CN114935421A

CN114935421A - Touch sensing system and method based on friction nano generator

Info

Publication number: CN114935421A
Application number: CN202210361749.3A
Authority: CN
Inventors: 丁文伯; 宋子午; 杨昉; 宋健; 王季宇
Original assignee: Tsinghua University
Current assignee: Tsinghua University
Priority date: 2022-04-07
Filing date: 2022-04-07
Publication date: 2022-08-23

Abstract

The application discloses touch perception system and method based on friction nanometer generator, wherein, the system includes: a tactile sensor based on a friction nanogenerator for generating an electrical signal when the tactile sensor is rubbed with a friction surface; the signal processing module is used for processing the electric signals to obtain the processed electric signals meeting preset conditions and identifying actual surface textures, actual contact pressure and/or actual material types of the friction surface; and the communication module is used for sending the actual surface texture, the actual contact pressure and/or the actual material type to a preset terminal. The system of this application embodiment can utilize single touch sensor simultaneously, discerns the surface texture and the material kind on friction surface or discerns the surface texture and the contact pressure on friction surface simultaneously, when effectively guaranteeing the discernment degree of accuracy of texture and material, simple easy realization easily deploys on the robot that the consumption is lower, the volume is light, effectively satisfies the user demand.

Description

Touch sensing system and method based on friction nano generator

Technical Field

The application relates to the technical field of electronic skins, in particular to a touch sensing system and method based on a friction nano generator.

Background

The human skin has fine and smooth touch perception on hardness, roughness, temperature, vibration and the like, and the hand muscles exert reasonable force to carry out self-adaptive grasping by comprehensively analyzing various touch information of an object. Under the assistance of the electronic skin, the intelligent robot system or the artificial limb can also carry out motion trail planning, object manipulation and safe operation by utilizing the tactile information, and acquire various information from the environment. For rigid body robots with higher and higher degrees of freedom, the traditional torque sensor is expensive and difficult to deploy, so the development of flexible and low-cost electronic skin is an important step for realizing efficient sensing and control.

Although various sensing technologies applied to robot tactile sensing, including piezoresistive arrays, soft optical strain sensors, magnetic micro-electro-mechanical systems, biomimetic capacitive arrays, and piezoelectric tactile sensing systems, have been widely studied, the related technologies focus on sensing of pressure, texture, and object shape in a large amount, and it is difficult to realize material recognition. Currently, it is still a challenge for a tactile sensing system to simultaneously enable identification of surface material type and texture type of an object.

In addition, a large-scale data processing algorithm is generally required for simultaneously identifying high-precision materials and textures, and in the data analysis link of the touch perception system, data analysis of related technologies is mostly established on a high-performance desktop computing platform, so that the related technologies cannot be deployed on a robot with low power consumption and light volume, and higher data delay can be brought in data transmission and communication.

In summary, the related art cannot recognize the types of surface materials and the types of textures of the object, and the high-precision material and texture recognition algorithm cannot be deployed on a robot with low power consumption and light volume, so the improvement is still needed.

Disclosure of Invention

The application provides a touch perception system and method based on a friction nanometer generator, and aims to solve the technical problems that in the related art, surface textures and material types cannot be identified simultaneously through a single touch sensor or the surface texture types and the contact pressure cannot be identified simultaneously, and then the touch perception system and method are difficult to deploy on a robot with low power consumption and light volume and have high data delay in data transmission and communication.

The embodiment of the first aspect of the present application provides a haptic perception system based on a friction nanometer generator, including: a tactile sensor based on a friction nanogenerator for generating an electrical signal when the tactile sensor is rubbed with a friction surface; the signal processing module is used for processing the electric signals to obtain the electric signals meeting preset conditions after processing, generating surface texture signals, pressure signals and/or material type signals of the friction surface, and identifying actual surface textures, actual contact pressure and/or actual material types of the friction surface; and a communication module for transmitting the actual surface texture, the actual contact pressure and/or the actual material type to a preset terminal.

Optionally, in an embodiment of the present application, the friction nanogenerator-based tactile sensor body includes a flexible substrate, a first electrode and a second electrode attached to an upper surface and a lower surface of the flexible substrate, and a flexible friction layer closely attached to the flexible substrate and the first electrode and the second electrode.

Optionally, in one embodiment of the present application, the material of the flexible substrate includes one or more of polydimethylsiloxane, polyethylene, polypropylene, polyvinylidene fluoride, perfluoroethylene propylene, vinylidene chloride acrylonitrile copolymer, polytetrafluoroethylene, polyvinyl chloride, polychloroprene, polyisobutylene, polyoxymethylene, polyamide, polyimide, melamine formaldehyde, polycarbonate, polyethylene glycol succinate, phenolic resin, aniline formaldehyde resin, neoprene, natural rubber, cellulose, ethyl cellulose, cellulose acetate, polyethylene adipate, polydiallyl phthalate, polyvinyl butyral, styrene propylene copolymer, styrene butadiene copolymer, polyethylene propylene carbonate, polystyrene, polymethacrylate, polyester, and polyurethane.

Optionally, in an embodiment of the present application, each of the first electrode and the second electrode includes a plurality of sub-electrodes, wherein a thickness of each sub-electrode layer is smaller than that of the flexible substrate, and each sub-electrode layer is closely attached to the flexible substrate.

Optionally, in an embodiment of the present application, a material of the plurality of sub-electrodes includes gold, silver, platinum, palladium, aluminum, nickel, copper, titanium, chromium, selenium, iron, manganese, molybdenum, tungsten, or vanadium, one or more of an aluminum alloy, a titanium alloy, a magnesium alloy, a beryllium alloy, a copper alloy, a zinc alloy, a manganese alloy, a nickel alloy, a lead alloy, a tin alloy, a cadmium alloy, a bismuth alloy, an indium alloy, a gallium alloy, a tungsten alloy, a molybdenum alloy, a niobium alloy, a tantalum alloy, graphite, and conductive glass.

Optionally, in one embodiment of the present application, the material of the flexible friction layer includes one or more of polydimethylsiloxane, polyethylene, polypropylene, polyvinylidene fluoride, perfluoroethylene propylene, vinylidene chloride acrylonitrile copolymer, polytetrafluoroethylene, polyvinyl chloride, polychloroprene, polyisobutylene, polyoxymethylene, polyamide, polyimide, melamine formaldehyde, polycarbonate, polyethylene glycol succinate, phenolic resin, aniline formaldehyde resin, neoprene, natural rubber, cellulose, ethyl cellulose, cellulose acetate, polyethylene adipate, polydiallyl phthalate, polyvinyl butyral, styrene propylene copolymer, styrene butadiene copolymer, polyethylene propylene carbonate, polystyrene, polymethacrylate, polyester, and polyurethane.

