Flexible Tactile Sensor Research Articles

Terrain Classification Based on Spatiotemporal Multihead Attention With Flexible Tactile Sensors Array

Terrain Classification Based on Spatiotemporal Multihead Attention With Flexible Tactile Sensors Array

A tactile perception method with flexible grating structural color

Abstract Affordable high-resolution cameras and state-of-the-art computer vision techniques have led to the emergence of various vision-based tactile sensors. However, current vision-based tactile sensors mainly depend on geometric optics or marker tracking for tactile assessments, resulting in limited performance. To solve this dilemma, we introduce optical interference patterns as the visual representation of tactile information for flexible tactile sensors. We propose a novel tactile perception method and its corresponding sensor combining structural colors from flexible blazed gratings with deep learning. The richer structural colors and finer data processing foster the tactile estimation performance. The proposed sensor has an overall normal force magnitude accuracy of 6 mN, a planar resolution of 79 μm, and a contact-depth resolution of 25 μm. This work presents a promising tactile method that combines wave optics, soft materials, and machine learning. It demonstrates well in tactile measurement, and can be expanded into multiple sensing fields.

Braille Tactile-to-Auditory Conversion System Based on Self-Learning Flexible Tactile Sensor Array With Attention-Mechanism Model

Braille Tactile-to-Auditory Conversion System Based on Self-Learning Flexible Tactile Sensor Array With Attention-Mechanism Model

Microstructured Self-Healing Flexible Tactile Sensors Inspired by Bamboo Leaves.

Wearable electronic devices with multifunctions such as flexible, integrated, and self-powered play a crucial role in the fields of health monitoring, motion monitoring, and human-computer interaction. However, their core basic components, flexible pressure sensors, face challenges including poor long-term stability and insufficient real-time sensing accuracy. In order to solve the challenges of long-term, stable, and accurate sensing of the sensor, this paper prepares polydimethylsiloxane (SHPDMS) with intrinsic self-healing property and designs a high-sensitivity self-healing capacitive flexible pressure sensor with dual microstructures (grating microstructured electrodes and microporous dielectric layer) as the substrate based on SHPDMS. Specifically speaking, the self-healing of the sensor under mild conditions was realized by introducing reversible imine bonds with low bonding energy into the polydimethylsiloxane (PDMS) flexible substrate, which solved the problem of the material's long-term service durability. A grating-like microstructure was introduced into the flexible electrode by using a spotted bamboo taro leaf as a template, and a dual microstructure sensor was constructed by combining it with a microporous dielectric layer doped with single-walled carbon nanotubes. This way reduces the elastic modulus of the dielectric layer, improves the dielectric constant of the sensor under loading, and thus significantly improves the sensor's sensitivity and extends the measurement accuracy in a low-stress range. The prepared self-healing flexible sensor achieves a sensitivity of 3.6 kPa-1, a minimum detection limit of 5 Pa, a response recovery time of less than 80 ms, and stability over 5000 cycles, which exceeds most previously reported silicone rubber-based capacitive flexible sensors.

Flexible tactile sensing of magnetic hydrogel composites based on electrical impedance tomography

Flexible and stretchable tactile sensors show promising applications in robotics, prosthetics, and wearable electronics. This paper demonstrates an electrical impedance tomography (EIT) based highly flexible, touch-sensitive pressure sensor using magnetic hydrogels. EIT technology has been explored for continuous distributed strain sensing and tactile imaging. Magnetic hydrogel composites are synthesized by simultaneous polymerization and crosslinking of acrylamide with magnetic carbonyl iron particles (CIPs). The effects of CIPs percentage on morphology, sensitivity, and electrical/mechanical properties is investigated. EIT accurately discerns pressure quantity, shape, and spatial distribution. Lower CIPs volume fractions (3%) exhibit maximum sensitivity to strain fields. Mechanical testing show the hydrogels have excellent fatigue resistance, ideal for repeated compression loading. The results demonstrate the potential of EIT with magnetic hydrogel composites for tactile sensing and imaging, with applications in soft robotics, prosthetics, and wearable electronics. The tunable sensitivity and multifunctionality make these soft composites promising materials for flexible electronics.

