New materials and advances in making electronic skin for interactive robots

Robotics Perception and Control: Key Technologies and Applications

The integration of advanced sensor technologies has significantly propelled the dynamic development of robotics, thus inaugurating a new era in automation and artificial intelligence. Given the rapid advancements in robotics technology, its core area-robot control technology-has attracted increasing attention. Notably, sensors and sensor fusion technologies, which are considered essential for enhancing robot control technologies, have been widely and successfully applied in the field of robotics. Therefore, the integration of sensors and sensor fusion techniques with robot control technologies, which enables adaptation to various tasks in new situations, is emerging as a promising approach. This review seeks to delineate how sensors and sensor fusion technologies are combined with robot control technologies. It presents nine types of sensors used in robot control, discusses representative control methods, and summarizes their applications across various domains. Finally, this survey discusses existing challenges and potential future directions.

A systematic review and multivariate meta-analysis of the physical and mental health benefits of touch interventions

Receiving touch is of critical importance, as many studies have shown that touch promotes mental and physical well-being. We conducted a pre-registered (PROSPERO: CRD42022304281) systematic review and multilevel meta-analysis encompassing 137 studies in the meta-analysis and 75 additional studies in the systematic review (n = 12,966 individuals, search via Google Scholar, PubMed and Web of Science until 1 October 2022) to identify critical factors moderating touch intervention efficacy. Included studies always featured a touch versus no touch control intervention with diverse health outcomes as dependent variables. Risk of bias was assessed via small study, randomization, sequencing, performance and attrition bias. Touch interventions were especially effective in regulating cortisol levels (Hedges' g = 0.78, 95% confidence interval (CI) 0.24 to 1.31) and increasing weight (0.65, 95% CI 0.37 to 0.94) in newborns as well as in reducing pain (0.69, 95% CI 0.48 to 0.89), feelings of depression (0.59, 95% CI 0.40 to 0.78) and state (0.64, 95% CI 0.44 to 0.84) or trait anxiety (0.59, 95% CI 0.40 to 0.77) for adults. Comparing touch interventions involving objects or robots resulted in similar physical (0.56, 95% CI 0.24 to 0.88 versus 0.51, 95% CI 0.38 to 0.64) but lower mental health benefits (0.34, 95% CI 0.19 to 0.49 versus 0.58, 95% CI 0.43 to 0.73). Adult clinical cohorts profited more strongly in mental health domains compared with healthy individuals (0.63, 95% CI 0.46 to 0.80 versus 0.37, 95% CI 0.20 to 0.55). We found no difference in health benefits in adults when comparing touch applied by a familiar person or a health care professional (0.51, 95% CI 0.29 to 0.73 versus 0.50, 95% CI 0.38 to 0.61), but parental touch was more beneficial in newborns (0.69, 95% CI 0.50 to 0.88 versus 0.39, 95% CI 0.18 to 0.61). Small but significant small study bias and the impossibility to blind experimental conditions need to be considered. Leveraging factors that influence touch intervention efficacy will help maximize the benefits of future interventions and focus research in this field.

Skin-Inspired Capacitive Flexible Tactile Sensor with an Asymmetric Structure for Detecting Directional Shear Forces

Flexible pressure sensors based on micro-/nanostructures can be integrated into robots to achieve sensitive tactile perception. However, conventional symmetric structures, such as pyramids or hemispheres, can sense only the magnitude of a force and not its direction. In this study, a capacitive flexible tactile sensor inspired by skin structures and based on an asymmetric microhair structure array to perceive directional shear force is designed. Asymmetric microhair structures are obtained by two-photon polymerization (TPP) and replication. Owing to the features of asymmetric microhair structures, different shear force directions result in different deformations. The designed device can determine the directions of both static and dynamic shear forces. Additionally, it exhibits large response scales ranging from 30 Pa to 300 kPa and maintains high stability even after 5000 cycles; the final relative capacitive change (ΔC/C0 ) is <2.5%. This flexible tactile sensor has the potential to improve the perception and manipulation ability of dexterous hands and enhance the intelligence of robots.