Optionally, in an embodiment of the present application, the signal processing module includes: the signal conditioning unit is provided with a multi-channel trans-impedance amplifier in an integrated manner, and each trans-impedance amplifier is provided with a current amplifier in an integrated manner so as to amplify the electric signal; a control unit provided with an analog-to-digital converter to convert a voltage signal from the processed electrical signal; and the signal processing unit is used for acquiring a surface texture signal, a pressure signal and/or a material type signal of the friction surface according to the voltage signal, and extracting texture features, pressure features and/or material features so as to identify the actual surface texture, the actual contact pressure and/or the actual material type based on the texture features, the pressure features and/or the material features.

Optionally, in an embodiment of the present application, the method further includes: and the rectifying circuit is arranged between the signal conditioning unit and the control unit so as to scale the voltage of the electric signal to a preset interval.

Optionally, in an embodiment of the present application, the signal processing unit is further configured to: performing band-pass filtering processing on the voltage signal to obtain a signal with noise removed; based on the signal, performing sliding sampling on the time sequence by using a sliding window with a preset width, and segmenting to obtain a feature pre-extraction signal; and acquiring macro features and micro features of the signals according to the surface texture signals, the pressure signals and/or the material type signals in the feature pre-extraction signals, determining the actual material type or the actual contact pressure according to the macro features, and determining the actual surface texture according to the micro features.

Optionally, in an embodiment of the present application, the obtaining of the macro features and the micro features of the signal from the surface texture signal, the pressure signal, and/or the material type signal in the feature pre-extraction signal includes: constructing a macroscopic feature space and a microscopic feature space based on the feature pre-extraction signal, and respectively calculating wavelet bases of the macroscopic feature space and the microscopic feature space to obtain the macroscopic feature and the microscopic feature; or constructing a low-pass filter and a high-pass filter based on the feature pre-extraction signal, and respectively calculating the amplitude-frequency characteristics of the low-pass filter and the high-pass filter to obtain the macroscopic features and the microscopic features.

The embodiment of the second aspect of the present application provides a touch sensing method based on a friction nano-generator, which adopts the above touch sensing system based on a friction nano-generator, wherein the method includes the following steps: collecting an electric signal generated when the touch sensor rubs with a friction surface;

processing the electric signal to obtain the electric signal which meets preset conditions after processing, generating a surface texture signal, a pressure signal and/or a material type signal of the friction surface, and identifying the actual surface texture, the actual contact pressure and/or the actual material type of the friction surface; and sending the actual surface texture, the actual contact pressure and/or the actual material type to a preset terminal.

Optionally, in an embodiment of the application, the processing the electrical signal to obtain an electrical signal that satisfies a preset condition after being processed, generating a surface texture signal, a pressure signal, and/or a material type signal of the friction surface, and identifying an actual surface texture, an actual contact pressure, and/or an actual material type of the friction surface includes: performing signal amplification processing on the electric signal; converting the processed electrical signal into a voltage signal; and acquiring a surface texture signal, a pressure signal and/or a material type signal of the friction surface according to the voltage signal, and extracting texture features, pressure features and/or material features to identify the actual surface texture, actual contact pressure and/or actual material type based on the texture features, pressure features and/or material features.

Optionally, in an embodiment of the present application, acquiring a surface texture signal, a pressure signal, and/or a material type signal of the friction surface according to the voltage signal, and extracting texture features, pressure features, and/or material features to identify the actual surface texture, actual contact pressure, and/or actual material type based on the texture features, pressure features, and/or material features includes: performing band-pass filtering processing on the voltage signal to obtain a signal with noise removed; based on the signal, performing sliding sampling on the time sequence by using a sliding window with a preset width, and segmenting to obtain a feature pre-extraction signal; and acquiring macro features and micro features of the signals according to the surface texture signals, the pressure signals and/or the material type signals in the feature pre-extraction signals, determining the actual material type or the actual contact pressure according to the macro features, and determining the actual surface texture according to the micro features.

Optionally, in an embodiment of the present application, before generating the voltage signal from the processed electrical signal, the method further includes: and scaling the voltage of the electric signal to a preset interval.

An embodiment of a third aspect of the present application provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the friction nanogenerator-based haptic sensation method as described in the embodiments above.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing computer instructions for causing a computer to perform a friction nanogenerator-based haptic sensation method as described in the above embodiments.

The embodiment of the application can process and simultaneously identify the actual surface texture, the actual contact pressure and/or the actual material type of the friction surface based on the electric signal generated when the touch sensor in the friction nano generator rubs with the friction surface, and the electronic skin based on the friction nano generator has the characteristics of super stretchability, high sensitivity, wide sensing range and the like, so that the output of the friction nano generator has sensitive output change for different friction materials, and the high-precision real-time identification of the surface texture, the contact pressure and/or the material type can be simultaneously realized only by a single touch sensor. Therefore, the technical problems that in the related art, the surface texture and the material type cannot be identified simultaneously or the surface texture type and the contact pressure cannot be identified simultaneously through a single touch sensor, and then the touch sensor is difficult to deploy on a robot with low power consumption and light volume and has high data delay in data transmission and communication are solved.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a schematic structural diagram of a friction nanogenerator-based haptic sensation system according to an embodiment of the application;

FIG. 2 is a block diagram of a tactile sensor of a friction nanogenerator based tactile sensation system according to an embodiment of the application;

FIG. 3 is a signal processing circuit in a signal processing module of a friction nanogenerator based haptic sensation system according to one embodiment of the application;

FIG. 4 is a model of a deep learning classification algorithm in a signal processing module of a friction nanogenerator based haptic sensation system according to an embodiment of the application;

FIG. 5 is a schematic diagram of the operation of a friction nanogenerator based haptic sensation system according to one embodiment of the application;

FIG. 6 is a flow chart of a method for tactile sensing based on a friction nanogenerator according to an embodiment of the application;

fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.

The following describes a friction nanogenerator-based haptic sensation system and method according to an embodiment of the present application with reference to the drawings. Aiming at the technical problems that the surface texture and the material type cannot be identified simultaneously or the surface texture type and the contact pressure cannot be identified simultaneously through a single touch sensor in the related technology mentioned in the background technology center, and further the touch sensor is difficult to be deployed on a robot with low power consumption and light volume, and higher data delay exists in data transmission and communication, the application provides a touch sensing system based on a friction nano generator, in the system, the actual surface texture, the actual contact pressure and/or the actual material type of the friction surface can be processed and identified simultaneously based on an electric signal generated when the touch sensor in the friction nano generator is in friction with the friction surface, and the electronic skin based on the friction nano generator has the characteristics of super stretchability, high sensitivity, wide sensing range and the like, so that the output of the friction nano generator has sensitive output change for different friction materials, the touch sensor can realize high-precision real-time identification of surface textures, contact pressure and/or material types by only using a single touch sensor, and has the advantages of simple structure, easy manufacture and low cost. Therefore, the technical problems that in the related art, the surface texture and the material type cannot be identified simultaneously or the surface texture type and the contact pressure cannot be identified simultaneously through a single touch sensor, and then the touch sensor is difficult to deploy on a robot with low power consumption and light volume and has high data delay in data transmission and communication are solved.

