Biomimetic hair touch sensor and applications

Abstract

The application relates to the technical field of sensors, in particular to a bionic hair touch sensor and application, which comprises a substrate and a bionic cilium array arranged on the substrate; the bionic cilium array comprises a plurality of array-arranged flexible bionic cilia, the bottom of the flexible bionic cilium is provided with a marking layer; an optical system is integrated in the substrate, the optical system comprises an imaging module and a micro prism, the micro prism reflects the deformation of the marking layer at the bottom of the flexible bionic cilium to the imaging module; the imaging module is used for capturing deformation data of the marking layer and sending the deformation data to a deep learning algorithm module, so that three-dimensional force decoupling and resolution are realized. The application realizes high-resolution multi-dimensional force detection and direction detection through the change of marking points caused by cilium deformation, and expands to sliding sensing and multi-modal object recognition functions by using array design.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sensors, in particular to a bionic hair tactile sensor and application. BACKGROUND

[0002] As a key component for robots to perceive external mechanical information, tactile sensors have experienced an evolution process from single mechanical parameter detection to multi-modal perception. In recent years, with the breakthroughs in flexible electronics, bionics and optical technology, tactile sensors face new technical demands in terms of sensitivity, multi-dimensional perception ability and environmental adaptability. Firstly, the application scenarios require refinement. Intelligent robots (such as surgical robots and collaborative robotic arms) have an increasing demand for the perception of micro-force operations (<1 mN) and complex contact states (such as sliding and rolling). For example, in minimally invasive surgery, it is necessary to detect contact forces of 0.5-5 mN on blood vessels, and traditional sensors cannot balance sensitivity and range. Secondly, there is a contradiction between real-time dynamic perception. High spatial and temporal resolution (>100 Hz sampling rate) is required for sliding detection, but high-density array sensors (such as capacitive arrays) are difficult to achieve real-time response due to signal crosstalk and data processing delays. Thirdly, there is a lack of robustness in complex environments. In industrial and medical scenarios, there are complex environments such as strong light, oil stains and electromagnetic interference, and traditional optical sensors (such as RGB cameras) are prone to failure, while magnetic sensors are significantly affected by metal environments.

[0003] Existing tactile sensors based on optical marker point deformation print high-contrast marker points on the surface of an elastic body, use a camera to capture the displacement changes of the marker points under external force, and then calculate the size and direction of the contact force. However, the existing optical marker point deformation technology has the problems of insufficient micro-force detection sensitivity, limited direction recognition dimension, and poor real-time dynamic perception. The existing optical marker point deformation technology (such as GelSight) has a micro-force detection lower limit of about 10 mN, which cannot meet the needs of high-precision scenarios such as surgical robots (which require 0.5-5 mN level detection). Optical fiber type cilium sensors can reach 1 mN, but are limited by light intensity detection noise, making it difficult to further improve resolution. Moreover, existing technologies rely on two-dimensional deformation analysis (such as marker point plane displacement or single-axis magnetic field change), which cannot decouple three-dimensional force components. For example, optical fiber sensors can only detect normal forces, while the visual-cilium fusion system (OmniTact) supports three-dimensional decoupling, but the direction error exceeds 10°, which cannot meet the needs of precision operations. For dynamic perception, optical fiber sensors have a bandwidth of less than 50 Hz, and visual solutions are affected by image processing delays (requiring GPU acceleration), resulting in a sliding detection response time of more than 50 ms, making it difficult to achieve real-time closed-loop control. SUMMARY

[0004] The embodiment of the present application provides a bionic hair tactile sensor and application, and aims to solve the deficiencies of the existing tactile sensor in micro-force detection sensitivity, multi-dimensional force direction identification, large-area dynamic sliding perception, object feature identification, and adaptability to complex environments.

[0005] To solve the above technical problems, in a first aspect, the embodiment of the present application provides a bionic hair tactile sensor, which comprises a substrate and a bionic cilium array arranged on the substrate; the bionic cilium array comprises a plurality of array-arranged flexible bionic cilia, and the bottom of each flexible bionic cilium is provided with a marking layer; an optical system is integrated in the substrate, the optical system comprises an imaging module and a micro-prism, and the micro-prism reflects the deformation of the marking layer at the bottom of the flexible bionic cilium to the imaging module; the imaging module is used for capturing deformation data of the marking layer and sending the deformation data to a deep learning algorithm module, so as to realize three-dimensional force decoupling and resolution.

[0006] In some example embodiments, the material of the flexible bionic cilium is PA6; the diameter of each flexible bionic cilium is 400 microns, and the height is 8 millimeters.

[0007] In some example embodiments, the density of the flexible bionic cilium array is 10-25 roots / cm2.