FOESO-Net: A specific neural network for fast sensorless robot manipulator torque estimation

Contact torque sensing allows robot manipulators to cooperate with humans and detect accidental collisions in real time to ensure safety. Most sensorless torque estimation schemes, which are based on linear observer approaches, cannot compromise between non-negligible noise and high observation bandwidth. Therefore, fast time-varying nonlinear torque observation cannot be satisfied. To achieve this challenge, a customized network called FOESO-Net based on a novel fractional-order extended state observer is carefully designed in this paper. The network firstly chooses momentum as the benchmark state for torque estimation, which can avoid joint acceleration and model's inverse inertia matrix solution. Then, a fractional-order extended state observer (FOESO) is proposed from the perspective of momentum control to better adapt to the nonlinear fast time varying torque. In addition, a fractional-order neural network and a weight update neural network parallel architecture are constructed to enable fractional-order and dynamic weight-based adaptive learning of FOESO parameters. Formal analysis and proofs are made to show that the error of FOESO-Net is convergent. Finally, the effectiveness of the proposed method is verified by numerical simulations and a real collaborative robot platform. Moreover, compared with existing methods, the FOESO-Net based torque estimation method can reduce the estimation error and response time, which illustrates the superiority of the designed method.

Interconnection Technologies for Flexible Electronics: Materials, Fabrications, and Applications

Flexible electronic devices require metal interconnects to facilitate the flow of electrical signals among the device components, ensuring its proper functionality. There are multiple factors to consider when designing metal interconnects for flexible electronics, including their conductivity, flexibility, reliability, and cost. This article provides an overview of recent endeavors to create flexible electronic devices through different metal interconnect approaches, with a focus on materials and structural aspects. Additionally, the article discusses emerging flexible applications, such as e-textiles and flexible batteries, as essential considerations.

Material Choice and Structure Design of Flexible Battery Electrode

With the development of flexible electronics, the demand for flexibility is gradually put forward for its energy supply device, i.e., battery, to fit complex curved surfaces with good fatigue resistance and safety. As an important component of flexible batteries, flexible electrodes play a key role in the energy density, power density, and mechanical flexibility of batteries. Their large-scale commercial applications depend on the fulfillment of the commercial requirements and the fabrication methods of electrode materials. In this paper, the deformable electrode materials and structural design for flexible batteries are summarized, with the purpose of flexibility. The advantages and disadvantages of the application of various flexible materials (carbon nanotubes, graphene, MXene, carbon fiber/carbon fiber cloth, and conducting polymers) and flexible structures (buckling structure, helical structure, and kirigami structure) in flexible battery electrodes are discussed. In addition, the application scenarios of flexible batteries and the main challenges and future development of flexible electrode fabrication are also discussed, providing general guidance for the research of high-performance flexible electrodes.

Large-Area 3D Printable Soft Electronic Skin for Biomedical Applications

Soft electronic skin (soft-e-skin) capable of sensing touch and pressure similar to human skin is essential in many applications, including robotics, healthcare, and augmented reality. However, most of the research effort on soft-e-skin was confined to the lab-scale demonstration. Several hurdles remain challenging, such as highly complex and expensive fabrication processes, instability in long-term use, and difficulty producing large areas and mass production. Here, we present a robust 3D printable large-area electronic skin made of a soft and resilient polymer capable of detecting touch and load, and bending with extreme sensitivity (up to 150 kPa^-1) to touch and load, 750 times higher than earlier work. The soft-e-skin shows excellent long-term stability and consistent performance up to almost a year. In addition, we describe a fabrication process capable of producing large areas and in large numbers, yet is cost-effective. The soft-e-skin consists of a uniquely designed optical waveguide and a layer of a soft membrane with an array of soft structures which work as passive sensing nodes. The use of a soft structure gives the liberty of stretching to the soft-e-skin without considering the disjoints among the sensing nodes. We have shown the functioning of the soft-e-skin under various conditions.

Neuro-inspired electronic skin for robots

Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.

High-Sensitivity Pressure Sensors Based on a Low Elastic Modulus Adhesive

With the rapid development of intelligent applications, the demand for high-sensitivity pressure sensor is increasing. However, the simple and efficient preparation of an industrial high-sensitivity sensor is still a challenge. In this study, adhesives with different elastic moduli are used to bond pressure-sensitive elements of double-sided sensitive grids to prepare a highly sensitive and fatigue-resistant pressure sensor. It was observed that the low elastic modulus adhesive effectively produced tensile and compressive strains on both sides of the sensitive grids to induce greater strain transfer efficiency in the pressure sensor, thus improving its sensitivity. The sensitivity of the sensor was simulated by finite element analysis to verify that the low elastic modulus adhesive could enhance the sensitivity of the sensor up to 12%. The preparation of high-precision and fatigue-resistant pressure sensors based on low elastic modulus, double-sided sensitive grids makes their application more flexible and convenient, which is urgently needed in the miniaturization and integration electronics field.

