A finger-shaped tactile sensor for fabric surfaces evaluation by 2-dimensional active sliding touch

Growth of Wide-Bandgap Monolayer Molybdenum Disulfide for a Highly Sensitive Micro-Displacement Sensor

Two-dimensional (2D) piezoelectric semiconductor materials are garnering significant attention in applications such as intelligent sensing and energy harvesting due to their exceptional physical and chemical properties. Among these, molybdenum disulfide (MoS2), a 2D wide-bandgap semiconductor, exhibits piezoelectricity in odd-layered structures due to the absence of an inversion symmetry center. In this study, we present a straightforward chemical vapor deposition (CVD) technique to synthesize monolayer MoS2 on a Si/SiO2 substrate, achieving a lateral size of approximately 50 µm. Second-harmonic generation (SHG) characterization confirms the non-centrosymmetric crystal structure of the wide-bandgap MoS2, indicative of its piezoelectric properties. We successfully transferred the triangular MoS2 to a polyethylene terephthalate (PET) flexible substrate using a wet-transfer method and developed a wide-bandgap MoS2-based micro-displacement sensor employing maskless lithography and hot evaporation techniques. Our testing revealed a piezoelectric response current of 5.12 nA in the sensor under a strain of 0.003% along the armchair direction of the monolayer MoS2. Furthermore, the sensor exhibited a near-linear relationship between the piezoelectric response current and the strain within a displacement range of 40-100 µm, with a calculated response sensitivity of 1.154 µA/%. This research introduces a novel micro-displacement sensor, offering potential for advanced surface texture sensing in various applications.

Touch, Texture and Haptic Feedback: A Review on How We Feel the World around Us

Touch is one most of the important aspects of human life. Nearly all interactions, when broken down, involve touch in one form or another. Recent advances in technology, particularly in the field of virtual reality, have led to increasing interest in the research of haptics. However, accurately capturing touch is still one of most difficult engineering challenges currently being faced. Recent advances in technology such as those found in microcontrollers which allow the creation of smaller sensors and feedback devices may provide the solution. Beyond capturing and measuring touch, replicating touch is also another unique challenge due to the complexity and sensitivity of the human skin. The development of flexible, soft-wearable devices, however, has allowed for the creating of feedback systems that conform to the human form factor with minimal loss of accuracy, thus presenting possible solutions and opportunities. Thus, in this review, the researchers aim to showcase the technologies currently being used in haptic feedback, and their strengths and limitations.

A Review of Sensors Used on Fabric-Handling Robots

While in most industries, most processes are automated and human workers have either been replaced by robots or work alongside them, fewer changes have occurred in industries that use limp materials, like fabrics, clothes, and garments, than might be expected with today’s technological evolution. Integration of robots in these industries is a relatively demanding and challenging task, mostly because of the natural and mechanical properties of limp materials. In this review, information on sensors that have been used in fabric-handling applications is gathered, analyzed, and organized based on criteria such as their working principle and the task they are designed to support. Categorization and related works are presented in tables and figures so someone who is interested in developing automated fabric-handling applications can easily get useful information and ideas, at least regarding the necessary sensors for the most common handling tasks. Finally, we hope this work will inspire researchers to design new sensor concepts that could promote automation in the industry and boost the robotization of domestic chores involving with flexible materials.

A bionic piezoelectric tactile sensor for features recognition of object surface based on machine learning

Based on the tactile mechanism of human fingertips, a bionic tactile sensor fabricated from polyvinylidene fluoride piezoelectric film is proposed, which can identify the surface softness, viscoelasticity, thermal conductivity, and texture roughness of the object. The tactile sensor is mounted on the fingertip of the bionic manipulator, which obtains the surface features by touching and sliding the object. The time-domain features of the output signal are used for preliminarily discriminating the softness, viscoelasticity, and heat conduction of the object. Finally, based on the Back Propagation and the Particle Swarm Optimization-Back Propagation neural network algorithm, the recognition experiment of texture roughness is carried out using the PSO algorithm to improve the BP neural network so that the optimized BP algorithm has a higher convergence accuracy. The results show that the PSO-BP algorithm achieved the highest accuracy of 98% for identifying samples with different roughnesses and the average recognition achieved an accuracy of 94%. The bionic piezoelectric tactile sensor proposed in this paper has a good application development prospect in recognizing the surface features of objects and intelligent robots.

