Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review

Recent Developments and Applications of Tactile Sensors with Biomimetic Microstructures

Humans possess an innate ability to perceive a wide range of objects through touch, which allows them to interact effectively with their surroundings. Similarly, tactile perception in artificial sensory systems enables the acquisition of object properties, human physiological signals, and environmental information. Biomimetic tactile sensors, as an emerging sensing technology, draw inspiration from biological systems and exhibit high sensitivity, rapid response, multimodal perception, and stability. By mimicking biological mechanisms and microstructures, these sensors achieve precise detection of mechanical signals, thereby paving the way for advancements in tactile sensing applications. This review provides an overview of key sensing mechanisms, microstructure designs, and advanced fabrication techniques of biomimetic tactile sensors. The system architecture design of biomimetic tactile sensing systems is also explored. Furthermore, the review highlights significant applications of these sensors in recent years, including texture recognition, human health detection, and human-machine interaction. Finally, the key challenges and future development prospects related to biomimetic tactile sensors are discussed.

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.

Biometric-Tuned E-Skin Sensor with Real Fingerprints Provides Insights on Tactile Perception: Rosa Parks Had Better Surface Vibrational Sensation than Richard Nixon

The dense mechanoreceptors in human fingertips enable texture discrimination. Recent advances in flexible electronics have created tactile sensors that effectively replicate slowly adapting (SA) and rapidly adapting (RA) mechanoreceptors. However, the influence of dermatoglyphic structures on tactile signal transmission, such as the effect of fingerprint ridge filtering on friction-induced vibration frequencies, remains unexplored. A novel multi-layer flexible sensor with an artificially synthesized skin surface capable of replicating arbitrary fingerprints is developed. This sensor simultaneously detects pressure (SA response) and vibration (RA response), enabling texture recognition. Fingerprint ridge patterns from notable historical figures - Rosa Parks, Richard Nixon, Martin Luther King Jr., and Ronald Reagan - are fabricated on the sensor surface. Vibration frequency responses to assorted fabric textures are measured and compared between fingerprint replicas. Results demonstrate that fingerprint topography substantially impacts skin-surface vibrational transmission. Specifically, Parks' fingerprint structure conveyed higher frequencies more clearly than those of Nixon, King, or Reagan. This work suggests individual fingerprint ridge morphological variation influences tactile perception and can confer adaptive advantages for fine texture discrimination. The flexible bioinspired sensor provides new insights into human vibrotactile processing by modeling fingerprint-filtered mechanical signals at the finger-object interface.

Research on Adaptive Grasping with a Prosthetic Hand Based on Perceptual Information on Hardness and Surface Roughness

In order to solve the problems of methods that use a single form of sensing, the ease of causing deformation damage to the targets with a low hardness during grasping, and the slow sliding inhibition of a prosthetic hand when the grasping target slides, which are problems that exist in most current intelligent prosthetic hands, this study introduces an adaptive control strategy for prosthetic hands based on multi-sensor sensing. Using a force-sensing resistor (FSR) to collect changes in signals generated after contact with a target, a prosthetic hand can classify the target's hardness level and adaptively provide the desired grasping force so as to reduce the deformation of and damage to the target in the process of grasping. A fiber-optic sensor collects the light reflected by the object to identify its surface roughness, so that the prosthetic hand adaptively adjusts the sliding inhibition method according to the surface roughness information to improve the grasping efficiency. By integrating information on the hardness and surface roughness of the target, an adaptive control strategy for a prosthetic hand is proposed. The experimental results showed that the adaptive control strategy was able to reduce the damage to the target by enabling the prosthetic hand to achieve stable grasping; after grasping the target with an initial force and generating sliding, the efficiency of slippage inhibition was improved, the target could be stably grasped in a shorter time, and the hardness, roughness and weight ranges of targets that could be grasped by the prosthetic hand were enlarged, thus improving the success rate of stable grasping under extreme conditions.

Growth of Wide-Bandgap Monolayer Molybdenum Disulfide for a Highly Sensitive Micro-Displacement Sensor