Artificial fast-adapting mechanoreceptor based on carbon nanotube percolating network

Most biological sensors preferentially encode changes in a stimulus rather than the steady components. However, intrinsically phasic artificial mechanoreceptors have not yet been described. We constructed a phasic mechanoreceptor by encapsulating carbon nanotube film in a viscoelastic matrix supported by a rigid substrate. When stimulated by a spherical indenter the sensor response resembled the response of fast-adapting mammalian mechanoreceptors. We modelled these sensors from the properties of percolating conductive networks combined with nonlinear contact mechanics and discussed the implications of this finding.

Applications of Graphene-Based Materials in Sensors: A Review

With the research and the development of graphene-based materials, new sensors based on graphene compound materials are of great significance to scientific research and the consumer market. However, in the past ten years, due to the requirements of sensor accuracy, reliability, and durability, the development of new graphene sensors still faces many challenges in the future. Due to the special structure of graphene, the obtained characteristics can meet the requirements of high-performance sensors. Therefore, graphene materials have been applied in many innovative sensor materials in recent years. This paper introduces the important role and specific examples of sensors based on graphene and its base materials in biomedicine, photoelectrochemistry, flexible pressure, and other fields in recent years, and it puts forward the difficulties encountered in the application of graphene materials in sensors. Finally, the development direction of graphene sensors has been prospected. For the past two years of the COVID-19 epidemic, the detection of the virus sensor has been investigated. These new graphene sensors can complete signal detection based on accuracy and reliability, which provides a reference for researchers to select and manufacture sensor materials.

A Hybrid Microstructure Piezoresistive Sensor with Machine Learning Approach for Gesture Recognition

Developments in flexible electronics have adopted various approaches which have enhanced the applicability of human–machine interface fields. Recently, microstructural integration and hybrid functional materials were designed for realizing human somatosensory. Nonetheless, designing tactile sensors with smart structures using facile and low-cost fabrication processes remains challenging. Furthermore, using the sensors for recognizing stimuli and feedback applications remains poorly validated. In this study, a highly flexible piezoresistive tactile sensor was developed by homogeneously dispersing carbon black (CB) in a microstructure porous sugar/PDMS-based sponge. Owning to its high flexibility and softness, the sensor can be mounted on human or robotic systems for different clinical applications. We validated the applicability of the proposed sensor by applying it to recognizing grasp and release forces in an open setting and to classifying hand motions that surgeons apply on the master interface of a robotic system during intravascular catheterization. For this purpose, we implemented the long short-term memory (LSTM)-dense classification model and five traditional machine learning methods, namely, support vector machine, multilayer perceptron, decision tree, and k-nearest neighbor. The models were used to classify the different hand gestures obtained in an open-setting experiment. Amongst all, the LSTM-dense method yielded the highest overall recognition accuracy (87.38%). Nevertheless, the performance of the other models was in a similar range, showing that our sensor structure can be applied in intelligence sensing or tactile feedback systems. Secondly, the sensor prototype was applied to analyze the motions made while manipulating an interventional robot. We analyzed the displacement and velocity of the master interface during typical axial (push/pull) and radial operations with the robot. The results obtained show that the sensor is capable of recording unique patterns during different operations. Thus, a combination of the flexible wearable sensors and machine learning could yield a future generation of flexible materials and artificial intelligence of things (AIoT) devices.

Review: Sensors for Biosignal/Health Monitoring in Electronic Skin

Skin is the largest sensory organ and receives information from external stimuli. Human body signals have been monitored using wearable devices, which are gradually being replaced by electronic skin (E-skin). We assessed the basic technologies from two points of view: sensing mechanism and material. Firstly, E-skins were fabricated using a tactile sensor. Secondly, E-skin sensors were composed of an active component performing actual functions and a flexible component that served as a substrate. Based on the above fabrication processes, the technologies that need more development were introduced. All of these techniques, which achieve high performance in different ways, are covered briefly in this paper. We expect that patients' quality of life can be improved by the application of E-skin devices, which represent an applied advanced technology for real-time bio- and health signal monitoring. The advanced E-skins are convenient and suitable to be applied in the fields of medicine, military and environmental monitoring.