Specifically, fig. 1 is a schematic structural diagram of a friction nanogenerator-based haptic sensing system provided in an embodiment of the application.

As shown in fig. 1, the friction nanogenerator-based haptic sensation system 10 includes: a friction nanogenerator 100, a signal processing module 200, and a communication module 300.

In particular, the tactile sensor 100 is based on a triboelectric nanogenerator for generating an electrical signal when the tactile sensor 100 is rubbed with a friction surface.

In actual implementation, the friction nanogenerator-based tactile sensor 100 can generate an electrical signal carrying material texture information when rubbing on different materials and different textured surfaces. In the embodiment of the present application, the electrical signal generated when the tactile sensor 100 rubs against the friction surface is collected, which may lay a foundation for subsequent electrical signal processing and identification of the actual surface texture and the actual material type of the friction surface or identification of the surface texture type and the contact pressure of the friction surface. The embodiment of the application has the advantages that the electronic skin based on the friction nano generator has the characteristics of super stretchability, high sensitivity, wide sensing range and the like, so that the output of the friction nano generator has sensitive output change for different friction materials, high-precision real-time identification on materials and texture types can be realized, the structure is simple, the manufacturing is easy, and the cost is low.

Optionally, in one embodiment of the present application, the friction nanogenerator-based tactile sensor 100 includes: the flexible friction layer is closely attached to the flexible substrate, the first electrode and the second electrode.

By way of example, the structure of the tactile sensor 100 may be as shown in FIG. 2:

the tactile sensor 100 has a layered structure, has certain flexibility, and can be restored to deform within a certain degree; the base layer 101 can be fixed on the surface of the robot in a sticking mode, can adapt to the curvature of the surface of the robot and can be tightly attached; the electrode layer 102 is attached to the substrate layer and comprises two interdigital electrodes which are not communicated with each other; the rubbing layer 103 is in close contact with the base layer 101 and the electrode layer 102, and charges are transferred when rubbing against an external article.

Specifically, the touch sensor 100 based on the friction nano-generator in the embodiment of the present application includes two sets of electrodes attached to the surface of the flexible substrate and friction layers attached to the flexible substrate and the electrodes, specifically, the flexible substrate, the first electrode and the second electrode attached to the upper surface and the lower surface of the flexible substrate, and the flexible friction layers attached to the flexible substrate, the first electrode, and the second electrode.

The embodiment of the application has the advantages that the electronic skin based on the friction nano generator has the characteristics of super stretchability, high sensitivity, wide sensing range and the like, so that the output of the friction nano generator has sensitive output change for different friction materials, high-precision real-time identification of materials and texture types can be realized, the structure is simple, the manufacturing is easy, and the cost is low.

Optionally, in one embodiment of the present application, the material of the flexible substrate comprises one or more of polydimethylsiloxane, polyethylene, polypropylene, polyvinylidene fluoride, perfluoroethylene propylene, vinylidene chloride acrylonitrile copolymer, polytetrafluoroethylene, polyvinyl chloride, polychlorotrifluoroethylene, polychloroprene, polyisobutylene, polyoxymethylene, polyamide, polyimide, melamine formaldehyde, polycarbonate, polyethylene glycol succinate, phenolic resin, aniline formaldehyde resin, neoprene, natural rubber, cellulose, ethyl cellulose, cellulose acetate, polyethylene adipate, polydiallyl phthalate, polyvinyl butyral, styrene propylene copolymer, styrene butadiene copolymer, polyethylene propylene carbonate, polystyrene, polymethacrylate, polyester, and polyurethane.

It will be appreciated that the flexible substrate is highly flexible, insulating, and the like, and is suitable for attachment to a variety of surfaces of greater curvature, and its shape can be customized to the size of the surface to be laid, wherein the choice of substrate material includes, but is not limited to, any of the following:

polydimethylsiloxane, polyethylene, polypropylene, polyvinylidene fluoride, perfluoroethylene propylene, vinylidene chloride acrylonitrile copolymer, polytetrafluoroethylene, polyvinyl chloride, polychlorotrifluoroethylene, polychloroprene, polyisobutylene, polyoxymethylene, polyamide, polyimide, melamine formaldehyde, polycarbonate, polyethylene glycol succinate, phenolic resin, aniline formaldehyde resin, neoprene, natural rubber, cellulose, ethyl cellulose, cellulose acetate, polyethylene glycol adipate, polydiallyl phthalate, polyvinyl butyral, styrene propylene copolymer, styrene butadiene copolymer, polyethylene propylene carbonate, polystyrene, polymethacrylate, polyester, and polyurethane.

It should be noted that, the skilled person can select a suitable substrate material according to practical situations, and the substrate material is not limited in particular.

Optionally, in an embodiment of the present application, each of the first electrode and the second electrode includes a plurality of sub-electrodes, wherein each of the sub-electrodes has a smaller layer thickness than the flexible substrate and is disposed in close contact with the flexible substrate.

In the actual implementation process, the two groups of electrodes attached to the flexible substrate, namely the first electrode and the second electrode, are composed of a plurality of sub-electrodes which are uniformly distributed on the flexible substrate, the thickness of each sub-electrode layer is smaller than that of the flexible substrate, each sub-electrode layer is tightly attached to the flexible substrate, and the sub-electrodes can deform along with the flexible substrate and do not fall off. The sub-electrodes are divided into two groups, the sub-electrodes belonging to the same group are mutually communicated, and the sub-electrodes belonging to different groups are mutually not communicated and are not contacted.

Optionally, in one embodiment of the present application, the material of the plurality of sub-electrodes includes gold, silver, platinum, palladium, aluminum, nickel, copper, titanium, chromium, selenium, iron, manganese, molybdenum, tungsten, or vanadium, one or more of an aluminum alloy, a titanium alloy, a magnesium alloy, a beryllium alloy, a copper alloy, a zinc alloy, a manganese alloy, a nickel alloy, a lead alloy, a tin alloy, a cadmium alloy, a bismuth alloy, an indium alloy, a gallium alloy, a tungsten alloy, a molybdenum alloy, a niobium alloy, a tantalum alloy, graphite, and conductive glass.

It will be understood by those skilled in the art that the material of the plurality of sub-electrodes may be any material that is electrically conductive and can be attached to the surface of the flexible substrate, and the selection includes, but is not limited to, any of the following materials:

gold, silver, platinum, palladium, aluminum, nickel, copper, titanium, chromium, selenium, iron, manganese, molybdenum, tungsten or vanadium, an aluminum alloy, a titanium alloy, a magnesium alloy, a beryllium alloy, a copper alloy, a zinc alloy, a manganese alloy, a nickel alloy, a lead alloy, a tin alloy, a cadmium alloy, a bismuth alloy, an indium alloy, a gallium alloy, a tungsten alloy, a molybdenum alloy, a niobium alloy or a tantalum alloy.