Two-dimensional (2D) piezoelectric semiconductor materials are garnering significant attention in applications such as intelligent sensing and energy harvesting due to their exceptional physical and chemical properties. Among these, molybdenum disulfide (MoS2), a 2D wide-bandgap semiconductor, exhibits piezoelectricity in odd-layered structures due to the absence of an inversion symmetry center. In this study, we present a straightforward chemical vapor deposition (CVD) technique to synthesize monolayer MoS2 on a Si/SiO2 substrate, achieving a lateral size of approximately 50 µm. Second-harmonic generation (SHG) characterization confirms the non-centrosymmetric crystal structure of the wide-bandgap MoS2, indicative of its piezoelectric properties. We successfully transferred the triangular MoS2 to a polyethylene terephthalate (PET) flexible substrate using a wet-transfer method and developed a wide-bandgap MoS2-based micro-displacement sensor employing maskless lithography and hot evaporation techniques. Our testing revealed a piezoelectric response current of 5.12 nA in the sensor under a strain of 0.003% along the armchair direction of the monolayer MoS2. Furthermore, the sensor exhibited a near-linear relationship between the piezoelectric response current and the strain within a displacement range of 40-100 µm, with a calculated response sensitivity of 1.154 µA/%. This research introduces a novel micro-displacement sensor, offering potential for advanced surface texture sensing in various applications.

JamTac: A Tactile Jamming Gripper for Searching and Grasping in Low-Visibility Environments

Humans can feel and grasp efficiently in the dark through tactile feedback, whereas it is still a challenging task for robots. In this research, we create a novel soft gripper named JamTac, which has high-resolution tactile perception, a large detection surface, and integrated sensing-grasping capability that can search and grasp in low-visibility environments. The gripper combines granular jamming and visuotactile perception technologies. Using the principle of refractive index matching, a refraction-free liquid-particle rationing scheme is developed, which makes the gripper itself to be an excellent tactile sensor without breaking its original grasping capability. We simultaneously acquire color and depth information inside the gripper, making it possible to sense the shape, texture, hardness, and contact force with high resolution. Experimental results demonstrate that JamTac can be a promising tool to search and grasp in situations when vision is not available.

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems.

Emerging Functional Polymer Composites for Tactile Sensing

In recent years, extensive research has been conducted on the development of high-performance flexible tactile sensors, pursuing the next generation of highly intelligent electronics with diverse potential applications in self-powered wearable sensors, human-machine interactions, electronic skin, and soft robotics. Among the most promising materials that have emerged in this context are functional polymer composites (FPCs), which exhibit exceptional mechanical and electrical properties, enabling them to be excellent candidates for tactile sensors. Herein, this review provides a comprehensive overview of recent advances in FPCs-based tactile sensors, including the fundamental principle, the necessary property parameter, the unique device structure, and the fabrication process of different types of tactile sensors. Examples of FPCs are elaborated with a focus on miniaturization, self-healing, self-cleaning, integration, biodegradation, and neural control. Furthermore, the applications of FPC-based tactile sensors in tactile perception, human-machine interaction, and healthcare are further described. Finally, the existing limitations and technical challenges for FPCs-based tactile sensors are briefly discussed, offering potential avenues for the development of electronic products.

Ultralow-Latency Textile Sensors for Wearable Interfaces with a Human-in-Loop Sensing Approach

The evolution of wearable technologies has led to the development of novel types of sensors customized for a wide range of applications. Wearable sensors need to possess a low form factor and be ergonomic, causing minimal impediment of the user's natural movement. Various principles have been explored to meet these requirements, ranging from optical, magnetic, resistive flex sensing to 3D printed sensors and liquid metals such as those using eutectic gallium-indium. However, manufacturing techniques for most current wearable sensors tend to be complex and difficult to scale. Challenges also exist in achieving high sensitivity with noise resistance and robustness to false detections, especially in capacitive sensors. In this research, a novel ultralow-latency soft tactile and pressure sensor developed using off-the-shelf e-textiles is proposed, which overcomes some of these limitations. The sensor does not use any specialized equipment or materials for manufacture. A human-in-loop (HIL) sensing technique is demonstrated, which provides high sensitivity, high sensing bandwidth, as well as ultralow latency, which makes it ideal as a wearable input device. In addition, the HIL method provides other advantages such as high noise rejection and resistance to accidental triggers that could be caused by other humans or environmental factors owing to its high signal to noise ratio. Finally, two applications-a wearable keyboard and gaming input device-were demonstrated using these sensors.

Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

Background

People use their hands to perform sophisticated tasks like playing a musical instrument by integrating manifold and diverse sensations of touch with motor control strategies. In contrast, prosthetic hands lack the capacity for multichannel haptic feedback and multitasking functionality remains rudimentary. There is a dearth of research exploring the potential of upper limb absent (ULA) people to integrate multiple channels of haptic feedback into dexterous prosthetic hand control strategies.