Quasi-Passive Resistive Exosuit for Space Activities: Proof of Concept

The limits of space travel are continuously evolving, and this creates increasingly extreme challenges for the crew’s health that must be addressed by the scientific community. Long-term exposure to micro-gravity, during orbital flights, contributes to muscle strength degradation and increases bone density loss. In recent years, several exercise devices have been developed to counteract the negative health effects of zero-gravity on astronauts. However, the relatively large size of these devices, the need for a dedicated space and the exercise time-frame for each astronaut, does not make these devices the best choice for future long range exploration missions. This paper presents a quasi-passive exosuit to provide muscle training using a small, portable, proprioceptive device. The exosuit promotes continuous exercise, by resisting the user’s motion, during routine all-day activity. This study assesses the effectiveness of the resistive exosuit by evaluating its effects on muscular endurance during a terrestrial walking task. The experimental assessment on biceps femoris and vastus lateralis, shows a mean increase in muscular activation of about 97.8% during five repetitions of 3 min walking task at 3 km/h. The power frequency analysis shows an increase in muscular fatigue with a reduction of EMG median frequency of about 15.4% for the studied muscles.

Sensing-range-tunable pressure sensors realized by self-patterned-spacer design and vertical CNT arrays embedded in PDMS

A pressure sensor design suitable for a broad sensing range with high sensitivity and good stability is highly desirable for the detection of various pressures and meeting the requirements of different applications. Herein, we report sensing-range-tunable piezoresistive pressure sensors realized by self-patterned-spacer design. In the sensors, the two CNT-array layers embedded in PDMS are separated by the proposed self-patterned spacer. With this structure, the realized sensors with large initial resistance designed show tunable response thresholds from 300 Pa to 6.5 kPa while maintaining high sensitivity, which are realized by controlling the spacer thickness and the CNT length. Besides, the vertical CNT arrays have a large specific surface area, which can dramatically change the resistance of the pressure sensors and lead to high sensitivity with nearly 50 kPa^-1. Benefiting from the designs of the self-patterned spacer and the advantageous combination of CNTs and PDMS, the pressure sensors also exhibit a rapid response/relaxation time of 24/32 ms, and good long-term stability with durability test over 10 000 loading/unloading cycles. On the other hand, the realized pressure sensors with small initial resistance designed show a typical piezoresistive characteristic. For applications, the pressure sensors with large initial resistance designed are suitable for the anti-noise applications with pressure thresholds to filter unnecessary noise and save power consumption, while the pressure sensors with small initial resistance designed show the capability of detecting mechanical forces and monitoring human physiological signals. Moreover, the self-patterned design and fabrication method of the spacers also show potentials to be applied in the existing works to further enhance or adjust the performance of those pressure sensors, showing great flexibility. This design demonstrates great potentials to be applied in future advanced flexible wearable systems such as health monitoring, human-machine interaction and the Internet of Things.

Process Parameters for FFF 3D-Printed Conductors for Applications in Sensors

With recent developments in additive manufacturing (AM), new possibilities for fabricating smart structures have emerged. Recently, single-process fused-filament fabrication (FFF) sensors for dynamic mechanical quantities have been presented. Sensors measuring dynamic mechanical quantities, like strain, force, and acceleration, typically require conductive filaments with a relatively high electrical resistivity. For fully embedded sensors in single-process FFF dynamic structures, the connecting electrical wires also need to be printed. In contrast to the sensors, the connecting electrical wires have to have a relatively low resistivity, which is limited by the availability of highly conductive FFF materials and FFF process conditions. This study looks at the Electrifi filament for applications in printed electrical conductors. The effect of the printing-process parameters on the electrical performance is thoroughly investigated (six parameters, >40 parameter values, >200 conductive samples) to find the highest conductivity of the printed conductors. In addition, conductor embedding and post-printing heating of the conductive material are researched. The experimental results helped us to understand the mechanisms of the conductive network’s formation and its degradation. With the insight gained, the optimal printing strategy resulted in a resistivity that was approx. 40% lower than the nominal value of the filament. With a new insight into the electrical behavior of the conductive material, process optimizations and new design strategies can be implemented for the single-process FFF of functional smart structures.

Measurement of Shear Strain Field in a Soft Material Using a Sensor System Consisting of Distributed Piezoelectric Polymer Film

Measurement of the internal stress and strain distributions within soft materials is necessary in the field of skin contact safety. However, conventional interactive force sensors cannot efficiently obtain or estimate these distributions. Herein, a shear strain sensor system consisting of distributed built-in piezoelectric polyvinylidene fluoride (PVDF) polymer films was developed to measure the internal shear strain field of a soft material. A shear strain sensing model was mathematically established, based on the piezoelectricity and mechanical behavior of a bending cantilever beam, to explain the sensing principle. An experiment in three-dimensional measurement of the shear strain distribution within an artificial skin was designed and conducted to assess the sensitivity of the sensing model. This sensor system could visualize the shear strain field and was sensitive to different contact conditions. The measurement results agreed well with the results of numerical simulation of the substrate, based on contact mechanics. The proposed sensor system was confirmed to provide a new sensing method for the field of shape analysis. The sensor system can be applied to develop sufficiently sensitive electronic skin and can significantly contribute to skin damage analysis and skin contact safety assessment.

Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications

Human-machine interfaces (HMIs) experience increasing requirements for intuitive and effective manipulation. Current commercialized solutions of glove-based HMI are limited by either detectable motions or the huge cost on fabrication, energy, and computing power. We propose the haptic-feedback smart glove with triboelectric-based finger bending sensors, palm sliding sensor, and piezoelectric mechanical stimulators. The detection of multidirectional bending and sliding events is demonstrated in virtual space using the self-generated triboelectric signals for various degrees of freedom on human hand. We also perform haptic mechanical stimulation via piezoelectric chips to realize the augmented HMI. The smart glove achieves object recognition using machine learning technique, with an accuracy of 96%. Through the integrated demonstration of multidimensional manipulation, haptic feedback, and AI-based object recognition, our glove reveals its potential as a promising solution for low-cost and advanced human-machine interaction, which can benefit diversified areas, including entertainment, home healthcare, sports training, and medical industry.

Electronic skins and machine learning for intelligent soft robots

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

Mechanical Sensing for Lower Limb Soft Exoskeletons: Recent Progress and Challenges

Soft exoskeletons hold promise for facilitating monitoring and assistance in case of light impairment and for prolonging independent living. In contrast to rigid material-based exoskeletons, they strongly demand for new approaches of soft sensing and actuation solutions. This chapter overviews soft exoskeletons in contrast to rigid exoskeletons and focuses on the recent advancements on the movement monitoring in lower limb soft exoskeletons. Compliant materials and soft tactile sensing approaches can be utilized to build smart sensorized garments for joint angle measurements (needed for both control and monitoring). However, currently there are still several open challenges derived from the needed close interaction between the human body and the soft exoskeleton itself, especially related to how sensing function and robustness are strongly affected by wearability, which will need to be overcome in the near future.

Soft eSkin: distributed touch sensing with harmonized energy and computing

Inspired by biology, significant advances have been made in the field of electronic skin (eSkin) or tactile skin. Many of these advances have come through mimicking the morphology of human skin and by distributing few touch sensors in an area. However, the complexity of human skin goes beyond mimicking few morphological features or using few sensors. For example, embedded computing (e.g. processing of tactile data at the point of contact) is centric to the human skin as some neuroscience studies show. Likewise, distributed cell or molecular energy is a key feature of human skin. The eSkin with such features, along with distributed and embedded sensors/electronics on soft substrates, is an interesting topic to explore. These features also make eSkin significantly different from conventional computing. For example, unlike conventional centralized computing enabled by miniaturized chips, the eSkin could be seen as a flexible and wearable large area computer with distributed sensors and harmonized energy. This paper discusses these advanced features in eSkin, particularly the distributed sensing harmoniously integrated with energy harvesters, storage devices and distributed computing to read and locally process the tactile sensory data. Rapid advances in neuromorphic hardware, flexible energy generation, energy-conscious electronics, flexible and printed electronics are also discussed. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.

Proposal of the Tactile Glove Device

This project aims to develop a tactile glove device and a virtual environment inserted in the context of tactile internet. The tactile glove allows a human operator to interact remotely with objects from a 3D environment through tactile feedback or tactile sensation. In other words, the human operator is able to feel the contour and texture from virtual objects. Applications such as remote diagnostics, games, remote analysis of materials, and others in which objects could be virtualized can be significantly improved using this kind of device. These gloves have been an essential device in all research on the internet next generation called "Tactile Internet", in which this project is inserted. Unlike the works presented in the literature, the novelty of this work is related to architecture, and tactile devices developed. They are within the 10 ms round trip latency limits required in a tactile internet environment. Details of hardware and software designs of a tactile glove, as well as the virtual environment, are described. Results and comparative analysis about round trip latency time in the tactile internet environment is developed.