In addition to metallic materials, such as: graphite, conductive glass, and the like, and non-metallic conductive materials with good conductivity can also be candidate materials for the sub-electrodes.

It should be noted that, a person skilled in the art may select a suitable sub-electrode material according to practical situations, and is not limited herein.

Optionally, in one embodiment of the present application, the material of the flexible friction layer includes one or more of polydimethylsiloxane, polyethylene, polypropylene, polyvinylidene fluoride, perfluoroethylene propylene, vinylidene chloride acrylonitrile copolymer, polytetrafluoroethylene, polyvinyl chloride, polychloroprene, polyisobutylene, polyoxymethylene, polyamide, polyimide, melamine formaldehyde, polycarbonate, polyethylene glycol succinate, phenol formaldehyde, aniline formaldehyde, neoprene, natural rubber, cellulose, ethyl cellulose, cellulose acetate, polyethylene adipate, polydiallyl phthalate, polyvinyl butyral, styrene propylene copolymer, styrene butadiene copolymer, polyethylene propylene carbonate, polystyrene, polymethacrylate, polyester, and polyurethane.

It can be understood that the flexible friction layer covers the flexible substrate and the surface of the interdigital electrode, and when the flexible friction layer is in contact friction with an external object, charge transfer and accumulation can be generated, so that an electric signal can be acquired. Wherein the amount of charge transfer is related to the electronegativity of the friction material and the contacted object.

The material selection of the flexible friction layer includes, but is not limited to, any one of the following materials:

polydimethylsiloxane, polyethylene, polypropylene, polyvinylidene fluoride, perfluoroethylene propylene, vinylidene chloride acrylonitrile copolymer, polytetrafluoroethylene, polyvinyl chloride, polychlorotrifluoroethylene, polychloroprene, polyisobutylene, polyoxymethylene, polyamide, polyimide, melamine formaldehyde, polycarbonate, polyethylene glycol succinate, phenol resin, aniline formaldehyde resin, chloroprene rubber, natural rubber, cellulose, ethyl cellulose, cellulose acetate, polyethylene glycol adipate, polydiallyl phthalate, polyvinyl butyral, styrene propylene copolymer, styrene butadiene copolymer, polyethylene propylene carbonate, polystyrene, polymethacrylate, polyester, polyurethane, and the like.

It should be noted that, the skilled person can select a suitable flexible friction layer material according to the actual situation, and the invention is not limited in particular.

The signal processing module 200 is configured to process the electrical signal to obtain an electrical signal meeting a preset condition after processing, generate a surface texture signal, a pressure signal and/or a material type signal of the friction surface, and identify an actual surface texture, an actual contact pressure and/or an actual material type of the friction surface.

As a possible implementation manner, the signal processing module 200 in this embodiment may implement conversion, amplification, noise reduction, conditioning of the micro current signal to the voltage signal, and analysis and processing of the acquired signal by using a feature extraction algorithm, and the working principle of which is shown in fig. 3.

Specifically, the signal processing module 200 may process the electrical signal to obtain the electrical signal meeting the preset condition after processing, and generate a surface texture signal and a pressure sensing signal of the corresponding friction surface, or a surface texture signal and a material type signal, so as to identify an actual surface texture and an actual contact pressure of the friction surface, or an actual surface texture and an actual material type. The embodiment of the application discloses tactile perception system based on friction nanometer generator, can be with the signal of telecommunication that produces when friction nanometer generator and friction surface rub, handle and discern the actual surface texture and the actual contact pressure of friction surface, or actual surface texture and actual material kind, electron skin based on friction nanometer generator has super stretchability, characteristics such as high sensitivity and wide sensing range, make the output of friction nanometer generator have sensitive output change to different friction materials, can realize carrying out the real-time identification of high accuracy to material and texture kind simultaneously, and simple structure, easily preparation, the cost is lower.

It should be noted that the electrical signals meeting the preset condition after being processed may be divided into macroscopic signals and microscopic signals, where the macroscopic signals may be pressure signals or material type signals, and the microscopic signals may be surface texture signals, so that the embodiment of the present application may identify the actual surface texture and the actual contact pressure of the friction surface, or the actual surface texture and the actual material type, by combining the macroscopic signals and the microscopic signals.

Optionally, in an embodiment of the present application, the signal processing module 200 includes: signal conditioning unit, control unit and signal processing unit.

The signal conditioning unit and the signal conditioning unit are integrally provided with a multi-channel trans-impedance amplifier, and each trans-impedance amplifier is integrally provided with a current amplifier so as to amplify and process the electric signals.

And the control unit is provided with an analog-to-digital converter so as to convert the processed electric signals into voltage signals.

And the signal processing unit is used for acquiring a surface texture signal, a pressure signal and/or a material type signal of the friction surface according to the voltage signal, and extracting texture features, pressure features and/or material features so as to identify actual surface texture, actual contact pressure and/or actual material types based on the texture features, the pressure features and/or the material features.

Specifically, the signal conditioning unit integrates a multi-channel trans-impedance amplifier, and a high-precision micro current amplifier is integrated in each trans-impedance amplifier, so that alternating current signals of pico-ampere and nano-ampere levels can be amplified, converted into voltage signals and transmitted into a rear-end rectifying circuit. The amplification factor of each trans-impedance amplifier can be adjusted by adjusting the corresponding matching resistor.

Further, the control unit can acquire a voltage signal output by the rectifying circuit through a built-in analog-to-digital converter. The quantization precision of the signal is related to the control chip, and the sampling frequency of the signal is related to the chip performance adopted by the control unit and the efficiency of the program. The control chip can be selected from any one of the following single-chip microcomputers:

nordic series, STM8 series, STM32 series, Arduino series, ESP8266 series, ESP32 series, STC89C51/52 series, NXP K60 series, and the like.

It should be noted that, a person skilled in the art may select a suitable single chip according to the actual situation, and is not limited herein.

The deep learning model deployed in the control chip is shown in fig. 4, after receiving signals input by the acquisition module, the deep learning model decomposes the signals into macroscopic features and microscopic features through wavelet decomposition, then the macroscopic features and the microscopic features are respectively input into two mutually independent one-dimensional convolutional neural networks, the upper neural network outputs material type information, the lower neural network outputs texture type information, and identification of object types is realized by combining material types or contact pressure and texture types.

Furthermore, the signal processing algorithm in the signal processing unit is a segmentation sampling algorithm, and may be composed of a preprocessing algorithm, a data segmentation algorithm, a feature pre-extraction algorithm, and a classification algorithm. The signal processing algorithm takes the voltage signal collected by the analog-to-digital converter as input and outputs the classification result of the touched object.

The signal processing algorithm segmentation sampling algorithm required in the actual execution process of the embodiment of the application can be deployed in the control unit through a programming method and comprises a preprocessing algorithm, a data segmentation algorithm, a feature pre-extraction algorithm and a classification algorithm. The signal processing algorithm takes the voltage signal collected by the analog-to-digital converter as input and outputs the classification result of the touched object.

