Artificial Tactile Sensors Research Articles

Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications.

Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human-machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.

Model-driven triboelectric sensors for multidimensional tactile perception

Tactile perception, as a key sensory function for environment awareness, has been the subject of extensive scientific research and technological application, producing artificial tactile sensors using piezoresistance, capacitance, magnetism to emerging triboelectricity. However, the decoupling of normal and shear loads remains elusive and faces a table-lookup challenge. Here, we report a tactile array sensor consisting of four triboelectric nanogenerator units with single-electrode-mode triboelectrification to realize the decoupling of normal and shear loads, where a unified theoretical model is developed based on a spinosum-hill small deformation hypothesis to address the table-lookup problem. The above decoupling of the normal and shear loads is further extended from a two-dimensional plane to a three-dimensional space based on the superposition principle of triboelectric outputs. Moreover, for a biological laboratory automation application, our sensor is installed on a robot manipulator to realize an automatic pipetting operation based on force feedback, solving the problem of droplet residue. We believe this study to be the very demonstration of model-driven tactile sensor for decoupling normal and shear loads, paving the way for the tactile perception of intelligent devices such as intelligent robotics.

Optical Nanofiber Skins for Multifunctional Humanoid Tactility

Humanoid tactility has been boosting robotic intelligence in object recognition, dexterous manipulation, and human–robot interaction. For many artificial tactile sensors, especially those based on optical principles, inflexibility, bulkiness, and monomodality limit their potential to function as humanoid skins. Herein, by embedding lab‐made optical nanofibers (ONFs) into elastomeric films, soft, flexible, thin (around 500 μm, similar to human skin), and multimodal (force and thermosensitive) robotic skins are achieved. These superior characteristics arise from the low flexural rigidity and large evanescent field of ONFs, due to their subwavelength diameters (down to 450 nm). By tuning light wavelength, ONF diameter, and skin thickness, variable sensitivities and sensing ranges for both force and temperature are reported. Depending on different sensing requirements, special modules can be further assembled on the ONF skins for various surface properties, including hardness, texture, and thermal conductivity. The ONF skins can enable a commercial robot to emulate human behaviors, including adaptive grasping of flimsy objects, contactless temperature measurement, and even the perception of leaf veins. It is anticipated that these ONF skins could offer a unique solution in multiple intelligent systems such as robotics, prosthetics, human–machine interface and wearable devices.

Artificial SA-I and RA-I afferents for tactile sensing of ridges and gratings.

For robot touch to reach the capabilities of human touch, artificial tactile sensors may require transduction principles like those of natural tactile afferents. Here we propose that a biomimetic tactile sensor (the TacTip) could provide suitable artificial analogues of the tactile skin dynamics, afferent responses and population encoding. Our three-dimensionally printed sensor skin is based on the physiology of the dermal-epidermal interface with an underlying mesh of biomimetic intermediate ridges and dermal papillae, comprising inner pins tipped with markers. Slowly adapting SA-I activity is modelled by marker displacements and rapidly adapting RA-I activity by marker speeds. We test the biological plausibility of these artificial population codes with three classic experiments used for natural touch: (1a) responses to normal pressure to test adaptation of single afferents and spatial modulation across the population; (1b) responses to bars, edges and gratings to compare with measurements from monkey primary afferents; and (2) discrimination of grating orientation to compare with human perceptual performance. Our results show a match between artificial and natural touch at single afferent, population and perceptual levels. As expected, natural skin is more sensitive, which raises a challenge to fabricate a biomimetic fingertip that demonstrates human sensitivity using the transduction principles of human touch.

Multidimensional Tactile Sensor with a Thin Compound Eye-Inspired Imaging System.

Artificial tactile sensing for robots is a counterpart to the human sense of touch, serving as a feedback interface for sensing and interacting with the environment. A vision-based tactile sensor has emerged as a novel and advantageous branch of artificial tactile sensors. Compared with conventional tactile sensors, vision-based tactile sensors possess stronger potential thanks to acquiring multimodal contact information in much higher spatial resolution, although they typically suffer from bulky size and fabrication challenges. In this article, we report a thin vision-based tactile sensor that draws inspiration from natural compound eye structures and demonstrate its capability of sensing three-dimensional (3D) force. The sensor is composed of an array of vision units, an elastic touching interface, and a supporting structure with illumination. Experiments validated the sensor's advantages, including competitive spatial resolution of deformation as high as 1016 dpi on a 5 × 8 mm2 sensing area, superior accuracy of 3D force measurement at levels of 0.018 N for tangential force and 0.213 N (0.108 N at the center region) for normal force, and real-time processing at 30 Hz, while achieving a thin size of 5 mm. We further demonstrate the sensor capability in sensing 3D force and slip occurrence in real grasping experiments. This device paves the way for robotic applications that require rich tactile information with miniaturized sensor structure.