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition

As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as "tactile avatar". A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various tactile materials ranging from smooth/soft to rough are assessed and found variation among participants. Because tactile responses vary among humans, a deep learning structure is designed to allow personalization through training based on individualized histograms of human tactile cognition and recording physical tactile information. The decision error in each avatar system is less than 2% when 42 materials are used to measure the tactile data with 100 trials for each material under 1.2N of contact force with 4cm s-1 of sliding velocity. As a tactile avatar, the machine categorizes newly experienced materials based on the tactile knowledge obtained from training data. The tactile sensation showed a high correlation with the specific user's tendency. This approach can be applied to electronic devices with tactile emotional exchange capabilities, as well as advanced digital experiences.

A Review of Piezoelectric PVDF Film by Electrospinning and Its Applications

With the increase of interest in the application of piezoelectric polyvinylidene fluoride (PVDF) in nanogenerators (NGs), sensors, and microdevices, the most efficient and suitable methods of their synthesis are being pursued. Electrospinning is an effective method to prepare higher content β-phase PVDF nanofiber films without additional high voltage poling or mechanical stretching, and thus, it is considered an economically viable and relatively simple method. This work discusses the parameters affecting the preparation of the desired phase of the PVDF film with a higher electrical output. The design and selection of optimum preparation conditions such as solution concentration, solvents, the molecular weight of PVDF, and others lead to electrical properties and performance enhancement in the NG, sensor, and other applications. Additionally, the effect of the nanoparticle additives that showed efficient improvements in the PVDF films was discussed as well. For instance, additives of BaTiO3, carbon nanotubes, graphene, nanoclays, and others are summarized to show their contributions to the higher piezo response in the electrospun PVDF. The recently reported applications of electrospun PVDF films are also analyzed in this review paper.

Self-Powered Pressure- and Vibration-Sensitive Tactile Sensors for Learning Technique-Based Neural Finger Skin

Finger skin electronics are essential for realizing humanoid soft robots and/or medical applications that are very similar to human appendages. A selective sensitivity to pressure and vibration that are indispensable for tactile sensing is highly desirable for mimicking sensory mechanoreceptors in skin. Additionally, for a human-machine interaction, output signals of a skin sensor should be highly correlated to human neural spike signals. As a demonstration of fully mimicking the skin of a human finger, we propose a self-powered flexible neural tactile sensor (NTS) that mimics all the functions of human finger skin and that is selectively and sensitively activated by either pressure or vibration stimuli with laminated independent sensor elements. A sensor array of ultrahigh-density pressure (20 × 20 pixels on 4 cm²) of interlocked percolative graphene films is fabricated to detect pressure and its distribution by mimicking slow adaptive (SA) mechanoreceptors in human skin. A triboelectric nanogenerator (TENG) was laminated on the sensor array to detect high-frequency vibrations like fast adaptive (FA) mechanoreceptors, as well as produce electric power by itself. Importantly, each output signal for the SA- and FA-mimicking sensors was very similar to real neural spike signals produced by SA and FA mechanoreceptors in human skin, thus making it easy to convert the sensor signals into neural signals that can be perceived by humans. By introducing microline patterns on the top surface of the NTS to mimic structural and functional properties of a human fingerprint, the integrated NTS device was capable of classifying 12 fabrics possessing complex patterns with 99.1% classification accuracy.

An Application of Deep Learning to Tactile Data for Object Recognition under Visual Guidance