The data preprocessing algorithm can perform band-pass filtering processing on the input voltage signal and remove noise caused by electromagnetic interference with a fixed frequency range in the environment.

The data segmentation algorithm can use a sliding window with a fixed width N to perform sliding sampling on a time sequence, and every time when a plurality of new data are updated, the algorithm selects N newly generated data points to form a time sequence x (t) with the length of N.

The feature pre-extraction algorithm comprises two alternatives, wherein x (t) is used as input, and macroscopic features and microscopic features of signals are output. Optionally, in an embodiment of the present application, the obtaining the macro features and the micro features of the signal from the surface texture signal, the pressure signal, and/or the material type signal in the feature pre-extraction signal includes: constructing a macroscopic feature space and a microscopic feature space based on the feature pre-extraction signal, and respectively calculating wavelet bases of the macroscopic feature space and the microscopic feature space to obtain macroscopic features and microscopic features; or constructing a low-pass filter and a high-pass filter based on the feature pre-extraction signal, and respectively calculating the amplitude-frequency characteristics of the low-pass filter and the high-pass filter to obtain the macroscopic features and the microscopic features.

Specifically, the feature pre-extraction algorithm, including two alternatives, may be implemented in any one of the first alternative and the second alternative. And both the first scheme and the second scheme take x (t) as input and output macroscopic features and microscopic features of signals.

The first scheme is as follows: the embodiment of the application can construct two feature spaces: macroscopic feature space V _j And a microscopic feature space W _j . Wherein, V _j From a wavelet basis phi _j,n (t) stretch forming, W _j From wavelet basis psi _j,n (t) tensioning. The feature pre-extraction algorithm takes the x (t) as input and respectively calculates the x (t) at V according to the following formula _j And W _j Is projected.

Computing wavelet basis phi of macroscopic feature space _j,n (t)：

φ _j (t)＝2 ^j/2 φ ₀ (2 ^j t)，

φ _j,n (t)＝φ _j (t-2 ^-j n)＝2 ^j/2 φ ₀ (2 ^j t-n)。

Computing wavelet basis psi of microscopic feature space _j,n (t)：

ψ _j (t)＝2 ^j/2 ψ ₀ (2 ^j t)，

ψ _j,n (t)＝ψ _j (t-2 ^-j n)＝2 ^j/2 ψ ₀ (2 ^j t-n)。

Defining an inner product operation between the signal and the wavelet basis:

wherein x is _ma (t) is a macroscopic feature, namely x (t) in space V _j Projection of (2), x _mi (t) is a microscopic feature, namely x (t) in space W _j Is projected.

Scheme II: the embodiment of the application can construct two filters, namely a low-pass filter F _L (t) and a high-pass filter F _H (t) of (d). Wherein, F _L (t) and F _H (t) respective amplitude-frequency characteristics G _L (j ω) and G _H (j ω) have the same cut-off frequency. Wherein G is _L (j ω) and G _H (j ω) is calculated as follows:

macro and micro features are x (t) and F respectively _L (t) and F _H Convolution of (t):

wherein x is _ma (t) is a macroscopic feature, x _mi And (t) is a microscopic feature.

Further, the signal processing unit may be configured to obtain a surface texture signal and a material type signal or a pressure sensing signal and a material type signal of the friction surface according to the voltage signal, and extract texture features and material features or texture features and pressure features to identify an actual surface texture and an actual material type or an actual surface texture and an actual contact pressure based on the texture features and the material features.

For the above proposed classification algorithm, the signal processing unit has two mutually independent data processing lines: line one and line two. The data processing algorithms of the two lines can be selected respectively, and the data processing processes are independent.

A first circuit: and (4) outputting a classification algorithm for the identification result of the material type by taking the macroscopic characteristics as input. An alternative algorithm for routing includes, but is not limited to, any of the following:

a support vector machine, a naive Bayes method, a deep learning classification algorithm based on a one-dimensional convolutional neural network, a deep learning algorithm based on a long-short term memory network and the like.

A second circuit: and outputting a classification algorithm of the identification result of the texture type by taking the microscopic features as input. The second line alternative algorithm includes but is not limited to any of the following:

support vector machine, naive Bayes method, deep learning classification algorithm based on one-dimensional convolutional neural network, and deep learning algorithm based on long-short term memory network.

In summary, the material type and texture type or the contact pressure and texture type output by the first line and the second line are the final output result of the signal processing unit.

A communication module 300 for transmitting the actual surface texture and/or the actual material type to a predetermined terminal.

In the actual implementation process, the communication module 300 may be integrated on a control chip (for example, a single chip microcomputer with a built-in wireless communication module such as Nordic series and ESP32 series), or may be independent from the control chip (for example, a single chip microcomputer without a built-in communication module such as STM32 series). Its communication principle may be based on, but not limited to: ordinary bluetooth, low power bluetooth, wireless networks, etc.

Specifically, the communication module 300 may digitally encode the classification results and then transmit to a remote device. For example, the communication module 300 may transmit the raw data and the analysis result in real time using bluetooth or a wireless local area network.

Optionally, in an embodiment of the present application, the friction nanogenerator-based haptic sensation system 10 further includes: a rectifier circuit.

The rectifying circuit is arranged between the signal conditioning unit and the control unit and used for scaling the voltage of the electric signal to a preset interval.

As a possible implementation manner, the rectifying circuit may migrate the ac signal and scale the ac signal to a preset interval of the control unit, where the preset interval is a tolerance range of the control unit, and a specific value of the ac signal may be set by a person skilled in the art according to an actual situation by adjusting parameters of a corresponding component in the circuit.

The friction nanogenerator-based haptic sensation system 10 according to one embodiment of the present application will be described in detail with reference to fig. 2 to 5.

The friction nanogenerator-based haptic sensation system 10 according to the embodiment of the application includes: a tactile sensor 100, a signal processing module 200, a communication module 300, and a rectifying circuit.

The tactile sensor 100 comprises a flexible substrate, two sets of electrodes attached to the surface of the flexible substrate, and a friction layer attached to the flexible substrate and the electrodes. In actual implementation, the friction nanogenerator-based tactile sensor 100 can generate an electrical signal carrying material texture information when rubbing on different materials and different textured surfaces.

Specifically, the tactile sensor 100 has a layered structure, has a certain flexibility, and can be deformed in a recoverable manner within a certain degree; the base layer 101 can be fixed on the surface of the robot in a sticking mode, can adapt to the curvature of the surface of the robot and can be tightly attached to the surface of the robot; the electrode layer 102 is attached to the substrate layer and comprises two interdigital electrodes which are not communicated with each other; the rubbing layer 103 is in close contact with the base layer 101 and the electrode layer 102, and charges are transferred when rubbing against an external article.

