks for this purpose. Thus, a SLAM framework has been investigated to recover simultaneously the robot trajectory and plate edges from ultrasonic echoes in a sonar-like pulse-echo sensing mode.

On-plate Simultaneous Localization and Mapping based on FastSLAM and beamforming

Propagation model calibration

As an inspection robot may operate in environmental conditions that may vary significantly, the automatic calibration of the wave propagation model must be conducted for accurate localization and mapping results. By leveraging the principles of optimal beamforming, it is possible to map the boundaries of a metal plate without prior knowledge of the propagation properties.

Learning the propagation model parameters by maximization of the focusing ability of beamforming

Ultrasonic occupancy grid mapping with a robotic platform

Ultrasonic measurements are integrated within the Occupancy Grid framework to construct more complete representations of the environment that can be useful for active-sensing, for example, and helps for interference rejection. The applicability of this strategy is successfully demonstrated with a real robotic platform.

Experimental robotic platform on a metal plate for ultrasonic localization and mapping, buidling an occupancy grid of a metal plate with ultrasonic measurements

Localization of tiny acoustic scatterers

The localization of through-the-thickness alteration of the material from pulse-echo measurements is challenging, due to the low signal intensity and multiplicity of the echoes. A promising solution would be to adaptively remove echoes due to reflections on plate edges (based on estimated sensor position and calibrated propagation model), so that the localization of tiny acoustic scatterers can become feasible.

Localization of a simulated defect after edge echo removal

Bibliography

Work realised by Othmane-Latif Ouabi.

  1. Chahine, G., Schroepfer, P., Ouabi, O.-L., & Pradalier, C. (2022). A magnetic crawler system for autonomous long-range inspection and maintenance on large structures. Sensors, 22(9), 3235. https://doi.org/10.3390/s22093235 PDF
  2. Ouabi, O.-L., Pomarede, P., Declercq, N. F., Zeghidour, N., Geist, M., & Pradalier, C. (2022). Learning the propagation properties of rectangular metal plates for Lamb wave-based mapping. Ultrasonics, 123, 106705. PDF
  3. Ouabi, O.-L., Pomarede, P., Geist, M., Declercq, N. F., & Pradalier, C. (2021). A fastslam approach integrating beamforming maps for ultrasound-based robotic inspection of metal structures. IEEE Robotics and Automation Letters, 6(2), 2908–2913. PDF
  4. Le Gentil, C., Ouabi, O.-L., Wu, L., Pradalier, C., & Vidal-Calleja, T. (2023). Accurate Gaussian-Process-based Distance Fields with Applications to Echolocation and Mapping. IEEE Robotics and Automation Letters. PDF
  5. Ouabi, O.-L., Ridani, A., Pomarede, P., Zeghidour, N., Declercq, N. F., Geist, M., & Pradalier, C. (2022). Combined Grid and Feature-based Mapping of Metal Structures with Ultrasonic Guided Waves. 2022 International Conference on Robotics and Automation (ICRA), 5056–5062. PDF
  6. Ouabi, O.-L., Pribić, R., & Olaru, S. (2021). Stochastic complex-valued neural networks for radar. 2020 28th European Signal Processing Conference (EUSIPCO), 1442–1446. PDF
  7. Ouabi, O.-L., Pomarede, P., Geist, M., Declercq, N. F., & Pradalier, C. (2021). Monte-carlo localization on metal plates based on ultrasonic guided waves. Experimental Robotics: The 17th International Symposium, 345–353. PDF
  8. Ridani, A., Ouabi, O.-L., Declercq, N. F., & Pradalier, C. (2021). On-plate autonomous exploration for an inspection robot using ultrasonic guided waves. 2021 European Conference on Mobile Robots (ECMR), 1–6. PDF
  9. Pradalier, C., Ouabi, O.-L., Pomarede, P., & Steckel, J. (2020). On-plate localization and mapping for an inspection robot using ultrasonic guided waves: A proof of concept. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5045–5050. PDF
  10. Pradalier, C., Ouabi, O.-L., Pomarede, P., & Steckel, J. (2020). On-plate localization and mapping for an inspection robot using ultrasonic guided waves: A proof of concept. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5045–5050. PDF