Methods

In this paper, we designed a novel experimental paradigm for three ULA people and nine additional subjects to investigate their ability to integrate two simultaneously activated channels of context-specific haptic feedback into their dexterous artificial hand control strategies. Artificial neural networks (ANN) were designed for pattern recognition of the array of efferent electromyogram signals that controlled the dexterous artificial hand. ANNs were also used to classify the directions that objects were sliding across two tactile sensor arrays on the index (I) and little (L) fingertips of the robotic hand. The direction of sliding contact at each robotic fingertip was encoded by different stimulation frequencies of wearable vibrotactile actuators for haptic feedback. The subjects were tasked with implementing different control strategies with each finger simultaneously depending upon the perceived directions of sliding contact. This required the 12 subjects to concurrently control individual fingers of the artificial hand by successfully interpreting two channels of simultaneously activated context-specific haptic feedback.

Results

Subjects were able to accomplish this complex feat of multichannel sensorimotor integration with an overall accuracy of 95.53% ± 0.23%. While there was no statistically significant difference in the classification accuracy between ULA people and the other subjects, the ULA people required more time to correctly respond to the simultaneous haptic feedback slip signals, suggesting a higher cognitive load required by the ULA people.

Conclusion

ULA people can integrate multiple channels of simultaneously activated and nuanced haptic feedback with their control of individual fingers of an artificial hand. These findings provide a step toward empowering amputees to multitask with dexterous prosthetic hands, which remains an ongoing challenge.

Dynamics of Sliding Friction between Laser-Induced Periodic Surface Structures (LIPSS) on Stainless Steel and PMMA Microspheres

In this work, we investigated the sliding friction measured between poly(methyl methacrylate) (PMMA) colloidal probes with two different diameters D (1.5 and 15 μm) and laser-induced periodic surface structures (LIPSS) on stainless steel with periodicities Λ of 0.42 and 0.9 μm, when the probes are elastically driven along two directions, perpendicular and parallel to the LIPSS. The time evolution of the friction shows the characteristic features of a reverse stick-slip mechanism recently reported on periodic gratings. The morphologies of colloidal probes and modified steel surfaces are geometrically convoluted in the atomic force microscopy (AFM) topographies simultaneously recorded with the friction measurements. The LIPSS periodicity is only revealed with smaller probes (D = 1.5 μm) and when Λ takes the largest value of 0.9 μm. The average value of the friction force is found to be proportional to the normal load, with a coefficient of friction μ varying between 0.23 and 0.54. The values of μ are rather independent of the direction of motion, and they reach their maximum when the small probe is scanned on the LIPSS with the larger periodicity. The friction is also found to decrease with increasing velocity in all cases, which is attributed to the corresponding decrease of the viscoelastic contact time. These results can be used to model the sliding contacts formed by a set of spherical asperities of different sizes driven on a rough solid surface.

An SMA Transducer for Sensing Tactile Sensation Focusing on Stroking Motion

The authors have developed a micro-vibration actuator using filiform SMA wire electrically driven by periodic electric current. While applying the SMA actuators to tactile displays, we discovered a phenomenon that the deformation caused by a given stress to an SMA wire generated a change in the electrical resistance. With this characteristic, the SMA wire works as a micro-force sensor with high sensitivity, while generating micro-vibration. In this paper, the micro-force sensing ability of an SMA transducer is described and discussed. Experiments are conducted by sliding the SMA sensor on the surface of different objects with different speeds, and the sensing ability is evaluated to be related with human tactile sensation.

BaroTac: Barometric Three-Axis Tactile Sensor with Slip Detection Capability

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.

3D-Printed Soft Sensors for Adaptive Sensing with Online and Offline Tunable Stiffness

The stiffness of a soft robot with structural cavities can be regulated by controlling the pressure of a fluid to render predictable changes in mechanical properties. When the soft robot interacts with the environment, the mediating fluid can also be considered an inherent information pathway for sensing. This approach to using structural tuning to improve the efficacy of a sensing task with specific states has not yet been well studied. A tunable stiffness soft sensor also renders task-relevant contact dynamics in soft robotic manipulation tasks. This article proposes a type of adaptive soft sensor that can be directly three-dimensional printed and controlled using pneumatic pressure. The tunability of such a sensor helps to adjust the sensing characteristics to better capturing specific tactile features, demonstrated by detecting texture with different frequencies. We present the design, modeling, Finite Element Simulation, and experimental characterization of a single unit of such a tunable stiffness sensor. How the sensing characteristics are affected by adjusting its stiffness is studied in depth. In addition to the tunability, the results show that such types of adaptive sensors exhibit good sensitivity (up to 2.6 KPa/N), high sensor repeatability (average std <0.008 KPa/N), low hysteresis (<6%), and good manufacturing repeatability (average std = 0.0662 KPa/N).