Drawing inspiration from haptic exploration of objects by humans, the current work proposes a novel framework for robotic tactile object recognition, where visual information in the form of a set of visually interesting points is employed to guide the process of tactile data acquisition. Neuroscience research confirms the integration of cutaneous data as a response to surface changes sensed by humans with data from joints, muscles, and bones (kinesthetic cues) for object recognition. On the other hand, psychological studies demonstrate that humans tend to follow object contours to perceive their global shape, which leads to object recognition. In compliance with these findings, a series of contours are determined around a set of 24 virtual objects from which bimodal tactile data (kinesthetic and cutaneous) are obtained sequentially and by adaptively changing the size of the sensor surface according to the object geometry for each object. A virtual Force Sensing Resistor array (FSR) is employed to capture cutaneous cues. Two different methods for sequential data classification are then implemented using Convolutional Neural Networks (CNN) and conventional classifiers, including support vector machines and k-nearest neighbors. In the case of conventional classifiers, we exploit contourlet transformation to extract features from tactile images. In the case of CNN, two networks are trained for cutaneous and kinesthetic data and a novel hybrid decision-making strategy is proposed for object recognition. The proposed framework is tested both for contours determined blindly (randomly determined contours of objects) and contours determined using a model of visual attention. Trained classifiers are tested on 4560 new sequential tactile data and the CNN trained over tactile data from object contours selected by the model of visual attention yields an accuracy of 98.97% which is the highest accuracy among other implemented approaches.

Recognition, classification, and prediction of the tactile sense

The emulation of the tactile sense is presented with the encoding of a complex surface texture through an electrical sensor device. To achieve a functional capability comparable to a human mechanoreceptor, a tactile sensor is designed by employing a naturally formed porous structure of a graphene film. The inherent tactile patterns are achievable by means of proper analysis of the electrical signals that the sensor provides during the event of touching the interacting objects. It is confirmed that the pattern-recognition method using machine learning is suitable for quantifying human tactile sensations. The classification accuracy of the tactile sensor system is better than that of human touch for the tested fabric samples, which have a delicate surface texture.

Humanoid Identification of Fabric Material Properties by Vibration Spectrum Analysis

In daily contexts, fabrics embodied in garments are in contact with human body all the time. Since fabric material properties—such as softness or fineness—can be easily sensed by human fingertips, fabric materials can be roughly identified by fingertip sliding. Identification by simply touching and sliding is convenient and fast, although the room for error is always very large. In this study, a highly discernible fabric humanoid identification method with a fingertip structure inspired tactile sensor is designed to investigate the fabric material properties by characterizing the power spectrum integral of vibration signal basing on fast Fourier transform integral S(FFT), which is generated from a steel ball probe rubbing against a fabric surface at an increasing sliding velocity and normal load, respectively. kv and kw are defined as the slope values to identify the fabric surface roughness and hardness. A sample of 21 pieces of fabric categorized by yarn weight, weave pattern, and material were tested by this method. It was proved that the proposed humanoid sensing method has more efficient compared with fingertip sliding while it is also much more accurate for fabric material identification. Our study would be discussed in light of textile design and has a great number of potential applications in humanoid tactile perception technology.

Tactile Estimation of Molded Plastic Plates Based on the Estimated Impulse Responses of Mechanoreceptive Units

This study proposes a tactile estimation method of molded plastic plates based on human tactile perception characteristics. Plastic plates are often used in consumer products. The tactile evaluation plays an important role in product development. However, physical quantities not taking into account human tactile perception have been employed in previous tactile estimation procedures. Hence, in this study, we adopted the vibrational thresholds of the mechanoreceptive units-FA I, FA II, SA I and SA II-for stimuli detection and developed a tactile estimation method for plastic plates that clarified the mechanoreceptive units related to tactile sensation. The developed tactile sensor consists of a base and a silicone rubber pad that contains strain gauges in it. We detected vibration during touch by the sensor and calculated the estimation of the firing values of the cutaneous mechanoreceptors, which are the essential data obtained by humans during tactile perception, in comparison to the amplitude spectrum of the vibration with the threshold amplitude of each mechanoreceptive unit. Simultaneously, we calculated the relationship between the normal and tangential forces recorded while the sensor ran over the samples. As a result of stepwise linear regression analysis using these values as explanatory variables, the evaluation scores for Soft were successfully estimated using the firing value of FA II and the relationship between normal/tangential forces, and the evaluation scores for Rough were estimated using the SA I firing value.

Active Tactile Transfer Learning for Object Discrimination in an Unstructured Environment Using Multimodal Robotic Skin

In this paper, we propose a probabilistic active tactile transfer learning (ATTL) method to enable robotic systems to exploit their prior tactile knowledge while discriminating among objects via their physical properties (surface texture, stiffness, and thermal conductivity). Using the proposed method, the robot autonomously selects and exploits its most relevant prior tactile knowledge to efficiently learn about new unknown objects with a few training samples or even one. The experimental results show that using our proposed method, the robot successfully discriminated among new objects with [Formula: see text] discrimination accuracy using only one training sample (on-shot-tactile-learning). Furthermore, the results demonstrate that our method is robust against transferring irrelevant prior tactile knowledge (negative tactile knowledge transfer).

