Artificial Tactile System Research Articles

Tactile sensory synapse based on organic electrochemical transistors with ionogel triboelectric layer

Recent advancements in artificial tactile sensory systems have significantly improved their ability to replicate the sensory functions of biological systems by integrating various sensory and synaptic components, yet these configurations often lack the efficiency and seamless integration seen in nature. To address these limitations, we introduce a monolithic artificial mechanoreceptor that combines an organic electrochemical transistor with an ionogel (IG), serving as both a mechano-responsive triboelectric sensing layer and a gate electrolyte. Triboelectrification upon mechanical stimulation using different materials produces an output voltage of the triboelectric generator which drives the movement of ions within the tribo-negative IG based on 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ionic liquid and poly(vinylidene fluoride-co-hexafluoropropylene), modulating de-doping of the poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) channel. This enhances synaptic functionalities such as spike number-dependent plasticity (SNDP) and spike duration-dependent plasticity (SDDP) during mechanical stimulation, allowing the device to mimic biological mechanoreceptors by sensing and converting external mechanical signals, including the number, force, and duration of touches, into synaptic plasticity. Furthermore, the device shows potential for material recognition using machine learning techniques, marking a significant advancement towards efficient artificial tactile systems.

Biomimic and bioinspired soft neuromorphic tactile sensory system

The progress in flexible and neuromorphic electronics technologies has facilitated the development of artificial perception systems. By closely emulating biological functions, these systems are at the forefront of revolutionizing intelligent robotics and refining the dynamics of human–machine interactions. Among these, tactile sensory neuromorphic technologies stand out for their ability to replicate the intricate architecture and processing mechanisms of the brain. This replication not only facilitates remarkable computational efficiency but also equips devices with efficient real-time data-processing capability, which is a cornerstone in artificial intelligence evolution and human–machine interface enhancement. Herein, we highlight recent advancements in neuromorphic systems designed to mimic the functionalities of the human tactile sensory system, a critical component of somatosensory functions. After discussing the tactile sensors which biomimic the mechanoreceptors, insights are provided to integrate artificial synapses and neural networks for advanced information recognition emphasizing the efficiency and sophistication of integrated system. It showcases the evolution of tactile recognition biomimicry, extending beyond replicating the physical properties of human skin to biomimicking tactile sensations and efferent/afferent nerve functions. These developments demonstrate significant potential for creating sensitive, adaptive, plastic, and memory-capable devices for human-centric applications. Moreover, this review addresses the impact of skin-related diseases on tactile perception and the research toward developing artificial skin to mimic sensory and motor functions, aiming to restore tactile reception for perceptual challenged individuals. It concludes with an overview of state-of-the-art biomimetic artificial tactile systems based on the manufacturing–structure–property–performance relationships, from devices mimicking mechanoreceptor functions to integrated systems, underscoring the promising future of artificial tactile sensing and neuromorphic device innovation.

Artificial tactile system for pressure monitoring in extracorporeal circulation processes

Current intraoperative pressure monitoring methods still face significant limitations in perception and feedback, struggling to strike a balance between precision and wearable flexibility. Inspired by biological skin, we propose a biomimetic tactile sensing system for pressure monitoring during extracorporeal circulation, comprising flexible pressure sensors and artificial synaptic transistors. Aimed at addressing the aforementioned issues, our system employs a pyramid-shaped elastic design for flexible pressure sensors, utilizing biocompatible materials polydimethylsiloxane and multi-walled carbon nanotubes as the strain-sensitive layer. This configuration boasts ultra-high sensitivity and resolution (115 kPa−1), accurately detecting subtle pressure changes, such as blood circulation wall pressures. With artificial synaptic transistors as the information processing core, our system successfully simulates crucial neural processing functions, including excitatory post-synaptic currents and double-pulse facilitation, while providing alerts for abnormal blood pressure signals. This system facilitates real-time data processing at the device edge, reducing power consumption, improving efficiency, and better addressing the demands of large-scale physiological pressure data processing. It presents a significant reference for future developments in biomedical electronics and bionics.

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.