Omnidirectional Tactile Profiling Using a Deformable Pressure Sensor Array Based on Localized Piezoresistivity

AbstractWith the recent progress of stretchable electronics, artificial tactile sensors pursue to imitate the human tactile system that is deformable and omnidirectional without signal interference. Although deformable tactile sensors without signal interference are demonstrated previously by using stretchable transistors or diodes, achieving omnidirectional tactility is very limited. It is because covering the 3D surfaces of fingers and hands with a sensor requests the structural simplicity and the customized shape design of the sensor. Here, a simple piezoresistive sensor without using the transistors or diodes is developed. The sensor is based on the localized piezoresistivity realized by creating the elastic resistive pixels that are laterally insulating with neighboring pixels. It can be wrapped on a finger and provide the pressure profiles and the spatial deformation of the substrate when multiple mechanical stimuli are applied from arbitrary directions. Based on this omnidirectional tactility, the blind contact recognition of objects that are piled up in a box is demonstrated.

60 nm Pixel-size pressure piezo-memory system as ultrahigh-resolution neuromorphic tactile sensor for in-chip computing

Neuromorphic tactile sensors provide considerable feasibility to simulate essential capabilities of the human tactile system even beyond the bio-perceptible range. Nevertheless, the complicated circuitry of artificial tactile sensors based on electrical-skin, memory, and processing components show serial barriers in terms of system resolution, integration, and power consumption. Here, a high-resolution pressure piezo-memory system (HPPMS) is reported with 60 nm pixel-size as a competent neuromorphic tactile sensor, that presents non-volatile force resistive conversion and force-tunable synaptic functions. The piezo-memory pixel arrays realize nanoscale force-image sensing and memory actions as same as neuromorphic tactile preprocessing by enhanced processing efficiencies and recognition rates. The proof-of-concept system affords the possibility to achieve ultra-small force distributions, simplify tactile sensor circuits, and develop advanced applications in in-sensor computing and artificial intelligence.

Advanced Materials and Technologies for Touch Sensing in Prosthetic Limbs

Neuroscience studies have proved that the absence of proper tactile feedback can affect human behavior. A qualitative and quantitative growth in flexible artificial touch sensing technologies has been witnessed over the recent years. The development of flexible, sensitive, cost-effective, and durable artificial tactile sensors is crucial for prosthetic rehabilitation. Many researchers are working on realizing a smart touch sensing system for prosthetic devices. To mimic the human sensory system is extremely difficult. The practical uses of the newly invented techniques in the industry are limited by complex fabrication processes and lack of proper data processing techniques. Many compatible flexible substrates, materials, and strategies for tactile sensors have been identified to enhance the amputee population. This paper reviews the flexible substrates, functional materials, preparation methods, and several computational techniques for artificial tactile sensors.

A tactile sensor system with sensory neurons and a perceptual synaptic network based on semivolatile carbon nanotube transistors

The human sensory system has a fascinating stimulus-detection capability attributed to the fact that the feature (pattern) of an input stimulus can be extracted through perceptual learning. Therefore, sensory information can be organized and identified efficiently based on iterative experiences, whereby the sensing ability is improved. Specifically, the distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. Herein, we demonstrate an artificial tactile sensor system with a sensory neuron and a perceptual synaptic network composed of a single device: a semivolatile carbon nanotube transistor. The system can differentiate the temporal features of tactile patterns, and its recognition accuracy can be improved by an iterative learning process. Furthermore, the developed circuit model of the system provides quantitative analytical and product-level feasibility. This work is a step toward the design and use of a neuromorphic sensory system with a learning capability for potential applications in robotics and prosthetics.

Perception of Tactile Directionality via Artificial Fingerpad Deformation and Convolutional Neural Networks.

Humans can perceive tactile directionality with angular perception thresholds of 14-40° via fingerpad skin displacement. Using deformable, artificial tactile sensors, the ability to perceive tactile directionality was developed for a robotic system to aid in object manipulation tasks. Two convolutional neural networks (CNNs) were trained on tactile images created from fingerpad deformation measurements during perturbations to a handheld object. A primary CNN regression model provided a point estimate of tactile directionality over a range of grip forces, perturbation angles, and perturbation speeds. A secondary CNN model provided a variance estimate that was used to determine uncertainty about the point estimate. A 5-fold cross-validation was performed to evaluate model performance. The primary CNN produced tactile directionality point estimates with an error rate of 4.3% for a 20° angular resolution and was benchmarked against an open-source force estimation network. The model was implemented in real-time for interactions with an external agent and the environment with different object shapes and widths. The perception of tactile directionality could be used to enhance the situational awareness of human operators of telerobotic systems and to develop decision-making algorithms for context-appropriate responses by semi-autonomous robots.

