Thermal sensor for object shape and material constitution

Russell, R. A.

doi:10.1017/s0263574700003568

Cited by 19 publications

(7 citation statements)

References 3 publications

(3 reference statements)

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“…In addition, all of these studies use methods that assume consistent initial conditions. 1) Hardware-centric: Russell [4,5] developed a thermal sensing array with which he compared the percent decrease in temperature from a uniform initial temperature after 3 s of contact. The array successfully recognized six distinct materials in a single trial [4,5].…”

Section: A Short-duration Contact With Consistent Initial Conditionsmentioning

confidence: 99%

“…1) Hardware-centric: Russell [4,5] developed a thermal sensing array with which he compared the percent decrease in temperature from a uniform initial temperature after 3 s of contact. The array successfully recognized six distinct materials in a single trial [4,5]. Siegal et al [6] developed another sensor with a slower temporal response, according to Monkman and Taylor [7].…”

Section: A Short-duration Contact With Consistent Initial Conditionsmentioning

confidence: 99%

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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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Section: A Short-duration Contact With Consistent Initial Conditionsmentioning

confidence: 99%

Section: A Short-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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“…The relationship between the thermal effusivity and the temperature change have been investigated in [5], [6], [7]. Since the parameter also relates to human thermal sensation, it plays a role in reproducing the thermal properties of different materials in the telepresence and the haptics research fields.…”

Section: Introductionmentioning

confidence: 99%

Material Classification Using Active Temperature Controllable Robotic Gripper

Osawa

Kase

Domae

et al. 2021

Preprint

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Recognition techniques allow robots to make proper planning and control strategies to manipulate various objects. Object recognition is more reliable when made by combining several percepts, e.g., vision and haptics. One of the distinguishing features of each object's material is its heat properties, and classification can exploit heat transfer, similarly to human thermal sensation. Thermal-based recognition has the advantage of obtaining contact surface information in realtime by simply capturing temperature change using a tiny and cheap sensor. However, heat transfer between a robot surface and a contact object is strongly affected by the initial temperature and environmental conditions. A given object's material cannot be recognized when its temperature is the same as the robotic grippertip. We present a material classification system using active temperature controllable robotic gripper to induce heat flow. Subsequently, our system can recognize materials independently from their ambient temperature. The robotic gripper surface can be regulated to any temperature that differentiates it from the touched object's surface. We conducted some experiments by integrating the temperature control system with the Academic SCARA Robot, classifying them based on a long short-term memory (LSTM) using temperature data obtained from grasping target objects.

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“…There has been several work in thermal sensing and rendering, see e.g. [6] [5]. In this paper, we address thermal rendering in real telepresence.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Thermal Rendering in Telepresence

Guiatni

Benallègue²,

Kheddar

2008

Haptics: Perception, Devices and Scenarios

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We propose a new method for thermal rendering in telepresence systems which allows an operator to feel most transparently the thermal behavior of the remote object. It is based on learning and thermal heat flux generation. Two databases are constructed from real measurements recorded during direct contact between operator's finger and different materials. One database is used in order to identify the material in the slave side, the other database is used to generate desired heat flux for the thermal display loop. The identification bloc is based on Principal Component Analysis and Neural Network. Experimental results validating the proposed method are discussed.

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Thermal sensor for object shape and material constitution

Cited by 19 publications

References 3 publications

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

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

Material Classification Using Active Temperature Controllable Robotic Gripper

Learning-Based Thermal Rendering in Telepresence

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