Application of High-Photoelasticity Polyurethane to Tactile Sensor for Robot Hands

We developed a tactile sensor for robot hands that can measure normal force (F_Z) and tangential forces (F_X and F_Y) using photoelasticity. This tactile sensor has three photodiodes and three light-emitting diode (LED) white light sources. The sensor is composed of multiple elastic materials, including a highly photoelastic polyurethane sheet, and the sensor can detect both normal and tangential forces through the deformation, ben sding, twisting, and extension of the elastic materials. The force detection utilizes the light scattering resulting from birefringence.

Using Novel Multi-Frequency Analysis Methods to Retrieve Material and Temperature Information in Tactile Sensing Areas

This article presents a novel artificial skin technology based on the Electric Impedance Tomography (EIT) that employs multi-frequency currents for detecting the material and the temperature of objects in contact with piezoresistive sheets. To date, few artificial skins in the literature are capable of detecting an object's material, e.g., wood, skin, leather, or plastic. EIT-based artificial skins have been employed mostly to detect the position of the contact but not its characteristics. Thanks to multi-frequency currents, our EIT-based artificial skin is capable of characterising the spectral profile of objects in contact and identifying an object's material at ambient temperature. Moreover, our model is capable of detecting several levels of temperature (from -10 up to 60 °C) and can also maintain a certain accuracy for material identification. In addition to the known capabilities of EIT-based artificial skins concerning detecting pressure and location of objects, as well as being low cost, these two novel modalities demonstrate the potential of EIT-based artificial skins to achieve global tactile sensing.

Review of Learning-Based Robotic Manipulation in Cluttered Environments

Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.

Static Tactile Sensing Based on Electrospun Piezoelectric Nanofiber Membrane

Here, a static tactile sensing scheme based on a piezoelectric nanofiber membrane, prepared via the electrospinning method, is presented. When the nanofiber membrane is kept under a constant vibration, an external contact onto the membrane will attenuate its vibration. By monitoring this change in the oscillation amplitude due to the physical contact via the piezoelectrically coupled voltage from the nanofiber membrane, the strength and duration of the static contact can be determined. The proof-of-concept experiment demonstrated here shows that the realization of a static tactile sensor is possible by implementing the piezoelectric nanofiber membrane as an effective sensing element.

Performance Analysis of a CSFH-Based Microgripper: Analytical Modeling and Simulation

Microgrippers are promising tools for micro-manipulation and characterization of cells. In this paper, a biocompatible electro-thermally actuated microgripper with rotary capacitive position sensor is presented. To overcome the limited displacement possibilities usually provided by electrothermal actuators and to achieve the desired tweezers output displacement, conjugate surface flexure hinges (CSFH) are adopted. The microgripper herein reported can in principle manipulate biological samples in the size range between 15 and 120 µm. A kinematics modeling approach based on the pseudo-rigid-body-method (PRBM) is applied to describe the microgripper's working mechanism, and analytical modeling, based on finite elements method (FEM), is used to optimize the electrothermal actuator design and the heat dissipation mechanism. Finally, FEM-based simulations are carried out to verify the microgripper, the electrothermal actuator and heat dissipation mechanism performance, and to assess the validity of the analytical modeling.

Self-Powered Active Sensing Based on Triboelectric Generators

The demand for portable and wearable chemical or biosensors and their expeditious development in recent years has created a scientific challenge in terms of their continuous powering. As a result, mechanical energy harvesters such as piezoelectric and triboelectric generators (TEGs) have been explored recently either as sensors or harvesters to store charge in small, but long-life, energy-storage devices to power the sensors. The use of energy harvesters as sensors is particularly interesting, as with such multifunctional operations it is possible to reduce the number devices needed in a system, which also helps overcome the integration complexities. In this regard, TEGs are promising, particularly for energy autonomous chemical and biological sensors, as they can be developed with a wide variety of materials, and their mechanical energy to electricity conversion can be modulated by various analytes. This review focuses on this interesting dimension of TEGs and presents various self-powered active chemical and biological sensors. A brief discussion about the development of TEG-based physical, magnetic, and optical sensors is also included. The influence of environmental factors, various figures of merit, and the significance of TEG design are explained in context with the active sensing. Finally, the key applications, challenges, and future perspective of chemical and biological detection via TEGs are discussed with a view to drive further advances in the field of self-powered sensors.

