Development Of Tactile Sensor Research Articles

Functional Tactile Sensor Based on Arrayed Triboelectric Nanogenerators

AbstractIn the era of Internet of Things (IoT) and Artificial Intelligence (AI), sensors have become an integral part of intelligent systems. Although the traditional sensing technology is very mature in long‐term development, there are remaining defects and limitations that make it difficult to meet the growing demands of current applications, such as high‐sensitivity detection and self‐supplied sensing. As a new type of sensor, array triboelectric nanogenerators (TENG)‐based tactile sensors can respond to wide dynamic range of mechanical stimuli in the surrounding environment and converting them into quantifiable electrical signals, thus realizing real‐time self‐supplied tactile sensing. The array structure allows for fine delineation of the sensing area and improved spatial resolution, resulting in accurate localization and quantification of the detected tactile signals, and have been widely used in wearable devices, smart interaction, medical and health detection, and other fields. In this paper, the latest research progress of functional tactile sensors based on arrayed triboelectric nanogenerators is systematically reviewed from the aspects of working mechanism, material selection, material processing, structural design, functional integration, and application. Finally, the challenges faced by arrayed triboelectric tactile sensors are summarized with a view to providing inspiration and guidance for the future development of tactile sensors.

A novel triboelectric-optical hybrid tactile sensor for human-machine tactile interaction

Tactile interaction is one of the primary modes of human-machine interaction, which requires artificial devices to possess tactile sensing abilities. In recent years, to enrich the tactile information perceived by artificial devices, tactile sensors based on hybrid sensing mechanisms have attracted extensive attention. However, for wearable or flexible artificial devices, the development of tactile sensors with a simple structure and preparation process, low cost and power consumption, and good maintainability and integration remains a tough challenge. Herein, we presented a triboelectric-optical hybrid tactile sensor (TOHTS) with a separable modular structure, comprising two units: a triboelectric unit designed based on the single-electrode triboelectric nanogenerator to receive external tactile stimuli, and a photoelectric unit with a liquid-membrane liquid lens structure and a function of translating the tactile stimuli into a photoelectric signal. Both units exhibit a simple structure, and their parts can be easily cannibalized and replaced. The triboelectric unit can control the on-off behavior of the light source embedded in the photoelectric unit to minimize power consumption and improve tactile response speed, according to its output signal. The photoelectric unit possesses superior electromagnetic interference immunity, making the TOHTS accurately and quantitatively perceive contact force. The sensing performance test results validate the TOHTS’s advantages of a short response time (about 9 ms), strong linearity (R2≈0.9952) in sensing contact force, and excellent durability and stability. Due to the above features, the TOHTS exhibits outstanding practicability in tactile interactive tasks, which is proved by some interactive input experiments, and it will have extensive application prospects for human-machine interactive devices.

A self-powered flexible tactile sensor utilizing chemical battery reactions to detect static and dynamic stimuli

Flexible and wearable tactile sensors have become increasingly important in various fields, including healthcare monitoring and human-computer interaction. However, traditional resistive or capacitive sensors require an external power supply to continuously consume energy, while piezoelectric and triboelectric sensors respond only to dynamic mechanical stimuli. The development of tactile sensors that do not require an external power source and are capable of monitoring dynamic/static mechanical stimuli could complement the shortcomings of existing sensing devices. In this paper, we propose a simple, convenient, and environmentally friendly paper-based sandwich solid Zn-MnO2 battery, which can be used as a self-powered flexible tactile sensor through simple structural design. As a battery, it exhibited an open circuit voltage of 1.29 V, the highest short circuit current density of 2.8 mA/cm2, and a capacity of 2.2 mAh/cm2 at a current density of 0.1 mA/cm2, which is sufficient to power small electrical appliances. As a self-powered pressure sensor, it exhibits an average sensitivity of 1.27 μA/kPa in the 0–25 kPa and 0.14 μA/kPa within the 25–500 kPa pressure range, a response time of 42 ms and a recovery time of 41 ms, a minimum pressure resolution of tens of Pascals, and long-period repeatability and stability. This work will significantly contribute to the further advancement of intelligent, self-powered tactile sensors for a wide range of applications in fields such as human healthcare and human-machine interfaces for detecting static and dynamic stimuli.