Integrated sensing–memory–computing artificial tactile system based on force sensors and memristors

Recently, numerous artificial tactile systems have been developed to mimic human tactile, employing force sensors in combination with external memory and computing units. However, the separated architecture of force sensing, memory, and computing results in high power consumption and significant delays, which pose a significant challenge for the development of efficient artificial tactile systems. In this study, we propose an integrated sensing–memory–computing artificial tactile system (smcATS) consisting of a graphene–polystyrene microparticle (G-PsMp) force sensor and an Ag-Fe3O4-ITO memristor. The design of the Ag-Fe3O4-ITO memristor with cross-shaped electrodes addresses the issue of micrometer-scale electrodes in conventional memristors that cannot be directly connected to force sensors. Furthermore, the smcATS demonstrates excellent properties of switching, endurance, and resistance–retention. Based on this, we have developed a visualized smcATS with a resistance state visualization circuit, which can better mimic skin bruising caused by strong external forces. Most importantly, the smcATS can avoid the need for analog-to-digital conversion and data transfer between separate memory and computing units, providing an alternative perspective for developing more efficient artificial tactile systems.

基于图形化铌酸钾钠纳米棒阵列的自供电压力传感器

Micro/nano-scaled pressure sensors that can detect the pressure from an external environment to analyze the location, quantity, and configuration of the applied force, are in high demand for electronic screen, electronic skin, motion monitoring, artificial tactile system and several other fields. In this study, a pressure sensor matrix that can realize the two-dimensional pressure mapping is successfully developed by utilizing the patterned piezoelectric (K,Na)NbO3 (KNN) nanorod arrays. The as-synthesized orthorhombic KNN nanorods exhibit outstanding mechanical flexibility and elasticity, together with high piezoelectric performance, which render outstanding piezoelectric response towards mechanical stimulation by external forces. A high sensitivity of single unit up to 0.20 V N−1 is achieved, with a detection limit down to 20 g and excellent stability, thanks to the highly flexible and elastic properties of the sensitive KNN nanorods. The micro sensor matrix with each unit separated spatially facilitates the detection of the pressure distribution in the effective areas with no cross-interference, which can accurately realize a self-powered pressure mapping and precisely analyze the mechanical stimulations.

Vision-Based High-Resolution Tactile Sensor By Using Visual Light Ring

In this letter, a vision-based high-resolution pressure array sensor with sensitivity enhanced by using visual light ring was proposed. The sensor consists of a transparent flexible layer integrating micropillar array and an image sensor with circular array light emitting diodes (LEDs). When pressure is applied to the micropillar, the surface of the substrate around the micropillar produces a concave, and the edges of the concave reflect the light of the LED array to form visual light ring in the image sensor. A relationship between the pressure and the size of the visual light ring was established. The sensitivity using the light ring was increased by three times compared with using deformation of the micropillar. The sensor achieves a high resolution of 625 sensing points per cm², which was higher than that on the finger of a human. Thus, the sensor will be a competitive solution to artificial tactile system which is of great importance for intelligent robots.

Electronic Skin to Feel "Pain": Detecting "Prick" and "Hot" Pain Sensations.

An artificial tactile system has attracted tremendous interest and intensive study, since it can be applied as a new functional interface between humans and electronic devices. Unfortunately, most previous works focused on improving the sensitivity of sensors. However, humans also respond to psychological feelings for sensations such as pain, softness, or roughness, which are important factors for interacting with others and objects. Here, we present an electronic skin concept that generates a "pain" warning signal, specifically, to sharp "prick" and "hot" sensations. To simplify the sensor structure for these two feelings, a single-body tactile sensor design is proposed. By exploiting "hot" feeling based on the Seebeck effect instead of the pyroelectric property, it is possible to distinguish points registering a "hot" feeling from those generating a "prick" feeling, which is based on the piezoelectric effect. The control of free carrier concentration in nanowire induced the appropriate level of Seebeck current, which enabled the sensor system to be more reliable. The first derivatives of the piezo and Seebeck output signals are the key factors for the signal processing of the "pain" feeling. The main idea can be applied to mimic other psychological tactile feelings.

Tactile Quality Control With Biomimetic Active Touch

Fully autonomous factories of the future will need automated quality control processes to monitor products during manufacture. Here, we demonstrate that an artificial tactile system offers a solution to autonomous quality inspection, using a biomimetic tactile fingertip mounted as end-effector on an industrial arm. The study considers a task of gap width inspection suitable for judging parts alignment, although the methods apply generally. An active perception method implements optimal decision making while controlling sensor location, which was recently shown to attain superresolved spatial perception. In consequence, gap width is estimated to submillimeter accuracy comparable to human discrimination performance and is robust to uncertainty in test object placement. We conclude that an artificial tactile system of the type here offers an ideal solution to automated quality control on the production line.