Conducting Polymers for the Design of Tactile Sensors

This paper provides an overview of the application of conducting polymers (CPs) used in the design of tactile sensors. While conducting polymers can be used as a base in a variety of forms, such as films, particles, matrices, and fillers, the CPs generally remain the same. This paper, first, discusses the chemical and physical properties of conducting polymers. Next, it discusses how these polymers might be involved in the conversion of mechanical effects (such as pressure, force, tension, mass, displacement, deformation, torque, crack, creep, and others) into a change in electrical resistance through a charge transfer mechanism for tactile sensing. Polypyrrole, polyaniline, poly(3,4-ethylenedioxythiophene), polydimethylsiloxane, and polyacetylene, as well as application examples of conducting polymers in tactile sensors, are overviewed. Attention is paid to the additives used in tactile sensor development, together with conducting polymers. There is a long list of additives and composites, used for different purposes, namely: cotton, polyurethane, PDMS, fabric, Ecoflex, Velostat, MXenes, and different forms of carbon such as graphene, MWCNT, etc. Some design aspects of the tactile sensor are highlighted. The charge transfer and operation principles of tactile sensors are discussed. Finally, some methods which have been applied for the design of sensors based on conductive polymers, are reviewed and discussed.

Self-Powered Tactile Sensor for Gesture Recognition Using Deep Learning Algorithms

A multifunctional wearable tactile sensor assisted by deep learning algorithms is developed, which can realize the functions of gesture recognition and interaction. This tactile sensor is the fusion of a triboelectric nanogenerator and piezoelectric nanogenerator to construct a hybrid self-powered sensor with a higher power density and sensibility. The power generation performance is characterized with an open-circuit voltage V_OC of 200 V, a short-circuit current I_SC of 8 μA, and a power density of 0.35 mW cm^-2 under a matching load. It also has an excellent sensibility, including a response time of 5 ms, a signal-to-noise ratio of 22.5 dB, and a pressure resolution of 1% (1-10 kPa). The sensor is successfully integrated on a glove to collect the electrical signal output generated by the gesture. Using deep learning algorithms, the functions of gesture recognition and control can be realized in real time. The combination of tactile sensor and deep learning algorithms provides ideas and guidance for its applications in the field of artificial intelligence, such as human-computer interaction, signal monitoring, and smart sensing.

Polymer-based planar waveguide chirped Bragg grating for high-resolution tactile sensing

A novel tactile sensor for two-dimensional force location measurements, based on polymer-based planar waveguide chirped Bragg gratings (PPCBGs) fabricated on sheet PMMA substrate, is presented. The planar waveguide and chirped Bragg grating are simultaneously generated using a KrF excimer laser and a phase mask covered by a quartz chrome mask. Location and magnitude of an applied force is measured by observing the change of the wavelength of a dip in the measured spectrum and a change in the reflectivity intensity. Experimental characterization indicates submillimeter spatial resolution of applied force in the range of 1-4 N with a sensitivity of 947.02 pm/mm.

Soft Multi-Directional Force Sensor for Underwater Robotic Application

Tactile information is crucial for recognizing physical interactions, manipulation of an object, and motion planning for a robotic gripper; however, concurrent tactile technologies have certain limitations over directional force sensing. In particular, they are expensive, difficult to fabricate, and mostly unsuitable for underwater use. Here, we present a facile and cost-effective synthesis technique of a flexible multi-directional force sensing system, which is also favorable to be utilized in underwater environments. We made use of four flex sensors within a silicone-made hemispherical shell structure. Each sensor was placed 90° apart and aligned with the curve of the hemispherical shape. If the force is applied on the top of the hemisphere, all the flex sensors would bend uniformly and yield nearly identical readings. When force is applied from a different direction, a set of flex sensors would characterize distinctive output patterns to localize the point of contact as well as the direction and magnitude of the force. The deformation of the fabricated soft sensor due to applied force was simulated numerically and compared with the experimental results. The fabricated sensor was experimentally calibrated and tested for characterization including an underwater demonstration. This study would widen the scope of identification of multi-directional force sensing, especially for underwater soft robotic applications.

Self-assembly of graphene oxide flakes for smart and multifunctional coating with reversible formation of wrinkling patterns

One of the main purposes of smart and multifunctional coatings is to have the versatility to be applied in a wide range of applications. However, the functions of smart materials are often highly limited. In particular, the stimuli-responsive lateral expansion of coatings based on 2D materials has not been reported before. This manuscript describes small two-dimensional graphene oxide (GO) flakes (e.g., thin sheets with a thickness of a few nanometers and much larger lateral dimensions) that act as elementary agents for the formation of smart and multifunctional coatings. The coating can be self-assembled from the GO flakes and disassembled flexibly when required. The coating is stimuli-responsive: upon localized contact with water, it expands and forms wrinkling patterns throughout its whole surface. Evaporating the water allows the wrinkles to disappear; hence, the process is reversible. This stimuli-responsiveness can be controlled to be reduced or completely switched off by temperature or pressure. These features are fundamentally due to the reversible intermolecular interactions among the flakes and favorable packing structure of the coating. The smart coating is shown to be useful for patterned fluidic systems of the desired shapes and the development of channels between fluidic reservoirs via the shortest path. Importantly, these results showed that a simple collection of uniquely 2D elementary agents with small nanoscale thickness can self-assemble into macroscopic materials that perform interactive and multifunctional operations.