Recent Developments in Printing Flexible and Wearable Sensing Electronics for Healthcare Applications

Wearable biosensors attract significant interest for their capabilities in real-time monitoring of wearers' health status, as well as the surrounding environment. Sensor patches are embedded onto the human epidermis accompanied by data readout and signal conditioning circuits with wireless communication modules for transmitting data to the computing devices. Wearable sensors designed for recognition of various biomarkers in human epidermis fluids, such as glucose, lactate, pH, cholesterol, etc., as well as physiological indicators, i.e., pulse rate, temperature, breath rate, respiration, alcohol, activity monitoring, etc., have potential applications both in medical diagnostics and fitness monitoring. The rapid developments in solution-based nanomaterials offered a promising perspective to the field of wearable sensors by enabling their cost-efficient manufacturing through printing on a wide range of flexible polymeric substrates. This review highlights the latest key developments made in the field of wearable sensors involving advanced nanomaterials, manufacturing processes, substrates, sensor type, sensing mechanism, and readout circuits, and ends with challenges in the future scope of the field. Sensors are categorized as biological and fluidic, mounted directly on the human body, or physiological, integrated onto wearable substrates/gadgets separately for monitoring of human-body-related analytes, as well as external stimuli. Special focus is given to printable materials and sensors, which are key enablers for wearable electronics.

Haptic Glove and Platform with Gestural Control For Neuromorphic Tactile Sensory Feedback In Medical Telepresence †

Advancements in the study of the human sense of touch are fueling the field of haptics. This is paving the way for augmenting sensory perception during object palpation in tele-surgery and reproducing the sensed information through tactile feedback. Here, we present a novel tele-palpation apparatus that enables the user to detect nodules with various distinct stiffness buried in an ad-hoc polymeric phantom. The contact force measured by the platform was encoded using a neuromorphic model and reproduced on the index fingertip of a remote user through a haptic glove embedding a piezoelectric disk. We assessed the effectiveness of this feedback in allowing nodule identification under two experimental conditions of real-time telepresence: In Line of Sight (ILS), where the platform was placed in the visible range of a user; and the more demanding Not In Line of Sight (NILS), with the platform and the user being 50 km apart. We found that the entailed percentage of identification was higher for stiffer inclusions with respect to the softer ones (average of 74% within the duration of the task), in both telepresence conditions evaluated. These promising results call for further exploration of tactile augmentation technology for telepresence in medical interventions.

A 3-D Surface Reconstruction with Shadow Processing for Optical Tactile Sensors

An optical tactile sensor technique with 3-dimension (3-D) surface reconstruction is proposed for robotic fingers. The hardware of the tactile sensor consists of a surface deformation sensing layer, an image sensor and four individually controlled flashing light emitting diodes (LEDs). The image sensor records the deformation images when the robotic finger touches an object. For each object, four deformation images are taken with the LEDs providing different illumination directions. Before the 3-D reconstruction, the look-up tables are built to map the intensity distribution to the image gradient data. The possible image shadow will be detected and amended. Then the 3-D depth distribution of the object surface can be reconstructed from the 2-D gradient obtained using the look-up tables. The architecture of the tactile sensor and the proposed signal processing flow have been presented in details. A prototype tactile sensor has been built. Both the simulation and experimental results have validated the effectiveness of the proposed 3-D surface reconstruction method for the optical tactile sensors. The proposed 3-D surface reconstruction method has the unique feature of image shadow detection and compensation, which differentiates itself from those in the literature.

Tactile Feels in Grasping/Cutting Processes with Scissors

Understanding the dynamic phenomena in grasping/cutting processes with scissors is important for the design of surgical robots and virtual reality systems. Here, we show the relationship between the mechanical stimuli and tactile sensations when forceps or scissors are used. Nineteen subjects grasped or cut objects and evaluated the tactile sensations in each of the processes. To conduct the tactile and mechanical evaluation simultaneously, subjects operated scissors that were fixed to a mechanical evaluation system. When subjects grasped urethane resin, stainless steel plate, and adhesive tape, soft, hard, and sticky feels were perceived, respectively. Dry, hard, and creaking feels were perceived in the paper cutting process. In addition, we observed four characteristic tangential force profiles in the processes. Regression analysis suggests the following findings: Hardness is perceived by the change of force and blade movement when the scissors make contact with the object. Stickiness is caused by the increase and decrease of force at the moment of peeling when the scissors break contact with the object. The cutting sensation is affected by fine force fluctuations during the scissors closing and the rapidly decreased force at the moment of cutting completion.

