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A tactile image sensor employing a camera is capable of obtaining rich tactile information through image sequences with high spatial resolution. There have been many studies on the tactile image sensors from more than 30 years ago, and, recently, they have been applied in the field of robotics. Tactile image sensors can be classified into three typical categories according to the method of conversion from physical contact to light signals: Light conductive plate-based, marker displacement- based, and reflective membrane-based sensors. Other important elements of the sensor, such as the optical system, image sensor, and post-image analysis algorithm, have been developed. In this work, the literature is surveyed, and an overview of tactile image sensors employing a camera is provided with a focus on the sensing principle, typical design, and variation in the sensor configuration.

Multifunctional electronic skins have attracted considerable attention for soft electronics including humanoid robots, wearable devices, and health monitoring systems. Simultaneous detection of multiple stimuli in a single self‐powered device is desired to simplify artificial somatosensory systems. Here, inspired by the structure and function of human skin, an ultrasensitive self‐powered multimodal sensor is demonstrated based on an interlocked ferroelectric copolymer microstructure. The triboelectric and pyroelectric effects of ferroelectric microstructures enable the simultaneous detection of mechanical and thermal stimuli in a spacer‐free single device, overcoming the drawbacks of conventional devices, including complex fabrication, structural complexity, and high‐power consumption. Furthermore, the interlocked microstructure induces electric field localization during ferroelectric polarization, leading to enhanced output performance. The multimodal tactile sensor provides ultrasensitive pressure and temperature detection capability (2.2 V kPa−1, 0.27 nA °C−1) over a broad range (0.1–98 kPa, −20 °C < ΔT < 30 °C). Furthermore, multiple simultaneous stimuli can be distinguished based on different response times of triboelectric and pyroelectric effects. The remarkable performance of this sensor enables real‐time monitoring of pulse pressure, acoustic wave detection, surface texture analysis, and profiling of multiple stimuli. Inspired by the structure and function of human skin, an ultrasensitive self‐powered multimodal sensor is demonstrated based on an interlocked ferroelectric copolymer microstructure. The triboelectric and pyroelectric effects of ferroelectric microstructures enable the simultaneous detection of mechanical and thermal stimuli in a single device, demonstrating potential applications in humanoid robots, wearable devices, and healthcare systems.

Ultrathin and flexible electromagnetic shielding materials hold great potential in civil and military applications. Despite tremendous research efforts, the development of advanced shielding materials is still needed to provide additional functionalities for various artificial-intelligence-driven systems, such as tactile sensing ability. Herein, a layering design strategy is proposed to fabricate ultrathin Ti 3 C 2 T x MXene-aramid nanofiber (MA) films by a layer-by-layer assembling process. Compared to that of randomly mixed films, the designed MA films exhibited a higher EMI shielding efficiency at an ultrathin thickness of 9 µm, which increased from 26.4 to 40.7 dB, owing to the additional multiple-interface scattering mechanism. Importantly, the novel MA films displayed strong EMI shielding ability even after heating/cooling treatments within a wide temperature range of −196 to 300 °C. Moreover, the same material displayed a tensile strength of 124.1 ± 2.7 MPa and a toughness of 6.3 ± 1.1 MJ·m −3 , which are approximately 9.1 times and 45 times higher than those of pure MXene films, respectively. The MA film is also capable of detecting tactile signals via the triboelectric effect. A 2 × 4 tactile sensor array was developed to achieve an accurate signal catching capability. Therefore, in addition to the shielding performance, the manifestation of tactile perception by the MA films offers exciting opportunities in the fields of soft robotics and human-machine interactions.

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

The tactile sensation is an important indicator of the added value of a product, and it is thus important to be able to evaluate this sensation quantitatively. Sensory evaluation is generally used to quantitatively evaluate the tactile sensation of an object. However, statistical evaluation of the tactile sensation requires many participants and is, thus, time-consuming and costly. Therefore, tactile sensing technology, as opposed to sensory evaluation, is attracting attention. In establishing tactile sensing technology, it is necessary to estimate the tactile sensation of an object from information obtained by a tactile sensor. In this research, we developed a tactile sensor made of two-layer silicone rubber with two strain gauges in each layer and obtained vibration information as the sensor traced an object. We then extracted features from the vibration information using deep autoencoders, following the nature of feature extraction by neural firing due to vibrations perceived within human fingers. We also conducted sensory evaluation to obtain tactile scores for different words from participants. We finally developed a tactile sensation estimation model for each of the seven samples and evaluated the accuracy of estimating the tactile sensation of unknown samples. We demonstrated that the developed model can properly estimate the tactile sensation for at least four of the seven samples.