A review on sensory perception for dexterous robotic manipulation

Sensory perception for dexterous robotic hands is an active research area and recent progress in robotics. Effective dexterous manipulation requires robotic hands to accurately feedback their state or perceive the surrounding environment. This article reviews the state-of-the-art of sensory perception for dexterous robotic manipulation. Two types of sensors, such as intrinsic and extrinsic sensors, are introduced according to their function and layout in robotic hands. These sensors provide rich information to a robotic hand, which contains the posture, the contact information of objects, and the physical information of the environment. Then, a comprehensive analysis of perception methods including planning-level, control-level, and learning-level perceptions is presented. The information obtained from sensory perception can help robotic hands to make decisions effectively. Previously issued reviews mainly focus on the design of tactile senor, while we analyze and discuss the relationship among sensing, perception, and dexterous manipulation. Some potential research topics on sensory perception are also summarized and 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.

NiO-Based Electronic Flexible Devices

Personal, portable, and wearable electronics have become items of extensive use in daily life. Their fabrication requires flexible electronic components with high storage capability or with continuous power supplies (such as solar cells). In addition, formerly rigid tools such as electrochromic windows find new utilizations if they are fabricated with flexible characteristics. Flexibility and performances are determined by the material composition and fabrication procedures. In this regard, low-cost, easy-to-handle materials and processes are an asset in the overall production processes and items fruition. In the present mini-review, the most recent approaches are described in the production of flexible electronic devices based on NiO as low-cost material enhancing the overall performances. In particular, flexible NiO-based all-solid-state supercapacitors, electrodes electrochromic devices, temperature devices, and ReRAM are discussed, thus showing the potential of NiO as material for future developments in opto-electronic devices.

Recent Development of Flexible Tactile Sensors and Their Applications

With the rapid development of society in recent decades, the wearable sensor has attracted attention for motion-based health care and artificial applications. However, there are still many limitations to applying them in real life, particularly the inconvenience that comes from their large size and non-flexible systems. To solve these problems, flexible small-sized sensors that use body motion as a stimulus are studied to directly collect more accurate and diverse signals. In particular, tactile sensors are applied directly on the skin and provide input signals of motion change for the flexible reading device. This review provides information about different types of tactile sensors and their working mechanisms that are piezoresistive, piezocapacitive, piezoelectric, and triboelectric. Moreover, this review presents not only the applications of the tactile sensor in motion sensing and health care monitoring, but also their contributions in the field of artificial intelligence in recent years. Other applications, such as human behavior studies, are also suggested.

Tactile Object Recognition for Humanoid Robots Using New Designed Piezoresistive Tactile Sensor and DCNN

A tactile sensor array is a crucial component for applying physical sensors to a humanoid robot. This work focused on developing a palm-size tactile sensor array (56.0 mm × 56.0 mm) to apply object recognition for the humanoid robot hand. This sensor was based on a PCB technology operating with the piezoresistive principle. A conductive polymer composites sheet was used as a sensing element and the matrix array of this sensor was 16 × 16 pixels. The sensitivity of this sensor was evaluated and the sensor was installed on the robot hand. The tactile images, with resolution enhancement using bicubic interpolation obtained from 20 classes, were used to train and test 19 different DCNNs. InceptionResNetV2 provided superior performance with 91.82% accuracy. However, using the multimodal learning method that included InceptionResNetV2 and XceptionNet, the highest recognition rate of 92.73% was achieved. Moreover, this recognition rate improved when the object exploration was applied to demonstrate.

A Survey of the Tactile Internet: Design Issues and Challenges, Applications, and Future Directions

The Tactile Internet (TI) is an emerging area of research involving 5G and beyond (B5G) communications to enable real-time interaction of haptic data over the Internet between tactile ends, with audio-visual data as feedback. This emerging TI technology is viewed as the next evolutionary step for the Internet of Things (IoT) and is expected to bring about a massive change in Healthcare 4.0, Industry 4.0 and autonomous vehicles to resolve complicated issues in modern society. This vision of TI makes a dream into a reality. This article aims to provide a comprehensive survey of TI, focussing on design architecture, key application areas, potential enabling technologies, current issues, and challenges to realise it. To illustrate the novelty of our work, we present a brainstorming mind-map of all the topics discussed in this article. We emphasise the design aspects of the TI and discuss the three main sections of the TI, i.e., master, network, and slave sections, with a focus on the proposed application-centric design architecture. With the help of the proposed illustrative diagrams of use cases, we discuss and tabulate the possible applications of the TI with a 5G framework and its requirements. Then, we extensively address the currently identified issues and challenges with promising potential enablers of the TI. Moreover, a comprehensive review focussing on related articles on enabling technologies is explored, including Fifth Generation (5G), Software-Defined Networking (SDN), Network Function Virtualisation (NFV), Cloud/Edge/Fog Computing, Multiple Access, and Network Coding. Finally, we conclude the survey with several research issues that are open for further investigation. Thus, the survey provides insights into the TI that can help network researchers and engineers to contribute further towards developing the next-generation Internet.

