Effective robotic grasping and manipulation requires knowledge about the surface properties of an object and the environment in which it is located. 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 used to classify a wide range of materials based on their thermal properties and surface texture. 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 an 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 performed better than humans by almost 10%.
|Title of host publication||Unknown Host Publication|
|Number of pages||6|
|Publication status||Published - 10 Dec 2014|
|Event||13th International Conference on Control, Automation, Robotics & Vision - Singapore|
Duration: 10 Dec 2014 → …
|Conference||13th International Conference on Control, Automation, Robotics & Vision|
|Period||10/12/14 → …|
Kerr, E., McGinnity, TM., & Coleman, SA. (2014). Material Classification based on Thermal and Surface Texture Properties Evaluated against Human Performance. In Unknown Host Publication (pp. 444-449). IEEE.