Disentangling Contact Location for Stretchable Tactile Sensors from Soft Waveguide Ultrasonic Scatter Signals

Flexible tactile sensors have the ability to provide unparalleled levels of tactile sensation, including information regarding roughness, contact force, and contact location. However, it remains a challenge to achieve precise contact location sensing that is decoupled from sensor strain and touching forces. This paper proposes a novel data‐driven approach for force contact location sensing (FCLS) with the influence of sensor strain and forces based on scatter signals (SS) of the ultrasonic waveguide. First, the envelope of the force contact scatter signal (FCSS) is extracted via the Hilbert transform, which retrieves the global features of SS. The time‐frequency spectrogram is obtained via continuous wavelet transform, which extracts the local features of SS. Second, a deep convolutional neural network (CNN) is utilized to extract these features separately and concentrate them together. Third, based on the outputs of the CNN, a multilayer perception regression model is applied to acquire the force contact location. The experimental results indicate that the accuracy of the proposed FCLS method has a mean absolute error of 0.627 mm and a mean relative error of 3.19%. This research provides a foundation for further multimodal sensing using ultrasonic waveguides and its application in robotic sensing.

Flexible Triboelectric Sensor based on Catalyst‐Diffusion Self‐Encapsulated Conductive Liquid‐Metal‐Silicone Ink for Somatosensory Soft Robotic System

AbstractThe combination of fluidity and metallic conductivity has attracted considerable attention to liquid metal (LM), but its development remains challenging due to enormous surface tension. Here, vinyl‐terminated silicone oil and platinum catalyst are added to LM to reduce its surface tension, which develops a special type of liquid‐metal‐silicone (LMS) ink with a catalyst diffusion effect. Combined with an embedded three‐dimentional (3D) printing method, the LMS ink is printed on the support matrix, and the catalyst diffuses outward along the print path to cure the silicone around it, directly constructing self‐encapsulated conductive composites with excellent conductivity and self‐encapsulated flexible tactile sensors based on triboelectric nanogenerator (TENG). The sensor exhibits excellent sensitivity (0.308 V kPa−1), high linearity (≈0.99), and good durability (over 10 000 cycles). Furthermore, when used in flexible wearable electronics, the sensor demonstrates a good performance with an accuracy of ≈96% in classifying different human postures using a convolutional neural network. Finally, through embedded 3D printing with LMS ink and silicone ink, a somatosensory soft robotic gripper with complex cavity structures is designed and manufactured in one step, achieving the all‐in‐one integration of sensors and actuators. This study shows great application potential in flexible electronics and soft robotic systems.

Investigation on voltage response and application of flexible tactile sensors based on multiple cooperative structures

Recently, flexible tactile sensors (FTSs) have gained great attention for their application prospects in human–machine interfaces and robots. However, there is a lack of effective and practical approaches to study the response and guide the design of FTSs. Establishing a valid analytical model and revealing the effect mechanism remain challengeable at present. Herein, an analytical model of an FTS based on multiple cooperative structures (FTS-MCS) is developed. The model is validated with numerical and experimental results. The relative error between the analytical and experimental results for the compression and recovery are 9.3% and 3.8%, respectively. The effect of structure parameters, including helix coil and permanent magnet, is investigated based on the analytical model. The results show that the voltage of FTS-MCS can be improved by tuning the turn, diameter, and shape of coils and the remanent flux density and dimensions of the permanent magnet. The maximum voltage is improved by 49.8% as the turn of coils grows four. The effect of the load conditions of the compression deformation and compression speed is also revealed. Then, a sensitivity analysis is performed to quantitatively analyze the effect of different parameters. Results show the diameter of coils and the compression deformation have the greatest effect on the voltage of FTS-MCS. Due to the flexibility, adaptability, and tactility, such FTS-MCS are mounted on a double-fingered claw to endow the claw with self-perception capacity. Consequently, our study gives a comprehensive understanding of such FTS-MCS and is conducive to promoting the application of FTS-MCS.