A bionic piezoelectric tactile sensor for features recognition of object surface based on machine learning

Based on the tactile mechanism of human fingertips, a bionic tactile sensor fabricated from polyvinylidene fluoride piezoelectric film is proposed, which can identify the surface softness, viscoelasticity, thermal conductivity, and texture roughness of the object. The tactile sensor is mounted on the fingertip of the bionic manipulator, which obtains the surface features by touching and sliding the object. The time-domain features of the output signal are used for preliminarily discriminating the softness, viscoelasticity, and heat conduction of the object. Finally, based on the Back Propagation and the Particle Swarm Optimization-Back Propagation neural network algorithm, the recognition experiment of texture roughness is carried out using the PSO algorithm to improve the BP neural network so that the optimized BP algorithm has a higher convergence accuracy. The results show that the PSO-BP algorithm achieved the highest accuracy of 98% for identifying samples with different roughnesses and the average recognition achieved an accuracy of 94%. The bionic piezoelectric tactile sensor proposed in this paper has a good application development prospect in recognizing the surface features of objects and intelligent robots.

A Frequency Modulation-Based Taxel Array: A Bio-Inspired Architecture for Large-Scale Artificial Skin

This work introduces an array prototype based on a Frequency Modulation (FM) encoding architecture to transfer multiple sensor signals on a single wire. The use case presented adopts Hall-effect sensors as an example to represent a much larger range of sensor types (e.g., proximity and temperature). This work aims to contribute to large area artificial skin systems which are a key element to enhance robotic platforms. Artificial skin will allow robotic platforms to have spatial awareness which will make interaction with objects and users safe. The FM-based architecture has been developed to address limitations in large-scale artificial skin scalability. Scalability issues include power requirements; number of wires needed; as well as frequency, density, and sensitivity bottlenecks. In this work, eight sensor signals are simultaneously acquired, transferred on a single wire and decoded in real-time. The overall taxel array current consumption is 36 mA. The work experimentally validates and demonstrates that different input signals can be effectively transferred using this approach minimizing wiring and power consumption of the taxel array. Four different tests using single as well as multiple stimuli are presented. Observations on performances, noise, and taxel array behaviour are reported. The results show that the taxel array is reliable and effective in detecting the applied stimuli.

A Soft Tactile Sensor Based on Magnetics and Hybrid Flexible-Rigid Electronics

Tactile sensing is crucial for robots to manipulate objects successfully. However, integrating tactile sensors into robotic hands is still challenging, mainly due to the need to cover small multi-curved surfaces with several components that must be miniaturized. In this paper, we report the design of a novel magnetic-based tactile sensor to be integrated into the robotic hand of the humanoid robot Vizzy. We designed and fabricated a flexible 4 × 2 matrix of Si chips of magnetoresistive spin valve sensors that, coupled with a single small magnet, can measure contact forces from 0.1 to 5 N on multiple locations over the surface of a robotic fingertip; this design is innovative with respect to previous works in the literature, and it is made possible by careful engineering and miniaturization of the custom-made electronic components that we employ. In addition, we characterize the behavior of the sensor through a COMSOL simulation, which can be used to generate optimized designs for sensors with different geometries.

Human Somatosensory Processing and Artificial Somatosensation

In the past few years, we have gained a better understanding of the information processing mechanism in the human brain, which has led to advances in artificial intelligence and humanoid robots. However, among the various sensory systems, studying the somatosensory system presents the greatest challenge. Here, we provide a comprehensive review of the human somatosensory system and its corresponding applications in artificial systems. Due to the uniqueness of the human hand in integrating receptor and actuator functions, we focused on the role of the somatosensory system in object recognition and action guidance. First, the low-threshold mechanoreceptors in the human skin and somatotopic organization principles along the ascending pathway, which are fundamental to artificial skin, were summarized. Second, we discuss high-level brain areas, which interacted with each other in the haptic object recognition. Based on this close-loop route, we used prosthetic upper limbs as an example to highlight the importance of somatosensory information. Finally, we present prospective research directions for human haptic perception, which could guide the development of artificial somatosensory systems.

