Material recognition using tactile sensing

•A system that can recognise materials based on tactile sensing alone is presented.•Identification achieved by analysis of thermal conductivity and surface texture.•Evaluation of various machine learning algorithms and developed hybrid classifiers.•The system outperforms humans in the task of materi...

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Veröffentlicht in:Expert systems with applications Jg. 94; S. 94 - 111
Hauptverfasser: Kerr, Emmett, McGinnity, TM, Coleman, Sonya
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 15.03.2018
Elsevier BV
Schlagworte:
Artificial intelligence
Biomimetic materials
Biomimetics
Classification
Classifiers
Compressibility
Data collection
Feature extraction
Grasping (robotics)
Human performance
Hybrid structures
Material classification
Neural networks
Object recognition
Principal components analysis
Signal processing
Studies
Systems analysis
Tactile sensing
Tactile sensors (robotics)
Thermodynamic properties
Signal processing
Tactile sensing
Material classification
Neural networks
Artificial intelligence
Classification
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•A system that can recognise materials based on tactile sensing alone is presented.•Identification achieved by analysis of thermal conductivity and surface texture.•Evaluation of various machine learning algorithms and developed hybrid classifiers.•The system outperforms humans in the task of material recognition. Identification of the material from which an object is made is of significant value for effective robotic grasping and manipulation. Characteristics of the material can be retrieved using different sensory modalities: vision based, tactile based or sound based. Compressibility, surface texture and thermal properties can each be retrieved from physical contact with an object using tactile sensors. This paper presents a method for collecting data using a biomimetic fingertip in contact with various materials and then using these data to classify the materials both individually and into groups of their type. Following acquisition of data, principal component analysis (PCA) is used to extract features. These features are used to train seven different classifiers and hybrid structures of these classifiers for comparison. For all materials, the artificial systems were evaluated against each other, compared with human performance and were all found to outperform human participants’ average performance. These results highlighted the sensitive nature of the BioTAC sensors and pave the way for research that requires a sensitive and accurate approach such as vital signs monitoring using robotic systems.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2017.10.045