Automatic Fracture Characterization Using Tactile and Proximity Optical Sensing - Palermo, Francesca; Konstantinova, Jelizaveta; Althoefer, Kaspar; Poslad, Stefan; Farkhatdinov, Ildar (Frontiers in Robotics and AI, 2020)
Remote characterization of the environment during physical robot-environment interaction is an important task more often accomplished in telerobotics. This paper demonstrates how tactile and proximity sensing can be efficiently used to perform automatic crack detection. A custom-designed integrated tactile and proximity sensor is implemented, to measure the deformation of its body when interacting with the physical environment and distance to the environment's objects with the help of fiber optics. This sensor was used to slide across different surfaces to record data which was used to detect and classify cracks. The proposed method implements machine learning techniques (Raw data, Mean Absolute Value and Root Mean Square features and Random Forest, KNN and QDA Classifiers) to detect fractures and determine their width. An average crack detection accuracy of 94% and width classification accuracy of 80% is achieved. Kruskal-Wallis results (p<0.001) indicate statistically significant differences among results obtained when analysing only force data, only proximity data and both force and proximity data. A real-time classification method is 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 is suitable for operation in extreme environments (such as nuclear facilities) where nuclear radiation damages electronic components of commonly employed sensing devices, such as standard force sensors based on strain gauges and video cameras.