A compressible high-sensitivity flexible sensor array for real-time motion artifact detection in magnetic resonance imaging

Magnetic Resonance Imaging (MRI) serves as a critical tool in modern medical diagnosis, and its accuracy is directly linked to patient treatment and recovery. However, unintentional patient movement during MRI scans often results in motion artifacts on the images, significantly compromising the precision of diagnosis. Traditional solutions typically involve identifying and addressing these issues after the scanning sequence is completed, leading to significant resource waste and delays in patient treatment. Here, we propose a flexible tactile sensor array system based on triboelectric nanogenerators (TENGs) for real-time monitoring of head motion artifacts in MRI. The unique design of the tactile sensor structure, operating in a dual-electrode mode, maintains high sensitivity and a wide response range, even with a minimal surface area. The advantage of TENG is the wide range of material choices, ensuring that a proper selection will not interfere with MRI during application. By integrating TENG sensor technology and control program systems, this solution provides a novel approach for monitoring head motion artifacts in MRI. On the basis of ensuring patient comfort, it has saved the time and money costs of monitoring, improved the efficiency of detection, and demonstrated significant potential in the field of medical imaging.

Modulating the doping level of conductive polymers for all polymer-based triboelectric self-powered sensors

Triboelectric self-powered sensors, utilizing flexible conductive polymers in place of traditional metal electrodes, provide improved flexibility and reduced weight, making them highly promising for next-generation Internet of Things sensing applications. Here we demonstrate that ionic liquid (IL) additives notably enhance the mechanical and electrical properties of conductive polymer (PEDOT:PSS) films, optimizing their triboelectric performance. Results show that IL as molecular dopant induces structural change in PEDOT, thereby enhancing charge delocalization and doping levels. These IL-doped PEDOT:PSS films achieved high conductivity, significantly elevated surface potential, and a fourfold increase in output triboelectric voltage compared to the neat film. Additionally, flexible all-polymer tactile sensors with high sensitivity (3.6 V/kPa) were fabricated, demonstrating potential for diverse applications such as activity signal detection and human–machine interfaces.

Electrical Impedance Tomography-Based Electronic Skin for Multi-Touch Tactile Sensing Using Hydrogel Material and FISTA Algorithm.

Flexible electronic skin (e-skin) can enable robots to have sensory forms similar to human skin, enhancing their ability to obtain more information from touch. The non-invasive nature of electrical impedance tomography (EIT) technology allows electrodes to be arranged only at the edges of the skin, ensuring the stretchability and elasticity of the skin's interior. However, the image quality reconstructed by EIT technology has deteriorated in multi-touch identification, where it is challenging to clearly reflect the number of touchpoints and accurately size the touch areas. This paper proposed an EIT-based flexible tactile sensor that employs self-made hydrogel material as the primary sensing medium. The sensor's structure, fabrication process, and tactile imaging principle were elaborated. To improve the quality of image reconstruction, the fast iterative shrinkage-thresholding algorithm (FISTA) was embedded into the EIDORS toolkit. The performances of the e-skin in aspects of assessing the touching area, quantitative force sensing and multi-touch identification were examined. Results showed that the mean intersection over union (MIoU) of the reconstructed images was improved up to 0.84, and the tactile position can be accurately imaged in the case of the number of the touchpoints up to seven (larger than two to four touchpoints in existing studies), proving that the combination of the proposed sensor and imaging algorithm has high sensitivity and accuracy in multi-touch tactile sensing. The presented e-skin shows potential promise for the application in complex human-robot interaction (HRI) environments, such as prosthetics and wearable devices.

Advanced Optical‐Thermal Integrated Flexible Tactile Sensor for High‐Fine Recognition of Liquid Property in Non‐Contact Mode

AbstractFlexible sensors have been applied to accurately identify the basic features of various solid objects. However, a significant obstacle is the accurate identification of liquids, due to it is impossible to exert force directly on those. Here, an optical‐thermal flexible tactile sensor is designed for liquid recognition, which integrates PEDOT:PSS/SrTiO3 thermoelectric foam and ZnS‐CaZnOS mechanoluminescent (ML) film coated with thermoplastic polyurethane (TPU), enabling the simultaneous detection of thermal and optical changes with low interference, via its exceptional mechanical stimuli‐caused light and superior thermoelectric voltage, respectively. In addition, the TPU coating imparts the device with excellent hydrophobic and oleophobic properties, enhancing its durability in complex conditions. This flexible sensor, possessing high stability under thermal and mechanical cycles, can distinguish different pure solvents and turbid suspensions, achieving a high‐fine recognition accuracy of 95% and 97.5%, respectively, with the aid of the fusion algorithm of multimodal sensory data based on the thermal and optical outputs. This capability of the device can underscore the practical utility of humanoid robots to identify hazardous liquids in some scenarios like safety inspection and kitchen condiments, which addresses the critical gaps in current sensory technologies by providing a robust, sensitive, and multifunctional strategy for accurate liquid recognition.