Structural Solution to Enhance the Sensitivity of a Self-Powered Pressure Sensor for an Artificial Tactile System.

Structural design factors of sensor units have been studied in order to enhance the sensitivity of pressure sensors based on utilizing a piezoelectric material for an artificial tactile sensor. In this study, we have primarily demonstrated the effect of a square pattern array design in a pressure sensor using ZnO nanowires. Nanowires grown on the edge of cells can be bent easily because of growth direction, density control, and buckling effect. Since smaller square pattern arrays induce a higher circumference to cell area ratio, if one sensor unit consists of many micro-level square pattern arrays, the design enhances the piezoelectric efficiency and the sensitivity. As a result, 20 μ m×20 μ m cell arrays showed three times higher pressure sensitivity than 250 μ m×250 μ m cell array structures at a pressure range from 4 kPa to 14 kPa. The induced piezoelectric voltage with the same pressure level also increased drastically. Therefore, the square pattern array design is more appropriate for a high-sensitive pressure sensor than a simple one-body cell design for tactile systems, and it has the advantage of better power efficiency, which is also important for artificial tactile systems. This suggested cell array design can be applied to various systems using piezoelectric nanowires.

Roughness Encoding in Human and Biomimetic Artificial Touch: Spatiotemporal Frequency Modulation and Structural Anisotropy of Fingerprints

The influence of fingerprints and their curvature in tactile sensing performance is investigated by comparative analysis of different design parameters in a biomimetic artificial fingertip, having straight or curved fingerprints. The strength in the encoding of the principal spatial period of ridged tactile stimuli (gratings) is evaluated by indenting and sliding the surfaces at controlled normal contact force and tangential sliding velocity, as a function of fingertip rotation along the indentation axis. Curved fingerprints guaranteed higher directional isotropy than straight fingerprints in the encoding of the principal frequency resulting from the ratio between the sliding velocity and the spatial periodicity of the grating. In parallel, human microneurography experiments were performed and a selection of results is included in this work in order to support the significance of the biorobotic study with the artificial tactile system.

Detection of incipient object slippage by skin-like sensing and neural network processing

Detection of incipient slippage is of great importance in robotics for the control of grasping and manipulation tasks. Together with fine-form reconstruction and primitive recognition, it has to be the main feature of an artificial tactile system. The system presented here is based on a neural network used to detect incipient slippage and on a skin-like sensor sensible to normal and shear stresses. Normal and shear stresses components inside the sensor are the input data of the neural net. An important feature of the system is that the a priori knowledge of the friction coefficient between the sensor and the object being manipulated is not needed. To validate the method we worked on both simulated and experimental data. In the first case, the finite element method is used to solve the direct problem of elastic contact in its full nonlinearity by resorting to the lowest number of approximations regarding the real problem. Simulation has shown that the network learns and is robust to noise. Then an experimental test was carried out. Experimental results show that, in a simple case, the method is able to detect the insipiency of slippage between an object and the sensor.

Towards the realization of an artificial tactile system: fine-form discrimination by a tensorial tactile sensor array and neural inversion algorithms

This paper describes techniques and methodologies so far developed to investigate object fine-form discrimination by means of artificial tactile sensors. Sensor arrays, selectively sensitive to stress-tensor components and based on piezoelectric polymer technology, have been realized. Sensor output data are used to solve inverse elastic contact problems, by means of neural networks suitably trained to learn regularized inverse maps. Two possible neural network designs are considered: one is based on the multilayer perceptron trained with the standard backpropagation algorithm, and the other is based on the use of radial basis functions. In both cases, reconstruction of object shapes is demonstrated to be effective and robust with both simulated and real data. >

Skin-like tactile sensor arrays for contact stress field extraction

Continuous scanning of variable contact forces occurring during object exploration and manipulation by means of miniature sensors is becoming increasingly important in medical prosthetics and orthotics, advanced robotics, teleoperation and telepresence. In this paper a system able to provide robots with tactile sensitivity is presented. Skin-like tactile sensor arrays, which are selectively sensitive to stress-tensor components and are based on piezoelectric polymer technology, have been implemented. The basic characteristics of the sensors, together with the mechanical devices and the electronics developed for setting up the artificial tactile system, are described. An investigation of the expected responses of the sensor has been carried out by modelling the mechanics of object-sensor interaction and by finding the stress solutions of a forward elastic contact problem. The system has been tested by comparing the theoretical results with data measured experimentally from the sensor.