Ultrasensitive, Low‐Voltage Operational, and Asymmetric Ionic Sensing Hydrogel for Multipurpose Applications

AbstractCurrent artificial tactile sensors mostly exploit a variety of electron‐related physical mechanisms to obtain high sensitivity and low detection force. However, these mechanisms are still distinct from the ion‐related biological processes of human's tactile sensation, and are therefore away from the goal of bionic applications. In the past few years, only several types of ionic tactile sensors have been proposed, and they are still subject to low sensitivity. Here, a novel type of ultrasensitive hydrogel tactile sensor is reported based on asymmetric ionic charge injection as the working mechanism, named as asymmetric ionic sensing hydrogel (AISH). With a small external working voltage of only tens of millivolts, these AISH devices show an extremely low detection force of 0.075 Pa, ultrahigh sensitivity of 57–171 kPa−1, and excellent cycling reliability upon pressing. Applications of these ultrasensitive tactile sensors in fingerprint identification of voice, monitoring of pulse waves, and detection of underwater wave signals are experimentally demonstrated. Combining the merits of simple fabrication process, ionic‐type detection mechanism, and ion injection procedure, such AISH sensors not only reveal a new strategy toward highly sensitive tactile sensors, but also show realistic potential applications in future wearable electronic and bioelectronic devices.

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems.

Neuroprosthetics and neuromodulation represent a promising field for several related applications in the central and peripheral nervous system, such as the treatment of neurological disorders, the control of external robotic devices, and the restoration of lost tactile functions. These actions are allowed by the neural interface, a miniaturized implantable device that most commonly exploits electrical energy to fulfill these operations. A neural interface must be biocompatible, stable over time, low invasive, and highly selective; the challenge is to develop a safe, compact, and reliable tool for clinical applications. In case of anatomical impairments, neuroprosthetics is bound to the need of exploring the surrounding environment by fast-responsive and highly sensitive artificial tactile sensors that mimic the natural sense of touch. Tactile sensors and neural interfaces are closely interconnected since the readouts from the first are required to convey information to the neural implantable apparatus. The role of these devices is pivotal hence technical improvements are essential to ensure a secure system to be eventually adopted in daily life. This review highlights the fundamental criteria for the design and microfabrication of neural interfaces and artificial tactile sensors, their use in clinical applications, and future enhancements for the release of a second generation of devices.

An Artificial Vibrissa-Like Sensor for Detection of Flows.

In nature, there are several examples of sophisticated sensory systems to sense flows, e.g., the vibrissae of mammals. Seals can detect the flow of their prey, and rats are able to perceive the flow of surrounding air. The vibrissae are arranged around muzzle of an animal. A vibrissa consists of two major components: a shaft (infector) and a follicle–sinus complex (receptor), whereby the base of the shaft is supported by the follicle-sinus complex. The vibrissa shaft collects and transmits stimuli, e.g., flows, while the follicle-sinus complex transduces them for further processing. Beside detecting flows, the animals can also recognize the size of an object or determine the surface texture. Here, the combination of these functionalities in a single sensory system serves as paragon for artificial tactile sensors. The detection of flows becomes important regarding the measurement of flow characteristics, e.g., velocity, as well as the influence of the sensor during the scanning of objects. These aspects are closely related to each other, but, how can the characteristics of flow be represented by the signals at the base of a vibrissa shaft or by an artificial vibrissa-like sensor respectively? In this work, the structure of a natural vibrissa shaft is simplified to a slender, cylindrical/tapered elastic beam. The model is analyzed in simulation and experiment in order to identify the necessary observables to evaluate flows based on the quasi-static large deflection of the sensor shaft inside a steady, non-uniform, laminar, in-compressible flow.

