Material classification based on thermal properties &amp;#x2014; A robot and human evaluation

Kerr, Emmett; McGinnity, T.M.; Coleman, Sonya

doi:10.1109/robio.2013.6739602

Cited by 30 publications

(30 citation statements)

References 12 publications

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“…This suggests that human temperature sensing may be emulated by thermal conduction sensing (Monkman and Taylor, 1993). An approach which analyses the thermal properties of various materials is described in (Kerr et al, 2013). The materials are classified into initial groupings and further identified individually via the data gathered from test materials using the Syntouch R BioTAC TM sensor.…”

Section: Background and Related Researchmentioning

confidence: 99%

“…Rather than classifying materials by analysing just one property, a combination of compressibility, texture and/ or thermal properties can be used to achieve higher rates of classification accuracy. Kerr et al (2014a), used a combination of two of these properties, extending their work in (Kerr et al, 2013) by considering surface texture (vibration) as an additional modality alongside the thermal properties of a material. Additionally, the authors redesigned the system to improve efficiency by selecting a reduced number of principal components, reducing the number of neurons in the hidden layer and hence training the system faster than previously.…”

Section: Background and Related Researchmentioning

confidence: 99%

“…The BioTAC fingertip is a tactile sensor that is shaped like a human fingertip and is filled with an incompressible liquid, giving it similar compliance to a human fingertip (Lin et al, 2009;Kerr et al, 2013). The sensor is capable of detecting a full range of information: micro vibrations, forces and temperature, similar to a human finger.…”

Section: Experimental Set-up 311 Tactile Sensormentioning

confidence: 99%

“…alu-minium, copper, pine etc.). Building on the work presented in (Kerr et al, 2013(Kerr et al, , 2014a and (Kerr et al, 2014b) the contribution of this work is the implementation of six further classifiers, hybrid configurations of the classifiers and a comparison of the performances of these classifiers. The classifiers implemented and compared are a one stage ANN (Kerr et al, 2014a), a two stage ANN (Kerr et al, 2014b), a one stage SVM, a two stage SVM, a hybrid ANN and SVM approach, GMM, LDA, NB, k -NN and MLP.…”

Section: Introductionmentioning

confidence: 99%

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Material recognition using tactile sensing

Kerr

McGinnity

Coleman

2018

Expert Systems with Applications

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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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Section: Background and Related Researchmentioning

confidence: 99%

Section: Background and Related Researchmentioning

confidence: 99%

Section: Experimental Set-up 311 Tactile Sensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Material recognition using tactile sensing

Kerr

McGinnity

Coleman

2018

Expert Systems with Applications

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“…They achieved classification by recording the convergent temperatures of 5 materials after 35 s of contact [19]. Kerr et al [20] also used a heated BioTAC sensor (allowed 15-20 minutes to reach a steady-state after it is powered on) to record the thermal response data of 6 material groups with varying thermal properties for 20 s. They used the static temperature (TAC) and dynamic thermal conductivity (TDC) data from 15 trials for each material and implemented ANN with 73% accuracy [20].…”

Section: B Long-duration Contact With Consistent Initial Conditionsmentioning

confidence: 99%

Material Recognition from Heat Transfer given Varying Initial Conditions and Short-Duration Contact

Bhattacharjee¹,

Wade²,

Kemp³

2015

Robotics: Science and Systems XI

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Abstract-When making contact with an object, a robot can use a tactile sensor consisting of a heating element and a temperature sensor to recognize the object's material based on conductive heat transfer from the tactile sensor to the object. When this type of tactile sensor has time to fully reheat prior to contact and the duration of contact is long enough to achieve a thermal steady state, numerous methods have been shown to perform well. In order to enable robots to more efficiently sense their environments and take advantage of brief contact events over which they lack control, we focus on the problem of material recognition from heat transfer given varying initial conditions and short-duration contact. We present both modelbased and data-driven methods. For the model-based method, we modeled the thermodynamics of the sensor in contact with a material as contact between two semi-infinite solids. For the data-driven methods, we used three machine learning algorithms (SVM+PCA, k-NN+PCA, HMMs) with time series of raw temperature measurements and temperature change estimates. When recognizing 11 materials with varying initial conditions and 3-fold cross-validation, SVM+PCA outperformed all other methods, achieving 84% accuracy with 0.5 s of contact and 98% accuracy with 1.5 s of contact.

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