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Material Classification based on Thermal and Surface Texture Properties Evaluated against Human Performance

Kerr, Emmett, McGinnity, TM and Coleman, SA (2014) Material Classification based on Thermal and Surface Texture Properties Evaluated against Human Performance. In: 13th International Conference on Control, Automation, Robotics & Vision, Singapore. IEEE. 6 pp. [Conference contribution]

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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%.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:31052
Deposited By: Dr Sonya Coleman
Deposited On:06 Mar 2015 09:48
Last Modified:06 Mar 2015 09:48

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