Pyroelectric Polyelectrolyte Brushes.

Piezo- and pyroelectric materials are of interest, for example, for energy harvesting applications, for the development of tactile sensors, as well as neuromorphic computing. This study reports the observation of pyro- and piezoelectricity in thin surface-attached polymer brushes containing zwitterionic and electrolytic side groups that are prepared via surface-initiated polymerization. The pyro- and piezoelectric properties of the surface-grafted polyelectrolyte brushes are found to sensitively depend on and can be tuned by variation of the counterion. The observed piezo- and pyroelectric properties reflect the structural complexity of polymer brushes, and are attributed to a complex interplay of the non-uniform segment density within these films, together with a non-uniform distribution of counterions and specific ion effects. The fabrication of thin pyroelectric films by surface-initiated polymerization is an important addition to the existing strategies toward such materials. Surface-initiated polymerization, in particular, allows for facile grafting of polar thin polymer films from a wide range of substrates via a straightforward two-step protocol that obviates the need for multistep laborious synthetic procedures or thin film deposition protocols. The ability to produce polymer brushes with piezo- and pyroelectric properties opens up new avenues of application of these materials, for example, in energy harvesting or biosensing.

Flexible tactile sensors with interlocking serrated structures based on stretchable multiwalled carbon nanotube/silver nanowire/silicone rubber composites.

Flexible tactile sensors have attracted significant interest because of their application scope in the fields of biomedicine, motion detection, and human-computer interaction. However, the development of tactile sensors with high sensitivity and flexibility remains a critical challenge. This study develops a patterned, stretchable, and fully elastomeric multiwalled carbon nanotube (MWCNT)/silver nanowire (Ag NW)/silicone rubber (SR) composite. The addition of Ag NWs to MWCNTs enhances the transmission path of the conductive network, yielding a CNT/Ag NW/SR composite with a sensitivity coefficient of 40. This characteristic renders it suitable for use as a piezoresistive sensing material. The interlocking sawtooth structure can convert the mechanical stimuli of the sensor to the tensile strain of the composite, thereby enhancing its sensitivity and flexibility. Experimental results indicate that the developed tactile sensor exhibited a sensitivity of 2.82 N-1 at 0-0.5 N and 1.51 N-1 at 0.5-2 N. These haptic sensors also demonstrate good dynamic response, repeatability, and long life. Furthermore, experimental results show that these haptic sensors exhibit high reproducibility, fast dynamic response, and good mechanical and electrical stability. Because of these exceptional properties, the as-prepared sensor can be applied in the development of smart robots, prosthetics, and wearable devices.

Integrating Point Spread Function into Taxel-based Tactile Pattern Super Resolution.

The past decade has witnessed the development of tactile sensors, which have been increasingly considered as an essential equipment in robotics, especially the dexterous manipulation and collaborative human-robot interactions. There are two major types of tactile sensors, i.e., the vision-based and taxel-based sensors. The latter is capable of achieving lower integration complexity with existing robotic systems, but unable to provide high-resolution (HR) tactile information as that of the vision-based counterpart due to the manufacturing limitations. Therefore, we propose a novel tactile pattern super-resolution (SR) scheme for taxel-based sensors, which is a data-driven scheme enabling customized selection on the number of applied "tapping" actions to achieve improvable performance from single tapping SR (STSR) to the multi-tapping SR (MTSR). In addition, we develop a new dataset for the proposed tactile SR scheme. In order to obtain scalable resolutions (e.g. ×4, ×10, ×20, etc.) of ground-truth HR tactile patterns, we propose a novel tactile point spread function (PSF) scheme to generate HR tactile patterns by leveraging the low-resolution (LR) data gathered directly from the taxel-based sensor and the depth information of contact surfaces. This is in strong contrast to the conventional ground-truth generation approach with overlapped multi-sampling and registration strategy, which can only provide a fixed resolution. Experimental results confirm the efficiency of the proposed scheme.

The role of bio-inspired micro-/nano-structures in flexible tactile sensors

This review focuses on the development of tactile sensors for parameters such as pressure, strain and integration of multiple functions. The application prospect of bionic sensors in e-skin, human–computer interaction, and bionic robots is envisioned.

