Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

Bioinspired tactile perception platform with information encryption function

Mimicking tactile perception is critical to the development of advanced interactive neuromorphic platforms.Inspired by cutaneous perceptual functions,a bionic tactile perceptual platform is proposed.PDMS-based tactile sensors act as bionic skin touch receptors.Flexible indium tin oxide neuromorphic transistors fabricated with a single-step mask pro-cessing act as artificial synapses.Thus,the tactile perceptual platform possesses the ability of information processing.Interestingly,the flexible tactile perception platform can find applications in information encryption and decryption.With adoption of cipher,signal transmitted by the perception platform is encrypted.Thus,the security of information transmis-sion is effectively improved.The flexible tactile perceptual platform would have potentials in cognitive wearable devices,advanced human-machine interaction system,and intelligent bionic robots.

Tactile perception of textile fabrics based on friction and brain activation

Tactile perception plays a critical role in the interaction of humans and environment.It begins with the mechanical stimulation induced by friction and is processed in the somatosensory cortex.To quantify the tactile perceptions of textile fabrics,the mechanical properties of fabrics and the features extracted from the friction and vibration signals were correlated with the subjective sensation rated by questionnaires.Meanwhile,the technique of functional magnetic resonance imaging(fMRI)was used to identify the brain areas responsible for the tactile perception of textile fabrics.The results showed that during the tactile perception of textile fabrics,the coefficient of friction increased with the increasing normal load,indicating that the deformation mechanism of skin was relevant to the friction of skin against fabrics.The features of spectral centroid(SC),coefficient of friction,and diameter and critical buckling force of fiber had a strong correlation with the perceived fineness,slipperiness,and prickliness of fabrics,respectively.The postcentral gyrus,supramarginal gyrus,and precentral gyrus,with the corresponding functional regions of the primary somatosensory cortex(SI),secondary somatosensory cortex(SII),primary motor cortex(MI),and secondary motor cortex(MII),were involved with the perceptions of fabric textures.The fiber properties and fabric surface structures that caused the multidimensional feelings tended to induce the large area,intensity,and percent signal change(PSC)of brain activity.This study is meaning for evaluating the tactile stimulation of textile fabrics and understanding the cognitive mechanism in the tactile perception of textile fabrics.

Embodied tactile perception and learning

Various living creatures exhibit embodiment intelligence,which is reflected by a collaborative interaction of the brain,body,and environment.The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation.The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning.Tactile information plays a critical role in this physical interaction process.It can be used to ensure safety,stability,and compliance,and can provide unique information that is difficult to capture using other perception modalities.However,due to the limitations of existing sensors and perception and learning methods,the development of robotic tactile research lags significantly behind other sensing modalities,such as vision and hearing,thereby seriously restricting the development of robotic embodiment intelligence.This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence.Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large-scale tactile array sensing devices,with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.

Memristor-based artificial tactile perception systems with integrated functions of sensing,storage and computing

Constructing an artificial intelligence interactive system is still challenging due to the lack of an integrated artificial sensing and processing system with high performance.In this work,an artificial tactile perception system with integrated sensing,storage,and computing functions is designed based on silk fibroin composite memristors and piezoresistive pressure sensors.The sensors based on polydimethylsiloxane/silver nanowires can sense the external pressure stimulation with fast response speed.In addition,the composite memristor based on silk fibroin possesses good cyclic stability and synaptic plasticity simulation and acts as an artificial synapse to process tactile information.As a result,the integrated tactile perception system realizes the perception,storage,and processing of pressure information,demonstrating the possibility to simulate the biological tactile perception nervous system.This type of system may promote potential applications in artificial intelligence,such as autonomous driving,wearable,flexible electronic devices,and bionic robots.

Metrics analysis of tactile perceptual space based on improved NMDS for leather textures

To solve the fuzzy and unstable tactile similarity relationship between some sample points in the perception experiment,an improved non-metric multidimensional scaling(INMDS)is proposed in this paper.In view of the inconsistency of each sample s contribution,the maximum marginal decision when constructing the perception space to describe the tactile perception characteristics is also proposed.The corresponding constraints are set according to the degree of similarity,and controlling the relaxation variable factor is proposed to optimize the perception dimension and coordinate measurement.The effectiveness of the INMDS algorithm is verified by two perception experiments.The results show that compared with the metric multidimensional scaling(MDS)and non-metric multidimensional scaling(NMDS)algorithms,the perceptual space constructed by INMDS can more accurately reflect the difference relationship between different leather sample points perceived by people.Moreover,the relative position of sample points in the perceptual space is more consistent with subjective perception results.

Investigation of Adhesive Perception Based on Friction and Brain Activation

The enhancement of adhesive perception is crucial to maintaining a stable and comfortable grip of the skin-touch products.To study the tactile perception of adhesive surfaces,subjective evaluation,skin friction and vibrations,and neurophysiological response of the brain activity were investigated systematically.Silicone materials,which are commonly used for bionic materials and skin-touch products,were chosen for the tactile stimulus.The results showed that with the increasing of surface adhesion,the dominant friction transferred from a combination of adhesive friction and deformation friction to adhesive friction.The friction coefficient and vibration amplitude had strong correlations with the perceived adhesion of surfaces.The parietal lobe and occipital lobe were involved in adhesive perceptions,and the area and intensity of brain activation increased with the increasing surface adhesion.Surfaces with larger adhesion tended to excite a high P300 amplitude and short latency,indicating that the judgment was faster and that more attentional resources were involved in adhesive perception.Furthermore,the electroencephalograph signals of the adhesive perception were simulated by the neural mass model.It demonstrated that the excitability and intensity of brain activity,and the connectivity strength between two neural masses increased with the increasing surface adhesion.This study is meaningful to understand the role of surface adhesion in tactile friction and the cognitive mechanism in adhesive perception to improve the tactile experience of adhesive materials.