It will be appreciated that the flexible substrate is highly flexible, insulating, and the like, and is suitable for attachment to a variety of surfaces of greater curvature, and may be shaped to suit the size of the surface to be laid, wherein the substrate material may be selected from any of the following materials, but is not limited to:

In the actual implementation process, the two groups of electrodes attached to the flexible substrate, namely the first electrode and the second electrode, are composed of a plurality of sub-electrodes uniformly distributed on the flexible substrate, the thickness of each sub-electrode layer is smaller than that of the flexible substrate, the sub-electrodes are tightly attached to the flexible substrate and can deform along with the flexible substrate without falling off. The sub-electrodes are divided into two groups, the sub-electrodes belonging to the same group are mutually communicated, and the sub-electrodes belonging to different groups are mutually not communicated and are not contacted.

polydimethylsiloxane, polyethylene, polypropylene, polyvinylidene fluoride, perfluoroethylene propylene, vinylidene chloride acrylonitrile copolymer, polytetrafluoroethylene, polyvinyl chloride, polychlorotrifluoroethylene, polychloroprene, polyisobutylene, polyoxymethylene, polyamide, polyimide, melamine formaldehyde, polycarbonate, polyethylene glycol succinate, phenol resin, aniline formaldehyde resin, chloroprene rubber, natural rubber, cellulose, ethyl cellulose, cellulose acetate, polyethylene adipate, polydiallyl phthalate, polyvinyl butyral, styrene propylene copolymer, styrene butadiene copolymer, polyethylene propylene carbonate, polystyrene, polymethacrylate, polyester, polyurethane, and the like.

It should be noted that, the skilled person can select a suitable flexible friction layer material according to practical situations, and the invention is not limited in particular.

The signal processing module 200, the signal processing module 200 may process the electrical signal to obtain the electrical signal satisfying the preset condition after processing, generate a surface texture signal, a pressure signal and/or a material type signal of the friction surface, and identify an actual surface texture, an actual contact pressure and/or an actual material type of the friction surface.

The signal processing module 200 includes: signal conditioning unit, control unit and signal processing unit.

Further, the control unit can acquire a voltage signal output by the rectifying circuit through a built-in analog-to-digital converter. The quantization precision of the signal is related to the control chip, and the sampling frequency of the signal is related to the chip performance adopted by the control unit and the efficiency of the program. The selection of the control chip includes but is not limited to any one of the following series of single-chip microcomputers:

The deep learning model deployed in the control chip is shown in fig. 4, after receiving signals input by the acquisition module, the model firstly decomposes the signals into macroscopic features and microscopic features through wavelet decomposition, then the macroscopic features and the microscopic features are respectively input into two mutually independent one-dimensional convolutional neural networks, the upper neural network outputs material type information, the lower neural network outputs texture type information, and the identification of object types is realized by combining pressure sensing and texture types or material types and texture types. The signal processing algorithm segmentation sampling algorithm required in the actual execution process can be deployed in the control unit through a programming method and comprises a preprocessing algorithm, a data segmentation algorithm, a feature pre-extraction algorithm and a classification algorithm. The signal processing algorithm takes the voltage signal collected by the analog-to-digital converter as input and outputs the classification result of the touched object.

The feature pre-extraction algorithm comprises two alternatives, and can be selected from one of the first alternative and the second alternative in the specific implementation process. The first scheme and the second scheme both take x (t) as input and output macroscopic characteristics and microscopic characteristics of signals.

Computing wavelet basis phi of macroscopic feature space _j,n (t)：

φ _j (t)＝2 ^j/2 φ ₀ (2 ^j t)，

φ _j,n (t)＝φ _j (t-2 ^-j n)＝2 ^j/2 φ ₀ (2 ^j t-n)。

Computing wavelet basis psi of microscopic feature space _j,n (t)：

ψ _j (t)＝2 ^j/2 ψ ₀ (2 ^j t)，

ψ _j,n (t)＝ψ _j (t-2 ^-j n)＝2 ^j/2 ψ ₀ (2 ^j t-n)。

Defining an inner product operation between the signal and the wavelet basis:

wherein x is _ma (t) is a macroscopic feature, i.e. x (t) in space V _j Projection of (2), x _mi (t) is a microscopic feature, namely x (t) in space W _j Is projected.

Scheme two is as follows: the embodiment of the application can construct two filters, namely a low-pass filter F _L (t) and a high-pass filter F _H (t) of (d). Wherein, F _L (t) and F _H (t) respective amplitude-frequency characteristics G _L (j ω) and G _H (j ω) have the same cut-off frequency. Wherein G is _L (j ω) and G _H (j ω) is calculated as follows:

Further, the signal processing unit may be configured to obtain a surface texture signal and a material type signal or a surface texture signal and a pressure sensing signal of the friction surface according to the voltage signal, and extract texture features and material features or texture features and pressure sensing features to identify actual surface texture and actual material type or actual surface texture and actual contact pressure based on the texture features and the material features or the texture features and the pressure sensing features.

A first circuit: and (4) outputting a classification algorithm of the identification result of the material type by taking the macroscopic characteristics as input. An alternative algorithm for routing includes, but is not limited to, any of the following:

A second circuit: and outputting a classification algorithm of the identification result of the texture type by taking the microscopic features as input. The second line alternative algorithm includes, but is not limited to, any one of the following:

In an actual implementation process of the communication module 300, the communication module 300 may be integrated on a control chip (for example, a single chip microcomputer of a built-in wireless communication module such as Nordic series and ESP32 series), or may be independent of the control chip (for example, a single chip microcomputer of an STM32 series without a built-in communication module). Its communication principle may be based on, but not limited to: ordinary bluetooth, low power bluetooth, wireless networks, etc.

The rectifier circuit can migrate the alternating current signal and zoom to a preset interval of the control unit, wherein the preset interval is a tolerance range of the control unit, and specific numerical values of the tolerance range can be set by a person skilled in the art according to actual conditions by adjusting corresponding component parameters in the circuit.

Specifically, the operation principle of the friction nanogenerator-based haptic perception system 10 of the embodiment of the application is shown in fig. 5:

the tactile sensor 100 of the tribo nanogenerator generates a tiny, alternating current signal when it comes into contact with different objects; an alternating current signal is transmitted to the rectifying circuit through the two electrodes of the tactile sensor 100; after being processed by the rectifying circuit, the current signal is converted into a voltage signal in an allowable interval of the analog-to-digital converter.

The signal processing module 200 is deployed on a single chip microcomputer, the single chip microcomputer firstly performs low-pass filtering on the acquired voltage signals, then macro features and micro features of the signals are respectively extracted through wavelet decomposition, the macro features and the micro features are respectively processed through two independent neural networks, results output by the two neural networks are integrated, and the results are used as final recognition results.

In the implementation process of the signal processing module 200, the signal conditioning unit may select a transimpedance amplifier and a biased rectifier circuit, the matching resistor on the transimpedance amplifier selects a 500 mega ohm chip resistor, the current signal output by the friction nano-generator may be amplified to a value between-1.5V and 1.5V, and the biased rectifier circuit adds 1.5V additive bias to the voltage signal output by the transimpedance amplifier, so that the final output voltage signal is between 0V and 3V.