A three-dimensional force/temperature composite flexible sensor

Flexible sensors that mimic human tactile perception are widely used in areas such as smart prosthetics and human-robot interaction, and play a key role in bridging the gap between humans, robots and the environment. The development of tactile sensors with versatile tactile sensing capabilities remains a challenge. In this paper, a three-dimensional force/temperature composite flexible sensor is proposed and developed. The upper part of the sensor is a temperature-sensitive unit completely covered with a hardened layer, which not only protects the temperature-sensitive material, but also reduces pressure interference. The lower part contains a truncated pyramid bump and four arrays of pressure-sensitive units to achieve three-dimensional force detection. When a three-dimensional force is applied on the sensor, four pressure-sensitive units undergo different resistance changes by which normal and shear components of the force can be decoupled. The temperature-sensitive unit is made of graphene/carbon nanotubes/silicone rubber (GR/CNT/SR) conductive nanocomposite and the pressure-sensitive units are made of graphene/silver nanoflakes/silicone rubber (GR/AgNF/SR) conductive nanocomposite. Experimental results show that force sensitivities of the sensor in x-, y- and z- directions are 1.694 N/V, 1.436 V/N and 0.435 N/V in the ranges of − 1.2–1.2 N, − 1.2–1.2 N and 0–7 N, respectively. The temperature sensitivity is 0.067 V/°C in the range of 25–90 °C. In addition, the sensor was mounted on a manipulator for daily grasping and results demonstrate good application potential.

Recent Development of Self-Powered Tactile Sensors Based on Ionic Hydrogels.

Hydrogels are three-dimensional polymer networks with excellent flexibility. In recent years, ionic hydrogels have attracted extensive attention in the development of tactile sensors owing to their unique properties, such as ionic conductivity and mechanical properties. These features enable ionic hydrogel-based tactile sensors with exceptional performance in detecting human body movement and identifying external stimuli. Currently, there is a pressing demand for the development of self-powered tactile sensors that integrate ionic conductors and portable power sources into a single device for practical applications. In this paper, we introduce the basic properties of ionic hydrogels and highlight their application in self-powered sensors working in triboelectric, piezoionic, ionic diode, battery, and thermoelectric modes. We also summarize the current difficulty and prospect the future development of ionic hydrogel self-powered sensors.

Machine Learning for Tactile Perception: Advancements, Challenges, and Opportunities

The past decades have seen the rapid development of tactile sensors in material, fabrication, and mechanical structure design. The advancement of tactile sensors has heightened the expectation of sensor functions, and thus put forward a higher demand for data processing. However, conventional analysis techniques have not kept pace with the tactile sensor development and still suffer from some severe drawbacks, like cumbersome models, poor efficiency, and expensive costs. Machine learning, with its prominent ability for big data analysis and fast processing speed, can offer many possibilities for tactile data analysis. Herein, the machine learning techniques employed for processing tactile signals are reviewed. Supervised learning and unsupervised learning for analog signals are covered, and processing spike signals with machine learning are summarized. Furthermore, the applications in robotic tactile perception and human activity monitoring are presented. Finally, the current challenges and future prospects in sensors, data, algorithms, and benchmarks are discussed.

An Atlas for the Inkjet Printing of Large-Area Tactile Sensors.

This review aims to discuss the inkjet printing technique as a fabrication method for the development of large-area tactile sensors. The paper focuses on the manufacturing techniques and various system-level sensor design aspects related to the inkjet manufacturing processes. The goal is to assess how printed electronics simplify the fabrication process of tactile sensors with respect to conventional fabrication methods and how these contribute to overcoming the difficulties arising in the development of tactile sensors for real robot applications. To this aim, a comparative analysis among different inkjet printing technologies and processes is performed, including a quantitative analysis of the design parameters, such as the costs, processing times, sensor layout, and general system-level constraints. The goal of the survey is to provide a complete map of the state of the art of inkjet printing, focusing on the most effective topics for the implementation of large-area tactile sensors and a view of the most relevant open problems that should be addressed to improve the effectiveness of these processes.

Guiding the design of superresolution tactile skins with taxel value isolines theory.