Mexican-Hat-Like Response in a Flexible Tactile Sensor Using a Magnetorheological Elastomer

A significant challenge in robotics is providing a sense of touch to robots. Even though several types of flexible tactile sensors have been proposed, they still have various technical issues such as a large amount of deformation that fractures the sensing elements, a poor maintainability and a deterioration in the sensitivity caused by the presence of a thick and soft covering. As one solution for these issues, we proposed a flexible tactile sensor composed of a magnet, magnetic transducer and dual-layer elastomer, which consists of a magnetorheological and nonmagnetic elastomer sheet. In this study, we first investigated the sensitivity of the sensor, which was found to be high (approximately 161 mV/N with a signal-to-noise ratio of 42.2 dB); however, the sensor has a speed-dependent hysteresis in its sensor response curve. Then, we investigated the spatial response and observed the following results: (1) the sensor response was a distorted Mexican-hat-like bipolar shape, namely a negative response area was observed around the positive response area; (2) the negative response area disappeared when we used a compressible sponge sheet instead of the incompressible nonmagnetic elastomer. We concluded that the characteristic negative response in the Mexican-hat-like response is derived from the incompressibility of the nonmagnetic elastomer.

Grasping Force Control for a Robotic Hand by Slip Detection Using Developed Micro Laser Doppler Velocimeter

The purpose of this paper is to show the feasibility of grasping force control by feeding back signals of the developed micro-laser Doppler velocimeter (μ-LDV) and by discriminating whether a grasped object is slipping or not. LDV is well known as a high response surface velocity sensor which can measure various surfaces—such as metal, paper, film, and so on—thus suggesting the potential application of LDV as a slip sensor for grasping various objects. However, the use of LDV as a slip sensor has not yet been reported because the size of LDVs is too large to be installed on a robotic fingertip. We have solved the size problem and enabled the performance of a feasibility test with a few-millimeter-scale LDV referred to as micro-LDV (μ-LDV) by modifying the design which was adopted from MEMS (microelectromechanical systems) fabrication process. In this paper, by applying our developed μ-LDV as a slip sensor, we have successfully demonstrated grasping force control with three target objects—aluminum block, wood block, and white acrylic block—considering that various objects made of these materials can be found in homes and factories, without grasping force feedback. We provide proofs that LDV is a new promising candidate slip sensor for grasping force control to execute target grasping.

Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review

During the last decades, smart tactile sensing systems based on different sensing techniques have been developed due to their high potential in industry and biomedical engineering. However, smart tactile sensing technologies and systems are still in their infancy, as many technological and system issues remain unresolved and require strong interdisciplinary efforts to address them. This paper provides an overview of smart tactile sensing systems, with a focus on signal processing technologies used to interpret the measured information from tactile sensors and/or sensors for other sensory modalities. The tactile sensing transduction and principles, fabrication and structures are also discussed with their merits and demerits. Finally, the challenges that tactile sensing technology needs to overcome are highlighted.

Inferring Interaction Force from Visual Information without Using Physical Force Sensors

In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where the shape of the object is changed by an external force. The force applied to the target can be estimated by means of the visual shape changes. However, the shape differences in the images are not very clear. To address this problem, we formulate a recurrent neural network-based deep model with fully-connected layers, which models complex temporal dynamics from the visual representations. Extensive evaluations show that the proposed learning models successfully estimate the interaction forces using only the corresponding sequential images, in particular in the case of three objects made of different materials, a sponge, a PET bottle, a human arm, and a tube. The forces predicted by the proposed method are very similar to those measured by force sensors.