The control unit selects an ESP32 single chip microcomputer, the output signal of the signal conditioning unit is connected to the analog input port of the ESP32 single chip microcomputer, a 12-bit analog-to-digital converter integrated in the control unit converts a 0-3V voltage signal into an integer digital signal, and then the control unit maps the integer digital signal into a single-precision floating-point digital signal between 0 and 1.

The signal processing algorithm deployed in the control unit low-pass filters the current signal, the filter used may be a butterworth filter, with a cut-off frequency of 50 Hz.

Intercepting 500 newly generated data points in a signal updated in real time by a data segmentation algorithm deployed on a control unit, wherein the interval of each sampling point is 4ms, the intercepted signal is recorded as x (t), calculating a macroscopic feature x by a mode shown by the following formula by using the first scheme of the feature pre-extraction algorithm _ma (t) calculating the microscopic features x _mi The formula for (t) is as follows:

in the embodiment of the application, the classification algorithm is based on a one-dimensional convolutional neural network and is provided with two independent data processing lines.

The first circuit is composed of a first winding laminated layer, a second winding laminated layer, a third winding base layer, a first full-connection layer and a second full-connection layer in sequence. The convolution kernel size of the first convolution layer is 5, the input dimension is 1, and the output dimension is 8; the convolution kernel size of the second convolution layer is 5, the input dimension is 8, and the output dimension is 16; the convolution kernel size of the first convolution layer is 5, the input dimension is 16, and the output dimension is 32; the first linear layer has an input dimension of 32, an output dimension of 16, the second linear layer has an input dimension of 16, and an output dimension of 4. And the index value of the position of the maximum element in the 4-dimensional vector of the line I is the final output of the line I, and each index value corresponds to one material type.

The second circuit is composed of a first winding lamination layer, a second winding lamination layer, a third winding base layer, a first full connection layer and a second full connection layer in sequence. The convolution kernel size of the first convolution layer is 5, the input dimension is 1, and the output dimension is 8; the second convolution layer contains convolution kernels with the size of 5, the input dimension of 8 and the output dimension of 16; the convolution kernel size of the first convolution layer is 5, the input dimension is 16, and the output dimension is 32; the first linear layer has an input dimension of 32, an output dimension of 16, the second linear layer has an input dimension of 16, and an output dimension of 4. And finally outputting the index value of the position of the maximum element in the 4-dimensional vector by the second line, namely the final output of the first line, wherein each index value corresponds to one texture type. And taking the index value output by the first line and the index value output by the second line as the final output of the data processing module.

The communication module 300 can adopt a Bluetooth wireless transmission mode, the communication module 300 is arranged in an ESP32 single chip microcomputer, an index value output by the data processing module is encoded into two-byte data by UTF-8, and the two-byte data are transmitted to a remote device by Bluetooth. The remote equipment decodes and contrasts the received data to obtain an index value output by the data processing module, and the identification result of the touch sensing system of the friction nano generator can be obtained by retrieving the material and texture types corresponding to the index value.

The manufacturing method of the nano-generator-based touch sensor 100 is as follows:

the flexible substrate size of the tactile sensor 100 of the triboelectric nanogenerator may be selected from polyimide films of 20mm x 30mm x 0.08 mm.

The electrode part is manufactured by using a flexible circuit printing technology, interdigital copper electrodes are printed on the front surface of the flexible substrate, the interdigital electrodes are divided into two groups which are not communicated with each other, each group comprises 3 rectangular interdigital electrodes which are communicated with each other on the back surface of the flexible substrate through via holes, the finger length is 14mm, and the finger width is 2 mm.

In addition, the distance between two groups of electrode fingers on the front surface of the flexible substrate is 2mm, and two bonding pads are arranged on the back surface of the flexible substrate and are respectively communicated with the two groups of electrodes.

The two groups of bonding pads are respectively connected with the two shielding wires through a soldering process and are used for conducting electric signals and reducing external electromagnetic interference.

The friction layer can be selected from polyimide film with the thickness of 20mm x 30mm x 0.08mm, and is flatly and tightly adhered to the front surface of the flexible substrate.

After the sensor is manufactured, the flexibility of the sensor is checked, the sensor is adhered to the surface of a manipulator finger with large curvature, and the like, and the sensor is tightly adhered to the surface of the manipulator finger without cracking.

According to the touch sensing system based on the friction nano generator provided by the embodiment of the application, the actual surface texture, the actual contact pressure and/or the actual material type of the friction surface can be processed and identified simultaneously based on the electric signal generated when the touch sensor in the friction nano generator rubs with the friction surface, and the electronic skin based on the friction nano generator has the characteristics of super-stretchability, high sensitivity, wide sensing range and the like, so that the output of the friction nano generator has sensitive output change for different friction materials, the high-precision real-time identification of the surface texture, the contact pressure and/or the material type can be realized simultaneously by only using a single touch sensor, and the touch sensing system is simple in structure, easy to manufacture and low in cost. Therefore, the technical problems that in the related art, the surface texture and the material type cannot be identified simultaneously or the surface texture type and the contact pressure cannot be identified simultaneously through a single touch sensor, and then the touch sensor is difficult to deploy on a robot with low power consumption and light volume and has high data delay in data transmission and communication are solved.

Next, a method for tactile perception based on a friction nanogenerator according to an embodiment of the present application will be described with reference to the accompanying drawings.

Fig. 6 is a schematic flowchart of a method for tactile sensing based on a friction nanogenerator according to an embodiment of the present application.

As shown in fig. 6, the method for tactile perception based on the friction nanogenerator adopts the above tactile perception system based on the friction nanogenerator, wherein the method comprises the following steps:

in step S601, an electric signal generated by the tactile sensor when rubbing against the friction surface is acquired.

In step S602, the electrical signal is processed to obtain an electrical signal satisfying a preset condition after the processing, and a surface texture signal, a pressure signal and/or a material type signal of the friction surface is generated to identify an actual surface texture, an actual contact pressure and/or an actual material type of the friction surface.

In step S603, the actual surface texture, the actual contact pressure and/or the actual material type are transmitted to a predetermined terminal.

Optionally, in an embodiment of the present application, processing the electrical signal to obtain an electrical signal that satisfies a preset condition after being processed, generating a surface texture signal, a pressure signal, and/or a material type signal of the friction surface, and identifying an actual surface texture, an actual contact pressure, and/or an actual material type of the friction surface includes: carrying out signal amplification processing on the electric signal; converting the processed electrical signal into a voltage signal; and acquiring a surface texture signal, a pressure signal and/or a material type signal of the friction surface according to the voltage signal, and extracting texture features, pressure features and/or material features to identify actual surface texture, actual contact pressure and/or actual material type based on the texture features, the pressure features and/or the material features.