Highly adaptive triboelectric tactile sensor on the foot of autonomous wall-climbing robots for detecting the adhesion state and avoiding the hazard

Due to the excellent maneuverability and obstacle crossing of legged robots,it is possible for an autonomous legged wallclimbing robots to replace manual inspection of ship exterior panels.However,when the magnetic adsorption legged wallclimbing robot steps on the convex point or convex line of the wall,or even when the robot missteps,the robot is likely to detach from the ferromagnetic wall.Therefore,this paper proposes a tactile sensor for the legged magnetic adsorption wall-climbing robot to detect the magnetic adsorption state and improve the safety of the autonomous crawling of the robot.The tactile sensor mainly comprises a three-dimensional(3D)-printed shell,a tactile slider,and three isometric sensing units,with an optimized geometry.The experiment shows that the triboelectric tactile sensor can monitor the sliding depth of the tactile slider and control the light-emitting device(LED)signal light.In addition,in the demonstration experiment of detecting the adsorption state of the robot's foot,the triboelectric tactile sensor has strong adaptability to various ferromagnetic wall surfaces.Finally,this study establishes a robot gait control system to verify the feedback control ability of the triboelectric tactile sensor.The results show that the robot equipped with the triboelectric tactile sensor can recognize the dangerous area on the crawling wall and autonomously avoid the risk.Therefore,the proposed triboelectric tactile sensor has great potential in realizing the tactile sensing ability of robots and enhancing the safety and intelligent inspection of ultra-large vessels.

Neuromorphic transcutaneous electrical nerve stimulation(nTENS)induces efficient tactile-related cortical networks in forearm amputees

Conventional transcutaneous electrical nerve stimulation(cTENS),which uses a modulated square waveform as stimuli,has been generally used in testing and eliciting artificial tactile perception in forearm amputees.However,a novel neuromorphic TENS(nTENS)model based on neural signals has been largely ignored.In this study,we further explore the effect of nTENS patterns to elicit tactile perception in forearm amputees.Four forearm amputees were recruited to test discriminate tactile perception elicited by different TENS patterns with electroencephalography(EEG)recording at the following four stimulated sites:the index finger and the little finger on both phantom and real sides.Finally,we compared the results of cortical networks in six frequency bands at different stimulated sites between forearm amputees and able-bodied subjects.Behavioral results suggested that n TENS patterns required a lower electric charge at each stimulated site than cTENS patterns.And forearm amputees required a higher intensity in each TENS pattern than able-bodied subjects.Moreover,amputees showed a lower clustering coefficient(aCP),global efficiency(aEG),local efficiency(aEL),and a longer path length(aLP)than able-bodied subjects in all six frequency bands when stimulation was accessed.Specifically,the SMU pattern showed a higher functional network efficiency in real fingers than at phantom sites in theta,alpha,and high gamma bands.This study highlighted the characteristics of n TENS patterns in eliciting tactile perception among forearm amputees,which provided insights into evaluating the neural mechanism of tactile information processing in forearm amputees and building tactile perceptual systems for sensory rehabilitation.

Experiment and modelling of texture and sliding direction dependence on finger friction behavior

Humans rely on their fingers to sense and interact with external environment.Understanding the tribological behavior between finger skin and object surface is crucial for various fields,including tactile perception,product appearance design,and electronic skin research.Quantitatively describing finger frictional behavior is always challenging,given the complex structure of the finger.In this study,the texture and sliding direction dependence of finger skin friction was quantified based on explicit mathematic models.The proposed double-layer model of finger skin effectively described the nonlinear elastic response of skin and predicted the scaling-law of effective elastic modulus with contact radius.Additionally,the skin friction model on textured surface considering adhesion and deformation factors was established.It revealed that adhesive term dominated finger friction behavior in daily life,and suggested that object texture size mainly influenced friction-induced vibrations rather than the average friction force.Combined with digital image correlation(DIC)technique,the effect of sliding direction on finger friction was analyzed.It was found that the anisotropy in finger friction was governed by the finger’s ratchet pawl structure,which also contributes to enhanced stick-slip vibrations in the distal sliding direction.The proposed friction models can offer valuable insights into the underlying mechanism of skin friction under various operating conditions,and can provide quantitative guidance for effectively encoding friction into haptics.

Triboelectric nanogenerator for neuromorphic electronics

Building the brain-inspired neural network computing system based neuromorphic electronics is an effective approach to break the von Neumann bottleneck on the hardware level and realize the information processing with high efficiency and low energy consumption in this big data explosion age.Triboelectric nanogenerator(TENG)has two functions of sensing and energy conversion,which promote the application as sensor and/or power supply in self-powered neuromorphic electronics for data storage and biological synapse/neuron behaviors mimicking.This article highlights the relevant works of TENGs for memory devices,artificial synapses and artificial neurons,performs a systematic comparison,and puts forward the future research possibilities and challenges,with the hope of attracting more researchers into this field and promoting the development of TENG based neuromorphic electronics.