A Solid-Liquid Composite Flexible Bionic Three-Axis Tactile Sensor for Dexterous Hands

A Solid-Liquid Composite Flexible Bionic Three-Axis Tactile Sensor for Dexterous Hands

Electromechanically stable and flexible poro-hyperelastic composites for multimodal robotic tactile sensing

The expansion of automated production places increased demands on intelligent sensory networks for collecting and processing various input signals, which necessitate higher standards for multimodal detection, sensitivity, and sensing response speed. Here, this work developed a poro-hyperelastic composites-based multimodal tactile sensor capable of triboelectric, piezoresistive, and capacitive modes across multiple strains and frequency domains. Such the multilayered flexible multimodal mechano-electronic sensor (MMES) is constructed from a carbon nanotubes porous sponge (CNPS), facilitating switching between trimodal sensing modes integrated within a single sensor structure, effectively enhancing the electromechanical robustness of the sensory system. The present MMES sensor exhibits excellent pressure sensing performances, with a wide range (0–20 kPa), fast response (less than 102 ms in piezoresistive mode and less than 62 ms in capacitive mode), high sensitivity (up to 0.12 V/kPa⁻1 in triboelectric mode), and long-term durability (exceeding 10,000 cycles). Moreover, a micro-CT generated 3D model was established using finite element analysis (FEA) to investigate porous hyperelastic behaviours of the CNPS composites, in agreement with experimental data. The results demonstrated that the MMES sensor is particularly suitable for dynamic feedback in robotic arms, allowing production lines to make accurate distinguishments and operations by sensing distinct materials and resistances encountered during assembly in real time. The outcomes offer promising applications of multimodal robotic tactile sensing for human-machine interaction.

Nanocellulose and multi-walled carbon nanotubes reinforced polyacrylamide/sodium alginate conductive hydrogel as flexible sensor

Conductive hydrogels have been widely applied in human–computer interaction, tactile sensing, and sustainable green energy harvesting. Herein, a double cross-linked network composite hydrogel (MWCNTs/CNWs/PAM/SA) by constructing dual enhancers acting together with PAM/SA was constructed. By systematically optimizing the compositions, the hydrogel displayed features advantages of good mechanical adaptability, high conductivity sensitivity (GF = 5.65, 53 ms), low hysteresis (<11 %), and shape memory of water molecules and temperature. The nanocellulose crystals (CNWs) were bent and entangled with the backbone of the polyacrylamide/ sodium alginate (PAM/SA) hydrogel network, which effectively transferred the external mechanical forces to the entire physical and chemical cross-linking domains. Multi-walled carbon nanotubes (MWCNTs) were filled into the cross-linking network of the hydrogel to enhance the conductivity of the hydrogel effectively. Notably, hydrogels are designed as flexible tactile sensors that can accurately recognize and monitor electrical signals from different gesture movements and temperature changes. It was also assembled as a friction nanogenerator (TENG) that continuously generates a stable open circuit voltage (28 V) for self-powered small electronic devices. This research provides a new prospect for designing nanocellulose and MWCNTs reinforced conductive hydrogels via a facile method.

Multimodal tactile sensing fused with vision for dexterous robotic housekeeping

As robots are increasingly participating in our daily lives, the quests to mimic human abilities have driven the advancements of robotic multimodal senses. However, current perceptual technologies still unsatisfied robotic needs for home tasks/environments, particularly facing great challenges in multisensory integration and fusion, rapid response capability, and highly sensitive perception. Here, we report a flexible tactile sensor utilizing thin-film thermistors to implement multimodal perceptions of pressure, temperature, matter thermal property, texture, and slippage. Notably, the tactile sensor is endowed with an ultrasensitive (0.05 mm/s) and ultrafast (4 ms) slip sensing that is indispensable for dexterous and reliable grasping control to avoid crushing fragile objects or dropping slippery objects. We further propose and develop a robotic tactile-visual fusion architecture that seamlessly encompasses multimodal sensations from the bottom level to robotic decision-making at the top level. A series of intelligent grasping strategies with rapid slip feedback control and a tactile-visual fusion recognition strategy ensure dexterous robotic grasping and accurate recognition of daily objects, handling various challenging tasks, for instance grabbing a paper cup containing liquid. Furthermore, we showcase a robotic desktop-cleaning task, the robot autonomously accomplishes multi-item sorting and cleaning desktop, demonstrating its promising potential for smart housekeeping.