Surface roughness estimation by optimal tactile features for fruits and vegetables

A study was conducted to evaluate the surface roughness levels of fruits and vegetables by a novel tactile sensor. Firstly, the effective areas of the sensor were determined through the mechanical analysis with the ANSYS software, and the sensitive elements of polyvinylidene fluoride piezoelectric films and strain gauges were randomly arranged in these areas. When the sensor contacted with the surfaces of the fruits and vegetables, the signals produced by the sensitive elements were output and tactile features were obtained. Secondly, the D-score criterion was applied to evaluate the contribution of every tactile feature component in expressing the surface roughness levels. According to the value of D-score, the strategies of the sequential forward selection and equential forward floating selection were used to guide the optimization of feature components selection. Back propagation neural network model was applied to evaluate the performance of the optimal features. Finally, the experimental results revealed that the identification accuracy of the algorithm was up to 93.737%, which demonstrated that the optimal feature subsets possessed fewer dimensions while maintaining a high performance in expressing the surface roughness characteristics of the fruits and vegetables. The results also provided a basis for the optimized design of the tactile sensor.

Highly Sensitive and Patchable Pressure Sensors Mimicking Ion-Channel-Engaged Sensory Organs

Biological ion channels have led to much inspiration because of their unique and exquisite operational functions in living cells. Specifically, their extreme and dynamic sensing abilities can be realized by the combination of receptors and nanopores coupled together to construct an ion channel system. In the current study, we demonstrated that artificial ion channel pressure sensors inspired by nature for detecting pressure are highly sensitive and patchable. Our ion channel pressure sensors basically consisted of receptors and nanopore membranes, enabling dynamic current responses to external forces for multiple applications. The ion channel pressure sensors had a sensitivity of ∼5.6 kPa(-1) and a response time of ∼12 ms at a frequency of 1 Hz. The power consumption was recorded as less than a few μW. Moreover, a reliability test showed stability over 10 000 loading-unloading cycles. Additionally, linear regression was performed in terms of temperature, which showed no significant variations, and there were no significant current variations with humidity. The patchable ion channel pressure sensors were then used to detect blood pressure/pulse in humans, and different signals were clearly observed for each person. Additionally, modified ion channel pressure sensors detected complex motions including pressing and folding in a high-pressure range (10-20 kPa).

A biomimetic tactile sensing system based on polyvinylidene fluoride film

Polyvinylidene fluoride (PVDF) film has been widely investigated as a sensing material due to its outstanding properties such as biocompatibility, high thermal stability, good chemical resistance, high piezo-, pyro- and ferro-electric properties. This paper reports on the design, test, and analysis of a biomimetic tactile sensor based on PVDF film. This sensor consists of a PVDF film with aluminum electrodes, a pair of insulating layers, and a "handprint" friction layer with a copper foil. It is designed for easy fabrication and high reliability in outputting signals. In bionics, the fingerprint of the glabrous skin plays an important role during object handling. Therefore, in order to enhance friction and to provide better manipulation, the ridges of the fingertips were introduced into the design of the proposed tactile sensor. And, a basic experimental study on the selection of the high sensitivity fingerprint type for the biomimetic sensor was performed. In addition, we proposed a texture distinguish experiment to verify the sensor sensitivity. The experiment's results show that the novel biomimetic sensor is effective in discriminating object surface characteristics. Furthermore, an efficient visual application program (LabVIEW) and a quantitative evaluation method were proposed for the verification of the biomimetic sensor. The proposed tactile sensor shows great potential for contact force and slip measurements.

Microfabricated tactile sensors for biomedical applications: a review

During the last decades, tactile sensors based on different sensing principles have been developed due to the growing interest in robotics and, mainly, in medical applications. Several technological solutions have been employed to design tactile sensors; in particular, solutions based on microfabrication present several attractive features. Microfabrication technologies allow for developing miniaturized sensors with good performance in terms of metrological properties (e.g., accuracy, sensitivity, low power consumption, and frequency response). Small size and good metrological properties heighten the potential role of tactile sensors in medicine, making them especially attractive to be integrated in smart interfaces and microsurgical tools. This paper provides an overview of microfabricated tactile sensors, focusing on the mean principles of sensing, i.e., piezoresistive, piezoelectric and capacitive sensors. These sensors are employed for measuring contact properties, in particular force and pressure, in three main medical fields, i.e., prosthetics and artificial skin, minimal access surgery and smart interfaces for biomechanical analysis. The working principles and the metrological properties of the most promising tactile, microfabricated sensors are analyzed, together with their application in medicine. Finally, the new emerging technologies in these fields are briefly described.