Title :
Material classification based on thermal properties — A robot and human evaluation
Author :
Kerr, Emmett ; McGinnity, Thomas Martin ; Coleman, Sonya
Author_Institution :
Intell. Syst. Res. Centre, Univ. of Ulster, Derry, UK
Abstract :
The surface properties of an object and the environment in which it is located are important for robot grasping and manipulation. Physical contact with an object using tactile sensors can enable the retrieval of detailed information about the object, i.e. compressibility, surface texture and thermal properties. This paper describes a system that classifies materials based on their thermal properties alone, minimising the amount of manipulation required. Following acquisition of data from a sophisticated tactile sensor, the system uses an Artificial Neural Network (ANN) to classify materials based on representations of their thermal properties. The system was compared with human performance in the task of classifying materials and was found to perform better.
Keywords :
image classification; image retrieval; image texture; manipulators; neural nets; neurocontrollers; robot vision; tactile sensors; ANN; artificial neural network; compressibility; data acquisition; human evaluation; information retrieval; material classification; robot evaluation; robot grasping; robot manipulation; surface texture; tactile sensors; thermal properties; Accuracy; Artificial neural networks; Conductivity; Materials; Metals; Robot sensing systems; Thermal conductivity;
Conference_Titel :
Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on
Conference_Location :
Shenzhen
DOI :
10.1109/ROBIO.2013.6739602