A 32 × 32 temperature and tactile sensing array using PI-copper films

Yang, Yao‐Joe; Cheng, Ming-Yuan; Shih, Syuan-He; Huang, Xiao-Xuan; Tsao, C.-M.; Chang, Fu−Yu; Fan, Kuang‐Chao

doi:10.1007/s00170-009-1940-z

Cited by 80 publications

(66 citation statements)

References 18 publications

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“…8) with a spatial resolution of 5 mm. In [69], larger arrays (32 × 32) with comparable spatial and pressure resolution are presented with the use of pads of electrically conductive transfer tape.…”

Section: Conductive Elastomer Compositesmentioning

confidence: 99%

Tactile sensing for dexterous in-hand manipulation in robotics—A review

Yousef

Boukallel

Althoefer

2011

Sensors and Actuators A: Physical

693

374

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Section: Conductive Elastomer Compositesmentioning

confidence: 99%

Tactile sensing for dexterous in-hand manipulation in robotics—A review

Yousef

Boukallel

Althoefer

2011

Sensors and Actuators A: Physical

693

374

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“…1) Hardware-centric: Many researchers have included thermal sensing as a part of multimodal tactile sensing hardware, such as absolute temperature sensors [10], [11] and sensors that use heat transfer [12], [13], [14], [15], [16]. However, this body of work focuses on hardware development with little evaluation of material recognition performance.…”

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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“…Also, they offered very advanced technological solutions for robotics and wearable systems. From a mechanical point of view, flexible and stretchable substrates were chosen, while the integrated components went from rigid (Yang et al, 2010;Kim et al, 2011;Xu et al, 2014) to semi-rigid (Engel et al, 2005;Yang et al, 2008) and finally to flexible (Tien et al, 2014;Park et al, 2015;Zhang et al, 2015) and stretchable (Someya et al, 2004;Sekitani et al, 2008;Park et al, 2014;Chou et al, 2015) devices.…”

mentioning

confidence: 99%