Artificial Tactile Sensor With Pin-type Module for Depth Profile and Surface Topography Detection

Haptic sensors based on piezoelectric sensor arrays with pin-type modules which have high responses and dynamic sensing capabilities are designed and studied for surface topography measurements. Unlike the human finger, most flexible tactile sensor designs do not detect the depth information of surfaces well, which change at the mm level despite the fact that they have a good sensitivity for pressure or force. To enhance the ability to detect depth information of the surfaces of objects, a piezoelectric sensor combining a pin-type module with excellent monitoring in the depth direction by spring is developed in this paper. Because spike types of piezoelectric signals do not match a specific surface topography directly, a signal processing method that reconstructs the surface topography is studied considering the piezoelectric working principle and spring dynamics. According to the sensor design, it can detect 3 mm depth changes with a two-dimensional plane structure at mm-level resolutions. The results show that the proposed sensor could measure various shapes and depth profiles precisely via a sliding motion, and the surface topography is reconstructed through highly accurate measurement results, similar to a human.

A novel optical 3D force and displacement sensor – Towards instrumenting the PapillArray tactile sensor

As robots move into unstructured environments, tactile sensing will become an essential capability of any robotic gripper attempting to engage in dexterous object manipulation. Sensing the frictional properties of the sensor-object interface (along with contact forces and torques) is essential for determining the minimum grip force required to securely grasp an object; however, most existing artificial tactile sensors are incapable of sensing the property of friction. In previous work, a design was presented for a grip security sensor – the PapillArray – which consists of an array of independent silicone pillars with different uncompressed heights, inspired by the papillae in the skin of the human finger pad. It was shown that tracking the relative deflections of the pillar tips can be used to detect incipient slip; and when incipient slip is detected, measuring the 3D force on the pillar provides an estimate of the coefficient of static friction. In a continuation of this work, here we present a method of instrumenting the movement and forces experienced by the pillars of the PapillArray. A novel pinhole camera design is implemented whereby deformation of the pillar results in both movement and change in area of a spot of light, projected onto a quadrant photodiode. The four quadrant photodiode signals are mapped to true 3D displacement and force using multivariate regression. The accuracy of the mapping’s position and force estimates are validated using both video-tracking of the pillar tip and measurements made with a 3D force sensor, respectively, when stimulating the pillar with a spiral XY displacement pattern at various Z compressions. The overall displacement estimation error (mean ± SD) in the X, Y and Z axes was 0.012 ± 0.077 mm, 0.041 ± 0.074 mm and 0.010 ± 0.023 mm, respectively, for a full-scale deflection of 10.0 mm in X and Y, and 3.25 mm in Z. The overall force estimate error (mean ± SD) was 0.002 ± 0.045 N, -0.007 ± 0.049 N and 0.073 ± 0.118 N, for full-scale force of approximately 4 N in X and Y and 11 N in Z, respectively. The sensitivity of the pillar to vibration (between 100 and 1000 Hz) was also tested, as this may help to detect incipient slip events. The pillar can detect vibration up to 1000 Hz for a vibration displacement amplitude as small as 0.35 μm. This technique can be used to instrument all pillars in the PapillArray sensor, thereby facilitating incipient slip detection and friction measurement. Such a sensor would be of particular benefit in a robotic gripping application where it is important to apply only the grip force required to prevent slip of the object of interest, both to reduce the energy usage of the gripping system and to avoid damaging the gripped object.

Artificial Tactile Sensor Structure for Surface Topography Through Sliding

Tactile sensors mimicking the human sense of touch have been studied and various technologies for the sensing of external stimuli have been suggested as well. Humans detect certain external stimuli and become aware of related sensations, such as roughness or smoothness. Among the various physical parameters, surface information is the most informative type of perception to impart these sensations onto an electromechanical system, such as an android robot or a smart phone. Here, an array sensor, which uses a sliding method for the precise perception of surface information, such as shapes and structures, is demonstrated. The suggested array sensor design with the excellent dynamic response of a piezoelectric material results in enhanced spatial resolutions with sliding motions and detects variable sliding speeds. A soft material was employed to the sensor to enhance the capability of shape distinction. Color mapping was applied to translate surface patterns into visual images. The reconfigured surface information had high accuracy compared to actual information. The demonstrated sliding speed, pattern detection, and shape detection capabilities as well as the higher spatial resolutions allow the sensor to be utilized as an artificial tactile sensor.

