Learning the Propagation Properties of Rectangular Metal Plates for Lamb Wave-based Mapping

The inspection of sizeable plate-based metal structures such as storage tanks or marine vessel hulls is a significant stake in the industry, which necessitates reliable and time-efficient solutions. Although Lamb waves have been identified as a promising solution for long-range non-destructive testing, and despite the substantial progress made in autonomous navigation and environment sensing, a Lamb-wave-based robotic system for extensive structure monitoring is still lacking. Following previous work on ultrasonic Simultaneous Localization and Mapping (SLAM), we introduce a method to achieve plate geometry inference without prior knowledge of the material propagation properties, which may be lacking during a practical inspection task in challenging and outdoor environments. Our approach combines focalization to adjust the propagation model parameters and beamforming to infer the plate boundaries location by relying directly on acoustic measurements acquired along the mobile unit trajectory. For each candidate model, the focusing ability of the corresponding beamformer is assessed over high-pass filtered beamforming maps to further improve the robustness of the plate geometry estimates. We then recover the optimal space-domain beamformer through a simulated annealing optimization process. We evaluate our method on three sets of experimental data acquired in different conditions and show that accurate plate geometry inference can be achieved without any prior propagation model. Finally, the results show that the optimal beamformer outperforms the beamformer resulting from the predetermined propagation model in non-nominal acquisition conditions.

• Robotics