The ultrasonic mapping of plate-based facilities is an essential step towards the robotic inspection of large metal structures such as storage tanks or ship hulls. This work proposes a novel framework that exploits ultrasonic echoes to recover grid-based and feature-based spatial representations jointly. We aim to improve on a previous mapping method [1] subject to errors due to interference, and which provides plate geometry estimates without uncertainty assessment. The grid can represent, all along the mapping process, both areas identified as inside or outside the current plate and areas whose state is still unknown, making it is suitable e.g. for detecting a change of plate, or for use in a later active-sensing strategy. We also leverage the resulting spatial information to filter out candidate plate edges that are no longer relevant, mitigating the detrimental effect of interference. We test the approach in simulation, with acoustic data acquired manually and with a real robot. Results show that it is effective for building combined map representations and robust to echo misdetection, contrary to a more standard mapping approach.
This article presents an active-sensing strategy based on frontier exploration to enable the autonomous reconstruction of the geometry of a metal surface by a mobile robot relying on ultrasonic echoes. Such a strategy can be beneficial to the development of a fully autonomous robotic agent for the inspection of large metal structures such as storage tanks or ship hulls. The developed method relies on a grid map generated by detecting the first echo within the measurements referring to the closest edge to the sensor, and it employs a utility function that we define to balance travel cost and information gain using an estimation of the plate geometry obtained via beamforming. Next, the sensor is directed to the next best location. The developed method is evaluated in simulation and compared with multiple algorithms, essentially closest and random frontier point selection. Finally, an experiment using a mobile robot equipped with co-localized pair of transducers is used to validate the viability of the approach.
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