Name
Title | ||
|---|---|---|
Material classification based on thermal and surface texture properties evaluated against human performance |
Abstract | ||
|---|---|---|
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%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICARCV.2014.7064346 | Control Automation Robotics & Vision |
Keywords | Field | DocType |
feature extraction,manipulators,neurocontrollers,principal component analysis,tactile sensors,artificial neural network,data acquisition,effective robotic grasping,feature extraction,manipulation,material classification,principal component analysis,surface texture,tactile sensors,thermal property | Compressibility,Computer vision,Thermal,Material classification,Computer science,Artificial intelligence,Surface finish,Artificial neural network,Principal component analysis,Tactile sensor | Conference |
ISSN | Citations | PageRank |
2474-2953 | 3 | 0.41 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emmett Kerr | 1 | 25 | 4.10 |
| McGinnity, T.M. | 2 | 141 | 16.14 |
| Sonya Coleman | 3 | 216 | 36.84 |