Active Tactile Transfer Learning for Object Discrimination in an Unstructured Environment Using Multimodal Robotic Skin

In this paper, we propose a probabilistic active tactile transfer learning (ATTL) method to enable robotic systems to exploit their prior tactile knowledge while discriminating among objects via their physical properties (surface texture, stiffness, and thermal conductivity). Using the proposed method, the robot autonomously selects and exploits its most relevant prior tactile knowledge to efficiently learn about new unknown objects with a few training samples or even one. The experimental results show that using our proposed method, the robot successfully discriminated among new objects with [Formula: see text] discrimination accuracy using only one training sample (on-shot-tactile-learning). Furthermore, the results demonstrate that our method is robust against transferring irrelevant prior tactile knowledge (negative tactile knowledge transfer).

Three Dimensional Distribution of Sensitive Field and Stress Field Inversion of Force Sensitive Materials under Constant Current Excitation

Force sensitive conductive composite materials are functional materials which can be used as the sensitive material of force sensors. However, the existing sensors only use one-dimensional electrical properties of force sensitive conductive materials. Even in tactile sensors, the measurement of contact pressure is achieved by large-scale arrays and the units of a large-scale array are also based on the one-dimensional electrical properties of force sensitive materials. The main contribution of this work is to study the three-dimensional electrical properties and the inversion method of three-dimensional stress field of a force sensitive material (conductive rubber), which pushes the application of force sensitive material from one dimensional to three-dimensional. First, the mathematical model of the conductive rubber current field distribution under a constant force is established by the effective medium theory, and the current field distribution model of conductive rubber with different geometry, conductive rubber content and conductive rubber relaxation parameters is deduced. Secondly, the inversion method of the three-dimensional stress field of conductive rubber is established, which provides a theoretical basis for the design of a new tactile sensor, three-dimensional stress field and space force based on force sensitive materials.

Active Prior Tactile Knowledge Transfer for Learning Tactual Properties of New Objects

Reusing the tactile knowledge of some previously-explored objects (prior objects) helps us to easily recognize the tactual properties of new objects. In this paper, we enable a robotic arm equipped with multi-modal artificial skin, like humans, to actively transfer the prior tactile exploratory action experiences when it learns the detailed physical properties of new objects. These experiences, or prior tactile knowledge, are built by the feature observations that the robot perceives from multiple sensory modalities, when it applies the pressing, sliding, and static contact movements on objects with different action parameters. We call our method Active Prior Tactile Knowledge Transfer (APTKT), and systematically evaluated its performance by several experiments. Results show that the robot improved the discrimination accuracy by around 10% when it used only one training sample with the feature observations of prior objects. By further incorporating the predictions from the observation models of prior objects as auxiliary features, our method improved the discrimination accuracy by over 20%. The results also show that the proposed method is robust against transferring irrelevant prior tactile knowledge (negative knowledge transfer).

Using Micro-Molding and Stamping to Fabricate Conductive Polydimethylsiloxane-Based Flexible High-Sensitivity Strain Gauges

In this study, polydimethylsiloxane (PDMS) and conductive carbon nanoparticles were combined to fabricate a conductive elastomer PDMS (CPDMS). A high sensitive and flexible CPDMS strain sensor is fabricated by using stamping-process based micro patterning. Compared with conventional sensors, flexible strain sensors are more suitable for medical applications but are usually fabricated by photolithography, which suffers from a large number of steps and difficult mass production. Hence, we fabricated flexible strain sensors using a stamping-process with fewer processes than photolithography. The piezoresistive coefficient and sensitivity of the flexible strain sensor were improved by sensor pattern design and thickness change. Micro-patterning is used to fabricate various CPDMS microstructure patterns. The effect of gauge pattern was evaluated with ANSYS simulations. The piezoresistance of the strain gauges was measured and the gauge factor determined. Experimental results show that the piezoresistive coefficient of CPDMS is approximately linear. Gauge factor measurement results show that the gauge factor of a 140.0 μm thick strain gauge with five grids is the highest.

Large-Area Self-Assembly of Silica Microspheres/Nanospheres by Temperature-Assisted Dip-Coating