Tactile feedback is essential to make robots more agile and effective in unstructured environments. However, high-resolution tactile skins are not widely available; this is due to the large size of robust sensing units and because many units typically lead to fragility in wiring and to high costs. One route toward high-resolution and robust tactile skins involves the embedding of a few sensor units (taxels) into a flexible surface material and the use of signal processing to achieve sensing with superresolution accuracy. Here, we propose a theory for geometric superresolution to guide the development of tactile sensors of this kind and link it to machine learning techniques for signal processing. This theory is based on sensor isolines and allows us to compute the possible force sensitivity and accuracy in contact position and force magnitude as a spatial quantity before building a sensor. We evaluate the influence of different factors, such as elastic properties of the material, structure design, and transduction methods, using finite element simulations and by implementing real sensors. We empirically determine sensor isolines and validate the theory in two custom-built sensors with 1D and 2D measurement surfaces that use barometric units. Using machine learning methods to infer contact information, our sensors obtain an average superresolution factor of over 100 and 1200, respectively. Our theory can guide future tactile sensor designs and inform various design choices.

Target Classification Method of Tactile Perception Data with Deep Learning.

In order to improve the accuracy of manipulator operation, it is necessary to install a tactile sensor on the manipulator to obtain tactile information and accurately classify a target. However, with the increase in the uncertainty and complexity of tactile sensing data characteristics, and the continuous development of tactile sensors, typical machine-learning algorithms often cannot solve the problem of target classification of pure tactile data. Here, we propose a new model by combining a convolutional neural network and a residual network, named ResNet10-v1. We optimized the convolutional kernel, hyperparameters, and loss function of the model, and further improved the accuracy of target classification through the K-means clustering method. We verified the feasibility and effectiveness of the proposed method through a large number of experiments. We expect to further improve the generalization ability of this method and provide an important reference for the research in the field of tactile perception classification.

Liquid Metal-Based Wearable Tactile Sensor for Both Temperature and Contact Force Sensing

Wearable tactile sensors can be used for haptic perception and are widely utilized in soft robotics, intelligent prosthetics, and other human interfaced/interface applications. The development of tactile sensors with for multifunctional tactile sensing capacity remains a challenge. This article presents the design and fabrication of a wearable tactile sensor based on galinstan liquid metal for the simultaneous sensing of temperature and contact force independently. The decoupling of temperature and contact force sensing, which is based on the measured output voltage signals, is achieved by a designed Wheatstone bridge circuit and by the structural design of the fingerprint-patterned microfluidic channels and the top oval-shaped protrusion. Characterization tests show that the fabricated tactile sensor has a relatively high force sensing sensitivity of 0.32 N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> and its temperature sensing sensitivities are 0.41% °C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> at 20~50 °C and 0.21% °C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> at 50~80 °C. Two wearable tactile sensors are worn on the index finger and thumb of the human hand and are used to detect temperature changes and contact forces during grasping applications. The results show that the developed liquid metal-based tactile sensor has a high flexibility and durability, and can accurately measure the contact forces and temperatures simultaneously. Thus, the developed wearable tactile sensor has great potential for robotic manipulation and healthcare condition sensing in humans.

Touch Sensor Based on Flexible AlN Piezocapacitor Coupled With MOSFET

This paper presents tactile sensor devices based on flexible aluminum nitride (AlN) piezocapacitor coupled with the metal-oxide-semiconductor field-effect transistor (MOSFET). The AlN exhibits piezoelectric behavior without the typical requirement of high voltage for poling, and this makes it an ideal candidate for sensor, where the transducer layer is integrated with MOSFET. The AlN film used here was deposited on a polyimide substrate by room temperature RF sputtering to obtain flexible piezocapacitor. The film properties, such as orientation, roughness, elemental composition, and thickness, were investigated by the X-ray diffraction (XRD), atomic force microscopy (AFM), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscope (SEM), respectively. The tactile sensor developed by connecting the flexible AlN piezocapacitor in an extended gate configuration exhibited a sensitivity of 2.64 $\text{N}^{-1}$ for a force range 0.5-3.5 N. The developed sensor demonstrates a promising route toward the development of a complete CMOS compatible process for the development of tactile sensors.

Seedless Hydrothermal Growth of ZnO Nanorods as a Promising Route for Flexible Tactile Sensors.

