Automatic Fracture Characterization Using Tactile and Proximity Optical Sensing

This paper demonstrates how tactile and proximity sensing can be used to perform automatic mechanical fractures detection (surface cracks). For this purpose, a custom-designed integrated tactile and proximity sensor has been implemented. With the help of fiber optics, the sensor measures the deformation of its body, when interacting with the physical environment, and the distance to the environment objects. This sensor slides across different surfaces and records data which are then analyzed to detect and classify fractures and other mechanical features. The proposed method implements machine learning techniques (handcrafted features, and state of the art classification algorithms). A real-time classification method has been implemented for online classification of explored surfaces. In contrast to previous techniques, which mainly rely on visual modality, the proposed approach based on optical fibers might be more suitable for operation in extreme environments (such as nuclear facilities) where radiation may damage electronic components of commonly employed sensing devices, such as standard force sensors based on strain gauges and video cameras.

Download complete paper here

Bibtext Citation:

@article{palermo2020automatic, title={Automatic Fracture Characterization Using Tactile and Proximity Optical Sensing}, author={Palermo, Francesca and Konstantinova, Jelizaveta and Althoefer, Kaspar and Poslad, Stefan and Farkhatdinov, Ildar}, journal={Frontiers in Robotics and AI}, volume={7}, year={2020}, publisher={Frontiers Media SA}}