Influence of Vibrissa Morphology on Artificial Tactile Sensors for Surface Texture Detection

AbstractAnimals, e.g. rats and cats, have different types of tactile hairs. One type are the mystacial vibrissae. They are located around the snout and in combination with the follicle‐sinus complex they are powerful tactile sensors. With them, animals are able to detect the distance to an object, recognize the object shape and determine the surface texture. Adapting the natural example, the goal is to design an artificial tactile sensor with a similar functionality. In the course of this, a model for surface texture detection is developed. The vibrissa is modeled as an Euler‐Bernoulli bending beam, incorporating large deflections. The contact, between vibrissa and surface, is modeled using Coulomb's law of friction. The mechanical modeling results in a nonlinear, system of first order differential equations. Solving the boundary‐value problem by applying a shooting method, a quasi‐static simulation is performed. Some first relations between the vibrissa morphology and the surface contact are analyzed. With view to an artificial sensor, the change of the reaction forces and moments at the base of the vibrissa due to the surface contact is the point of focus. Out of the reaction forces, the coefficient of static friction between vibrissa and surface is determined. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Addition of a Biomimetic Fingerprint on an Artificial Fingertip Enhances Tactile Spatial Acuity

The fingerprint is a morphological aspect of the human fingertip that has interesting implications for our sense of touch. Previous studies focused on how the fingerprint affects the perception of stimuli that excite high temporal frequencies, such as for texture perception. These studies also only add papillary ridges to their sensors. Here, we endow a biomimetic sensor with both papillary ridges (fingerprint) and a dermal stiffness contrast (stiffer intermediate ridges), and assess the impact on localization perception accuracy. The sensor is based on a novel modular version of a three-dimensional printed tactile sensor (TacTip). Tactile data were collected with these tips on nine stimuli with varying curvature. The location perception acuity of three tips [smooth, fingerprint, and fingerprint (cores)] were compared with a probabilistic classification method, finding that both fingerprinted tips increase the perceptual acuity of small spatial scales. Interestingly, the fingerprint variant had poorer accuracy than the smooth tip for larger spatial scales; however, adding cores to enhance the dermal stiffness counteracted the degradation of accuracy. This supports the theories that the fingerprint aids the classification of edges and smaller spatial scales, and demonstrates that the addition of a fingerprint to an artificial tactile sensors improves its acuity.

Bioinspired tactile sensor for surface roughness discrimination

Surface texture discrimination using artificial tactile sensors has attracted increasing attentions in the past decade as it can endow robot systems with a key missing ability. However, as a major component of texture, roughness has rarely been explored. This paper presents an approach for tactile surface roughness discrimination, which includes two parts: (1) design and fabrication of a bioinspired artificial fingertip, and (2) tactile signal processing for tactile surface roughness discrimination. The bioinspired fingertip is comprised of two polydimethylsiloxane (PDMS) layers, a polymethyl methacrylate (PMMA) bar, and two perpendicular polyvinylidene difluoride (PVDF) film sensors. This artificial fingertip mimics human fingertips in three aspects: (1) Elastic properties of epidermis and dermis in human skin are replicated by the two PDMS layers with different stiffness, (2) The PMMA bar serves the role analogous to that of a bone, and (3) PVDF film sensors emulate Meissner’s corpuscles in terms of both location and response to the vibratory stimuli. Various extracted features and classification algorithms including support vector machines (SVM) and k-nearest neighbors (kNN) are examined for tactile surface roughness discrimination. Eight standard rough surfaces with roughness values (Ra) of 50μm, 25μm, 12.5μm, 6.3μm, 3.2μm, 1.6μm, 0.8μm, and 0.4μm are explored. By simply sliding the sensor on the surfaces without any load and speed controller, we found that the highest classification accuracy of (82.6±10.8) % can be achieved using solely one PVDF film sensor with kNN (k=9) classifier and the standard deviation feature, i.e., the developed approach is very affordable, robust and suitable for real time surface roughness evaluation.

Bio-inspired Tactile FA-I Spiking Generation under Sinusoidal Stimuli

For the design and development of advanced prosthetic limbs, many attempts have been made to restore the function of mechanoreceptors using artificial tactile sensors. Mechanoreceptors in human skin, which make dexterous manipulation possible, respond to the mechanical stimuli in the form of spike trains. In this paper, a bio-inspired approach to replicate the Fast Adapting type I (FA-I) mechanoreceptor is developed, where piezoelectric materials, such as polyvinylidene difluoride (PVDF) films, are used to generate continuous analog electrical signals; then the analog signals are successfully converted into spike trains using the spiking neuron model. By comparing with spike trains measured from the glabrous skin of macaque monkeys, it was found that this approach can mimic FA-I afferent spiking activities in terms of both the average inter-spike interval and the first spike latency. Spike features of the FA-I mechanoreceptors, such as the variability, frequency dependent responses, and population activity, were also explored, which may play a vital role in the understanding of the functionality of FA-I mechanoreceptors and the development of advanced prosthetic limbs.