Flexible Arc-Shaped Micro-Fiber Bragg Grating Array Three-Dimensional Tactile Sensor for Fingertip Signals Detection and Human Pulse Monitoring.

A flexible arc-shaped micro-Fiber Bragg Grating (mFBG) array three-dimensional tactile sensor for fingertip signal detection and human pulse monitoring is presented. It is based on a three mFBGs array which is embedded in an arc-shaped poly (dimethylsiloxane) (PDMS) elastomer, which can effectively discriminate the normal force, left force, and right force by monitoring the reflected intensity variation of the three mFBGs. Different from the traditional FBG sensors, this sensor measures force by detecting changes in light intensity, effectively avoiding the wavelength cross-sensitivity impact of temperature variations on the sensor performance. This design strategy simplifies the sensor structure, reduces the system complexity and signal interrogation cost, and enhances reliability and practicality. Through systematic experiments, we successfully validated the sensor's superior performance, achieving a minimum detection force of 0.01 N and providing robust data support for practical applications. In addition, the sensor has been used to monitor human pulse accurately. The successful fabrication and experimental validation of this sensor lay a foundation for its widespread application in fields such as robot perception and human vital signal detection.

Morse Code Recognition Based on a Flexible Tactile Sensor with Carbon Nanotube/Polyurethane Sponge Material by the Long Short-Term Memory Model.

Morse code recognition plays a very important role in the application of human-machine interaction. In this paper, based on the carbon nanotube (CNT) and polyurethane sponge (PUS) composite material, a flexible tactile CNT/PUS sensor with great piezoresistive characteristic is developed for detecting Morse code precisely. Thirty-six types of Morse code, including 26 letters (A-Z) and 10 numbers (0-9), are applied to the sensor. Each Morse code was repeated 60 times, and 2160 (36 × 60) groups of voltage time-sequential signals were collected to construct the dataset. Then, smoothing and normalization methods are used to preprocess and optimize the raw data. Based on that, the long short-term memory (LSTM) model with excellent feature extraction and self-adaptive ability is constructed to precisely recognize different types of Morse code detected by the sensor. The recognition accuracies of the 10-number Morse code, the 26-letter Morse code, and the whole 36-type Morse code are 99.17%, 95.37%, and 93.98%, respectively. Meanwhile, the Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) models are built to distinguish the 36-type Morse code (letters of A-Z and numbers of 0-9) based on the same dataset and achieve the accuracies of 91.37%, 88.88%, 87.04%, and 90.97%, respectively, which are all lower than the accuracy of 93.98% based on the LSTM model. All the experimental results show that the CNT/PUS sensor can detect the Morse code's tactile feature precisely, and the LSTM model has a very efficient property in recognizing Morse code detected by the CNT/PUS sensor.

Innovations in Tactile Sensing: Microstructural Designs for Superior Flexible Sensor Performance

AbstractTactile sensors have garnered considerable interest for their capacity to detect and quantify tactile information. The incorporation of microstructural designs into flexible tactile sensors has emerged as a potent strategy to augment their sensitivity to pressure variations, thereby enhancing their linearity, response spectrum, and mechanical robustness. This review underscores the imperative for progress in microstructured flexible tactile sensors. Subsequently, the discourse transitions to the prevalent materials employed in the fabrication of sensor electrodes, encapsulation layers, and active sensing mediums, elucidating their merits and limitations. In‐depth discussions are devoted to tactile sensors adorned with microstructures, including but not limited to, micropyramids, microhemispheres, micropillars, microporous configurations, microcracks, topological interconnections, multilevel constructs, random roughness, biomimetic microstructures inspired by flora and fauna, accompanied by exemplar studies from each category. Moreover, the utility of flexible tactile sensors within the realm of intelligent environments is explicated, highlighting their application in the monitoring of physiological signals, the detection of sliding motions, and the discernment of surface textures. The review culminates in a critical examination of the paramount challenges and predicaments that must be surmounted to further the development and enhance the functional performance of tactile sensors, paving the way for their integration into advanced sensory systems.

Neuromorphic Computing-Assisted Triboelectric Capacitive-Coupled Tactile Sensor Array for Wireless Mixed Reality Interaction.

Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.