Hydrothermal growth of ZnO nanorods has been widely used for the development of tactile sensors, with the aid of ZnO seed layers, favoring the growth of dense and vertically aligned nanorods. However, seed layers represent an additional fabrication step in the sensor design. In this study, a seedless hydrothermal growth of ZnO nanorods was carried out on Au-coated Si and polyimide substrates. The effects of both the Au morphology and the growth temperature on the characteristics of the nanorods were investigated, finding that smaller Au grains produced tilted rods, while larger grains provided vertical rods. Highly dense and high-aspect-ratio nanorods with hexagonal prismatic shape were obtained at 75 °C and 85 °C, while pyramid-like rods were grown when the temperature was set to 95 °C. Finite-element simulations demonstrated that prismatic rods produce higher voltage responses than the pyramid-shaped ones. A tactile sensor, with an active area of 1 cm2, was fabricated on flexible polyimide substrate and embedding the nanorods forest in a polydimethylsiloxane matrix as a separation layer between the bottom and the top Au electrodes. The prototype showed clear responses upon applied loads of 2–4 N and vibrations over frequencies in the range of 20–800 Hz.

Development of tactile sensor using stereo fisheye camera by rotation

In this paper, we propose a 3D coordinate measurement method using a stereo fisheye camera using rotation for tactile sensors.A soft haptic interface by an optical method using a camera has simple wiring and the like, and has a point that it does not impair the mechanical properties of a flexible outer skin and is strong against noise. On the other hand, since many ranges inside the sensor are used for sensing, there is a disadvantage that the usable range inside is narrow. In this paper, we propose a three-dimensional coordinate estimation method using the rotation of a fish-eye camera to expand the usable range inside the tactile sensor. Experimental results show the effectiveness of the proposed method.

Flexible Tactile Sensor Array for Slippage and Grooved Surface Recognition in Sliding Movement.

Flexible tactile sensor with contact force sensing and surface texture recognition abilities is crucial for robotic dexterous grasping and manipulation in daily usage. Different from force sensing, surface texture discrimination is more challenging in the development of tactile sensors because of limited discriminative information. This paper presents a novel method using the finite element modeling (FEM) and phase delay algorithm to investigate the flexible tactile sensor array for slippage and grooved surfaces discrimination when sliding over an object. For FEM modeling, a 3 × 3 tactile sensor array with a multi-layer structure is utilized. For sensor array sliding over a plate surface, the initial slippage occurrence can be identified by sudden changes in normal forces based on wavelet transform analysis. For the sensor array sliding over pre-defined grooved surfaces, an algorithm based on phase delay between different sensing units is established and then utilized to discriminate between periodic roughness and the inclined angle of the grooved surfaces. Results show that the proposed tactile sensor array and surface texture recognition method is anticipated to be useful in applications involving human-robotic interactions.

Multi‐Layered, Hierarchical Fabric‐Based Tactile Sensors with High Sensitivity and Linearity in Ultrawide Pressure Range

AbstractResistive tactile sensors based on changes in contact area have been extensively explored for a variety of applications due to their outstanding pressure sensitivity compared to conventional tactile sensors. However, the development of tactile sensors with high sensitivity in a wide pressure range still remains a major challenge due to the trade‐off between sensitivity and linear detection range. Here, a tactile sensor comprising stacked carbon nanotubes and Ni‐fabrics is presented. The hierarchical structure of the fabrics facilitates a significant increase in contact area between them under pressure. Additionally, a multi‐layered structure that can provide more contact area and distribute stress to each layer further improves the sensitivity and linearity. Given these advantages, the sensor presents high sensitivity (26.13 kPa−1) over a wide pressure range (0.2–982 kPa), which is a significant enhancement compared with the results obtained in previous studies. The sensor also exhibits outstanding performances in terms of response time, repeatability, reproducibility, and flexibility. Furthermore, meaningful applications of the sensor, including wrist‐pulse‐signal analysis, flexible keyboards, and tactile interface, are successfully demonstrated. Based on the facile and scalable fabrication technique, the conceptually simple but powerful approach provides a promising strategy to realize next‐generation electronics.

An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic, prosthetic, and industrial applications.