This work reports a temperature-assisted dip-coating method for self-assembly of silica (SiO₂) microspheres/nanospheres (SPs) as monolayers over large areas (∼cm²). The area over which self-assembled monolayers (SAMs) are formed can be controlled by tuning the suspension temperature (T_s), which allows precise control over the meniscus shape. Furthermore, the formation of periodic stripes of SAMs, with excellent dimensional control (stripe width and stripe-to-stripe spacing), is demonstrated using a suitable set of dip-coating parameters. These findings establish the role of T_s, and other parameters such as withdrawal speed (V_w), withdrawal angle (θ_w), and withdrawal step length (L_w). For T_s ranged between 25 and 80 °C, the morphological analysis of dip-coatings shows layered structures comprising of defective layers (25-60 °C), single layers (70 °C), and multilayers (>70 °C) owing to the variation of SP flux at the meniscus/substrate assembling interface. At T_s = 70 °C, there is an optimum V_w, approximately equal to the downshift speed of the meniscus (V_m = 1.3 μm/s), which allows the SAM formation over areas (2.25 cm²) roughly 10 times larger than reported in the literature using nanospheres. Finally, the large-area SAM is used to demonstrate the enhanced performance of antireflective coatings for photovoltaic cells and to create metal nanomesh for Si nanowire synthesis.

Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin

This paper presents novel Neural Nanowire Field Effect Transistors (υ-NWFETs) based hardware-implementable neural network (HNN) approach for tactile data processing in electronic skin (e-skin). The viability of Si nanowires (NWs) as the active material for υ-NWFETs in HNN is explored through modeling and demonstrated by fabricating the first device. Using υ-NWFETs to realize HNNs is an interesting approach as by printing NWs on large area flexible substrates it will be possible to develop a bendable tactile skin with distributed neural elements (for local data processing, as in biological skin) in the backplane. The modeling and simulation of υ-NWFET based devices show that the overlapping areas between individual gates and the floating gate determines the initial synaptic weights of the neural network - thus validating the working of υ-NWFETs as the building block for HNN. The simulation has been further extended to υ-NWFET based circuits and neuronal computation system and this has been validated by interfacing it with a transparent tactile skin prototype (comprising of 6 × 6 ITO based capacitive tactile sensors array) integrated on the palm of a 3D printed robotic hand. In this regard, a tactile data coding system is presented to detect touch gesture and the direction of touch. Following these simulation studies, a four-gated υ-NWFET is fabricated with Pt/Ti metal stack for gates, source and drain, Ni floating gate, and Al2O3 high-k dielectric layer. The current-voltage characteristics of fabricated υ-NWFET devices confirm the dependence of turn-off voltages on the (synaptic) weight of each gate. The presented υ-NWFET approach is promising for a neuro-robotic tactile sensory system with distributed computing as well as numerous futuristic applications such as prosthetics, and electroceuticals.

Thermally Conductive, Electrical Insulating, Optically Transparent Bi-Layer Nanopaper

Cellulose nanofiber (CNF) from abundant and renewable wood is an emerging material with excellent mechanical, chemical, and optical properties. Transparent nanopaper made of CNF (CNF-nanopaper) could potentially replace plastics in electronics due to its excellent optical transparency, mechanical strength, and biodegradability. However, CNF-nanopaper normally has a low thermal conductivity and poor stability in increasing temperatures, which is not suitable for long-term stability and reliability in devices. Herein, for the first time, we report a thermally conductive, electrically insulating, and optically transparent nanopaper using a bilayer design where a thin layer of boron nitride (BN) nanosheets were coated on the CNF-nanopaper. An optical transparency (70%) and a thermal conductivity (0.76 W/m/K) were successfully achieved through a solution-based process at room temperature. Such an optically transparent, electrically insulating, and thermally conductive bilayer nanopaper can find applications in a range of electronic devices.

A biomimetic tactile sensing system based on polyvinylidene fluoride film

Polyvinylidene fluoride (PVDF) film has been widely investigated as a sensing material due to its outstanding properties such as biocompatibility, high thermal stability, good chemical resistance, high piezo-, pyro- and ferro-electric properties. This paper reports on the design, test, and analysis of a biomimetic tactile sensor based on PVDF film. This sensor consists of a PVDF film with aluminum electrodes, a pair of insulating layers, and a "handprint" friction layer with a copper foil. It is designed for easy fabrication and high reliability in outputting signals. In bionics, the fingerprint of the glabrous skin plays an important role during object handling. Therefore, in order to enhance friction and to provide better manipulation, the ridges of the fingertips were introduced into the design of the proposed tactile sensor. And, a basic experimental study on the selection of the high sensitivity fingerprint type for the biomimetic sensor was performed. In addition, we proposed a texture distinguish experiment to verify the sensor sensitivity. The experiment's results show that the novel biomimetic sensor is effective in discriminating object surface characteristics. Furthermore, an efficient visual application program (LabVIEW) and a quantitative evaluation method were proposed for the verification of the biomimetic sensor. The proposed tactile sensor shows great potential for contact force and slip measurements.