Optical-Tactile Sensor for Lump Detection Using Pneumatic Control

Soft tactile sensors are an attractive solution when robotic systems must interact with delicate objects in unstructured and obscured environments, such as most medical robotics applications. The soft nature of such a system increases both comfort and safety, while the addition of simultaneous soft active actuation provides additional features and can also improve the sensing range. This paper presents the development of a compact soft tactile sensor which is able to measure the profile of objects and, through an integrated pneumatic system, actuate and change the effective stiffness of its tactile contact surface. We report experimental results which demonstrate the sensor's ability to detect lumps on the surface of objects or embedded within a silicone matrix. These results show the potential of this approach as a versatile method of tactile sensing with potential application in medical diagnosis.

Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

Design of a Sensitive Balloon Sensor for Safe Human-Robot Interaction

As the safety of a human body is the main priority while interacting with robots, the field of tactile sensors has expanded for acquiring tactile information and ensuring safe human-robot interaction (HRI). Existing lightweight and thin tactile sensors exhibit high performance in detecting their surroundings. However, unexpected collisions caused by malfunctions or sudden external collisions can still cause injuries to rigid robots with thin tactile sensors. In this study, we present a sensitive balloon sensor for contact sensing and alleviating physical collisions over a large area of rigid robots. The balloon sensor is a pressure sensor composed of an inflatable body of low-density polyethylene (LDPE), and a highly sensitive and flexible strain sensor laminated onto it. The mechanical crack-based strain sensor with high sensitivity enables the detection of extremely small changes in the strain of the balloon. Adjusting the geometric parameters of the balloon allows for a large and easily customizable sensing area. The weight of the balloon sensor was approximately 2 g. The sensor is employed with a servo motor and detects a finger or a sheet of rolled paper gently touching it, without being damaged.

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Interaction between a robot and its environment requires perception about the environment, which helps the robot in making a clear decision about the object type and its location. After that, the end effector will be brought to the object’s location for grasping. There are many research studies on the reaching and grasping of objects using different techniques and mechanisms for increasing accuracy and robustness during grasping and reaching tasks. Thus, this paper presents an extensive review of research directions and topics of different approaches such as sensing, learning and gripping, which have been implemented within the current five years.

Hybridized Nanogenerators for Multifunctional Self-Powered Sensing: Principles, Prototypes, and Perspectives

Sensors are a key component of the Internet of Things (IoTs) to collect information of environments or objects. Considering the tremendous number and complex working conditions of sensors, multifunction and self-powered feathers are two basic requirements. Nanogenerators are a kind of devices based on the triboelectric, piezoelectric, or pyroelectric effects to harvest ambient energy and then converting to electricity. The hybridized nanogenerators that combined multiple effects in one device have great potential in multifunctional self-powered sensors because of the unique superiority such as generating electrical signals directly, responding to diverse stimuli, etc. This review aims at introducing the latest advancements of hybridized nanogenerators for multifunctional self-powered sensing. Firstly, the principles and sensor prototypes based on TENG are summarized. To avoid signal interference and energy insufficiently, the multifunctional self-powered sensors based on hybridized nanogenerators are reviewed. At last, the challenges and future development of multifunctional self-powered sensors have prospected.

Flexible P(VDF-TrFE) Shared Bottom Electrode Sensor Array Assisted with Machine Learning for Motion Detection

Lightweight, flexible and distributed-pixel piezoelectric sensors are desired in activity monitoring and human–machine interaction (HMI). In this work, a flexible P(VDF-TrFE) piezoelectric sensor array using ITO-coated PET substrate as the shared bottom electrode is demonstrated. The traditional array fabrication, which connects an individual sensor unit into an array, could easily lead to the signal discrepancy due to fabrication and assembly errors. To this end, this work introduces the shared ITO-coated-PET substrate and proposes a synchronous-fabrication method for generating the same thickness of every P(VDF-TrFE) sensor unit through a single spin coating. The designed Au top electrodes were sputtered on the spin-coated P(VDF-TrFE) to form the sensor array at one time without additional assembly step, further ensuring unit consistency. The performance of the cross-shaped sensor array was tested under cyclic compressing–releasing agitation. The results of the positive compression test show that our sensor array has a high consistency. Then, the cross-shaped array design that covers the central position is put forward, which realizes tactile sensing ability with a small number of units. Moreover, the fabricated flexible multi-pixel sensor has the advantage of sensitive identification of different contact scenes, and a recognition accuracy of 95.5% can be obtained in different types of hand touch through the machine learning technology.