The absence of tactile perception limits the dexterity of a prosthetic hand and its acceptance by amputees. Recreating the sensing properties of the skin using a flexible tactile sensor could have profound implications for prosthetics, whereas existing tactile sensors often have limited functionality with cross‐interference. In this study, we propose a machine‐learning‐assisted multifunctional tactile sensor for smart prosthetics, providing a human‐like tactile sensing approach for amputations. This flexible sensor is based on a poly(3,4‐ethylenedioxythiophene): poly(styrene sulfonate) (PEDOT:PSS)–melamine sponge, which enables the detection of force and temperature with low cross‐coupling owing to two separate sensing mechanisms: the open‐circuit voltage of the sensor as a force‐insensitive intrinsic variable to measure the absolute temperature and the resistance as a temperature‐insensitive extrinsic variable to measure force. Furthermore, by analyzing the unsteady heat conduction and characterizing it using real‐time thermal imaging, we demonstrated that the process of open‐circuit voltage variation resulting from the unsteady heat conduction is closely correlated with the heat‐conducting capabilities of materials, which can be utilized to discriminate between substances. Assisted by the decision tree algorithm, the device is endowed with thermal conductivity sensing ability, which allows it to identify 10 types of substances with an accuracy of 94.7%. Furthermore, an individual wearing an advanced myoelectric prosthesis equipped with the above sensor can sense pressure, temperature, and recognize different materials. We demonstrated that our multifunctional tactile sensor provides a new strategy to help amputees feel force, temperature and identify the material of objects without the aid of vision. image

This paper reviews the existing vision-based tactile sensor (VBTS) designs reported in the literature. Although some reviews on VBTSs already exist in the literature. We believe it is necessary to review existing VBTS designs to formulate a guideline for developing such systems considering recent developments in the manufacturing and imaging technologies. Therefore, the main emphasis of this paper is to investigate current manufacturing trends and component selection criteria for developing a complete VBTS system. Further, the motivation behind this review is to identify the shortcomings in the current VBTS development technology and to furnish viable solutions to overcome such challenges. First, three different modalities of VBTSs are discussed: i) Waveguide-type designs, ii) marker-tracking based designs, and ii) reflective membrane designs. Next, a detailed discussion on various design aspects, like manufacturing, selection, and arrangements of the various sensor components, of the VBTSs is included. Then, a discussion on the validation/testing of various VBTSs is presented. Finally, based on the review, several challenges related to the development of VBTS are presented and the future research directions to overcome such challenges are recommended. This will serve the research community in determining the future research directions in the area of VBTS development.

Tactile sensors for robotic applications enhance the performance of robotic end-effectors as they ca n provide tactile information to operate various tasks. In particular, tactile sensors can measure multi-axial force and detect slip can aid the end-effectors in grasping diverse objects in an unstructured environment. We propose BaroTac, which measures three-axial forces and detects slip with a barometric pressure sensor chip (BPSC) for robotic applications. A BPSC is an off-the-shelf commercial sensor that is inexpensive, easy to customize, robust, and simple to use. While a single BPSC-based tactile sensor can measure pressure, an array of BPSC-based tactile sensors can measure multi-axial force through the reactivity of each sensor and detect slip by observing high frequency due to slip vibration. We first experiment with defining the fundamental characteristics of a single-cell BPSC-based sensor to set the design parameters of our proposed sensor. Thereafter, we suggest the sensing method of BaroTac: calibration matrix for three-axis force measurement and discrete wavelet transform (DWT) for slip detection. Subsequently, we validate the three-axis force measuring ability and slip detectability of the fabricated multi-cell BPSC-based tactile sensor. The sensor measures three-axis force with low error (0.14, 0.18, and 0.3% in the X-, Y- and Z-axis, respectively) and discriminates slip in the high-frequency range (75–150 Hz). We finally show the practical applicability of BaroTac by installing them on the commercial robotic gripper and controlling the gripper to grasp common objects based on our sensor feedback.

The resolution of contact location is important in many applications in robotics and automation. This is generally done by using an array of contact or tactile receptors, which increases cost and complexity as the required resolution or area is increased. Tactile sensors have also been developed using a continuous deformable medium between the contact and the receptors, which allows few receptors to interpolate the information among them, avoiding the weakness highlighted in the former approach. The latter is generally used to measure contact force intensity or magnitude but rarely used to identify the contact locations. This paper presents a systematic design and characterisation procedure for magnetic-based soft tactile sensors (utilizing the latter approach with the deformable contact medium) with the goal of locating the contact force location. This systematic procedure provides conditions under which design parameters can be selected, supported by a selected machine learning algorithm, to achieve the desired performance of the tactile sensor in identifying the contact location. An illustrative example, which combines a particular sensor configuration (magnetic hall effect sensor as the receptor, a selected continuous medium and a selected sensing resolution) and a specific data-driven algorithm, is used to illustrate the proposed design procedure. The results of the illustrative example design demonstrates the efficacy of the proposed design procedure and the proposed sensing strategy in identifying a contact location. The resulting sensor is also tested on a robotic hand (Allegro Hand, SimLab Co) to demonstrate its application in real-world scenarios.

Wearable human–machine interface (HMI) is an advanced technology that has a wide range of applications from robotics to augmented/virtual reality (AR/VR). In this study, an optically driven wearable human-interactive smart textile is proposed by integrating a polydimethylsiloxane (PDMS) patch embedded with optical micro/nanofibers (MNF) array with a piece of textiles. Enabled by the highly sensitive pressure dependent bending loss of MNF, the smart textile shows high sensitivity (65.5 kPa −1 ) and fast response (25 ms) for touch sensing. Benefiting from the warp and weft structure of the textile, the optical smart textile can feel slight finger slip along the MNF. Furthermore, machine learning is utilized to classify the touch manners, achieving a recognition accuracy as high as 98.1%. As a proof-of-concept, a remote-control robotic hand and a smart interactive doll are demonstrated based on the optical smart textile. This optical smart textile represents an ideal HMI for AR/VR and robotics applications. Graphical abstract