Optionally, in an embodiment of the present application, acquiring a surface texture signal, a pressure signal, and/or a material type signal of the friction surface according to the voltage signal, and extracting texture features, pressure features, and/or material features to identify an actual surface texture, an actual contact pressure, and/or an actual material type based on the texture features, the pressure features, and/or the material features includes: performing band-pass filtering processing on the voltage signal to obtain a signal with noise removed; based on the signal, performing sliding sampling on the time sequence by using a sliding window with a preset width, and segmenting to obtain a feature pre-extraction signal; and (3) acquiring macro features and micro features of the signals according to surface texture signals, pressure signals and/or material type signals in the feature pre-extraction signals, determining actual material types or actual contact pressure according to the macro features, and determining actual surface textures according to the micro features.

Optionally, in an embodiment of the present application, the obtaining the macro features and the micro features of the signal from the surface texture signal, the pressure signal, and/or the material type signal in the feature pre-extraction signal includes: constructing a macroscopic feature space and a microscopic feature space based on the feature pre-extraction signal, and respectively calculating wavelet bases of the macroscopic feature space and the microscopic feature space to obtain macroscopic features and microscopic features; or constructing a low-pass filter and a high-pass filter based on the feature pre-extraction signal, and respectively calculating the amplitude-frequency characteristics of the low-pass filter and the high-pass filter to obtain the macroscopic features and the microscopic features.

It should be noted that the foregoing explanation of the embodiment of the touch sensing system based on the friction nano-generator also applies to the touch sensing method based on the friction nano-generator of this embodiment, and details are not repeated here.

According to the touch sensing method based on the friction nano generator, the actual surface texture, the actual contact pressure and/or the actual material type of the friction surface can be processed and identified simultaneously based on the electric signal generated when the touch sensor in the friction nano generator rubs with the friction surface, and the electronic skin based on the friction nano generator has the characteristics of super-stretchability, high sensitivity, wide sensing range and the like, so that the output of the friction nano generator has sensitive output change for different friction materials, the high-precision real-time identification of the surface texture, the contact pressure and/or the material type can be realized simultaneously by only using a single touch sensor, and the touch sensing method based on the friction nano generator is simple in structure, easy to manufacture and low in cost. Therefore, the technical problems that the surface texture and the material type cannot be identified simultaneously or the surface texture type and the contact pressure cannot be identified simultaneously through a single touch sensor in the related technology, and then the touch sensor cannot be deployed on a robot with low power consumption and light volume, and higher data delay exists in data transmission and communication are solved.

Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:

memory 701, processor 702, and a computer program stored on memory 701 and executable on processor 702.

The processor 702, when executing the program, implements the friction nanogenerator-based haptic sensation method provided in the embodiments described above.

Further, the electronic device further includes:

a communication interface 703 for communication between the memory 701 and the processor 702.

A memory 701 for storing computer programs operable on the processor 702.

The memory 701 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.

Alternatively, in specific implementation, if the memory 701, the processor 702, and the communication interface 703 are integrated on one chip, the memory 701, the processor 702, and the communication interface 703 may complete mutual communication through an internal interface.

The processor 702 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.

The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the friction nanogenerator-based haptic sensation method as described above.

In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless explicitly defined otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims

1. A friction nanogenerator-based haptic sensation system, comprising:

a tactile sensor based on a friction nanogenerator for generating an electrical signal when the tactile sensor is rubbed with a friction surface;

the signal processing module is used for processing the electric signals to obtain the electric signals which meet preset conditions after processing, generating surface texture signals, pressure signals and/or material type signals of the friction surface, and identifying actual surface textures, actual contact pressure and/or actual material types of the friction surface; and

and the communication module is used for sending the actual surface texture, the actual contact pressure and/or the actual material type to a preset terminal.

2. The system of claim 1, wherein the tribo nanogenerator-based tactile sensor body comprises a flexible substrate, first and second electrodes attached to upper and lower surfaces of the flexible substrate, and a flexible friction layer affixed to the flexible substrate and the first and second electrodes.

3. The system of claim 2, wherein the first electrode and the second electrode each comprise a plurality of sub-electrodes, wherein each sub-electrode layer is less thick than the flexible substrate and is disposed in close proximity to the flexible substrate.

4. The system of claim 1, wherein the signal processing module comprises:

the signal conditioning unit is provided with a multi-channel trans-impedance amplifier in an integrated manner, and each trans-impedance amplifier is provided with a current amplifier in an integrated manner so as to amplify the electric signal;

a control unit provided with an analog-to-digital converter to convert the processed electrical signal into a voltage signal;

and the signal processing unit is used for acquiring a surface texture signal, a pressure signal and/or a material type signal of the friction surface according to the voltage signal, and extracting texture features, pressure features and/or material features so as to identify the actual surface texture, the actual contact pressure and/or the actual material type based on the texture features, the pressure features and/or the material features.

5. The system of claim 6, further comprising:

and the rectifying circuit is arranged between the signal conditioning unit and the control unit so as to scale the voltage of the electric signal to a preset interval.

6. The system of claim 5, wherein the signal processing unit is further configured to:

performing band-pass filtering processing on the voltage signal to obtain a signal with noise removed;

based on the signal, performing sliding sampling on the time sequence by using a sliding window with a preset width, and segmenting to obtain a feature pre-extraction signal;

and acquiring macro features and micro features of the signals according to the surface texture signals, the pressure signals and/or the material type signals in the feature pre-extraction signals, determining the actual material type or the actual contact pressure according to the macro features, and determining the actual surface texture according to the micro features.

7. The system of claim 8, wherein the obtaining of the macro features and the micro features of the signal from the surface texture signal, the pressure signal, and/or the material type signal in the feature pre-extraction signal comprises:

constructing a macroscopic feature space and a microscopic feature space based on the feature pre-extraction signal, and respectively calculating wavelet bases of the macroscopic feature space and the microscopic feature space to obtain the macroscopic feature and the microscopic feature;

or constructing a low-pass filter and a high-pass filter based on the feature pre-extraction signal, and respectively calculating the amplitude-frequency characteristics of the low-pass filter and the high-pass filter to obtain the macroscopic features and the microscopic features.

8. A method for tactile perception based on a triboelectric nanogenerator, wherein the system for tactile perception based on a triboelectric nanogenerator according to any one of claims 1 to 7 is used, wherein the method comprises the following steps:

collecting an electric signal generated when the touch sensor rubs with a friction surface;

processing the electric signal to obtain the electric signal which meets preset conditions after processing, generating a surface texture signal, a pressure signal and/or a material type signal of the friction surface, and identifying the actual surface texture, the actual contact pressure and/or the actual material type of the friction surface; and

and sending the actual surface texture, the actual contact pressure and/or the actual material type to a preset terminal.

9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the friction nanogenerator-based haptic sensation method of claim 8.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for implementing the friction nanogenerator-based haptic perception method according to claim 8.