Knowledge of the physical properties of objects is a requirement to enable effective robotic grasping. Identifying the material from which the object is made, is one such physical property. Characteristics of the material can be retrieved using different sensors; vision-based, tactile based or sound based. Physical contact with materials using tactile sensors can enable the retrieval of detailed information about the material, i.e. compressibility, surface texture and thermal properties. This paper describes a system to classify a wide range of materials based on their thermal properties and surface texture. This system will work towards a combined system using both tactile sensing and vision based sensing. Following acquisition of data from a sophisticated tactile sensor, the system uses principal component analysis (PCA) to extract features from the data which are used to train a two stage Artificial Neural Network (ANN) to classify materials, first into groups and then as individual materials. The system is compared with human performance and the results demonstrate that the proposed system can almost performed as effectively as humans.
|Title of host publication||Unknown Host Publication|
|Number of pages||6|
|Publication status||Published (in print/issue) - 27 Aug 2014|
|Event||Irish Machine Vision and Image Processing 2014 - |
Duration: 27 Aug 2014 → …
|Conference||Irish Machine Vision and Image Processing 2014|
|Period||27/08/14 → …|