"Francesca Palermo, Jelizaveta Konstantinova, Kaspar Althoefer, Stefan Poslad, Ildar Farkhatdinov, Implementing Tactile and Proximity Sensing for Crack Detection, in: 2020 IEEE International Conference on Robotics and Automation, Paris, France, 2020"
Remote characterisation of the environment during physical robot-environment interaction is an important task commonly 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.
It measures the deformation of its body when interacting with the physical environment and distance to the environment's objects with the help of fibre optics.
This sensor was used to slide across different surfaces and the data recorded during the experiments was used to detect and classify cracks, bumps and undulations.
The proposed method uses machine learning techniques (mean absolute value as feature and random forest as classifier) to detect cracks and determine their width.
An average crack detection accuracy of 86.46% and width classification accuracy of 57.30% is achieved.
In contrast to previous techniques, which mainly rely on visual modality, the proposed approach based on optical fibres is suitable for operation in extreme environments, such as nuclear facilities in which nuclear radiation may damage the electronic components of video cameras.
Human Augmentation & Interactive Robotics Lab (HAIR), The Centre for Advanced Robotics @ Queen Mary (ARQ), School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
#icra2020 #tactile #proximity #nuclearenvironment