[0008] In some example embodiments, the bionic cilium array is formed by a nylon particle hot pressing process, and the surface is activated by oxygen plasma to improve the adhesion of the marking layer; the hot pressing process conditions are as follows: die pressing at 280 DEG C, pressure maintaining time of 90 seconds, and cooling gradient of 3 DEG C / s.

[0009] In some example embodiments, the marking layer is formed by immersing a marking material in a micro-groove coating device and curing the marking material by a nano-UV LED array to form a stable coating.

[0010] In some example embodiments, the marking material is one of liquid silicone, a polymer film or a metal film.

[0011] In some example embodiments, the Shore hardness of the marking layer is 20A, and the thickness is 200 microns.

[0012] In some example embodiments, the bottom of each flexible bionic cilium is provided with a marking layer, and the thickness of the marking layer is 2 millimeters.

[0013] In some example embodiments, the imaging module is one of an embedded camera, a flexible endoscope, a CMOS sensor or a micro-camera.

[0014] In a second aspect, the embodiment of the present application further provides an application of the bionic hair tactile sensor as described in the above embodiment in the field of sensing micro-force operation and complex contact state of an intelligent robot.

[0015] The technical scheme provided by the embodiment of the application has at least the following advantages:

[0016] The embodiment of the application provides a bionic hair tactile sensor and application, the sensor comprising: a substrate and a bionic cilium array arranged on the substrate; the bionic cilium array comprises a plurality of array-arranged flexible bionic cilia, and the bottom of the flexible bionic cilium is provided with a marking layer; an optical system is integrated in the substrate, the optical system comprising an imaging module and a micro-prism, the micro-prism reflects deformation of the marking layer at the bottom of the flexible bionic cilium to the imaging module; the imaging module is used for capturing deformation data of the marking layer and sending the deformation data to a deep learning algorithm module, so that three-dimensional force decoupling and resolution are realized. The application realizes high-resolution multi-dimensional force detection and direction detection through changes of marking points caused by deformation of the cilia, and expands to sliding perception and multi-modal object recognition functions by using array design. BRIEF DESCRIPTION OF DRAWINGS

[0017] One or more embodiments are illustrated by way of example in the drawings that are not intended to be limiting of the embodiments, unless otherwise specifically indicated, the drawings in which:

[0018] Figure 1 A structural schematic diagram of a bionic hair tactile sensor provided by an embodiment of the application.

[0019] Figure 2 A surface topography diagram of a bionic hair tactile sensor provided by an embodiment of the application.

[0020] Figure 3 A hair follicle topography diagram of a back surface of a surface layer of a bionic hair tactile sensor provided by an embodiment of the application.

[0021] Figure 4 A surface topography diagram of a bionic hair tactile sensor provided by an embodiment of the application.

[0022] Figure 5 A schematic diagram of the elastic modulus of bionic hair provided by an embodiment of the application. DETAILED DESCRIPTION

[0023] As known from the background, the existing tactile sensor has obvious deficiencies in micro-force detection sensitivity, multi-dimensional force direction recognition, large-area dynamic sliding perception, object feature recognition, and complex environment adaptability.

[0024] Existing optical marker point deformation-based tactile sensors, which print high-contrast markers on the surface of an elastomer, capture the displacement of the markers under external force using a camera, and then calculate the size and direction of the contact force. For example, the GelSight sensor developed by MIT (2015) uses an elastomer coating combined with reflective particles to reconstruct the spatial distribution of contact force through image processing algorithms. Its advantages include sub-millimeter spatial resolution and the ability to detect surface texture characteristics. However, this technology has significant drawbacks: low sensitivity to micro-force detection (lower limit of about 10 mN), which cannot meet the sensing needs of sub-millinewton forces in surgical robots and other scenarios; limited directional recognition ability to two-dimensional tangential force estimation, which cannot decouple three-dimensional force components; in addition, the sensor relies on visible light illumination, and its performance significantly decreases in strong light or dark environments, making it less adaptable to the environment.

[0025] For optical fiber-based bionic cilium tactile sensors, such sensors combine flexible cilium structures with optical fiber sensing technology to achieve mechanical detection through changes in optical fiber transmission loss or light intensity caused by cilium bending. A typical representative is the magnetic-optical composite cilium sensor proposed by Alfadh el et al. (2016), which uses magnetic field changes to modulate the optical fiber output signal. The advantages of this scheme include strong anti-electromagnetic interference capability and the ability to detect micro-forces of about 1 mN. However, its limitations are also obvious: single-dimensional signal, only capable of detecting normal force through light intensity changes, unable to distinguish tangential force direction; complex optical multiplexing circuit is required for cilium array expansion, resulting in high system cost; in addition, the light intensity detection bandwidth is usually lower than 50 Hz, making it difficult to meet the real-time requirements of dynamic scenarios such as sliding detection.

[0026] In the field of visual-cilium fusion tactile system technology, this technology sets reflective markers at the top of the cilium and uses a high-speed camera to track the cilium bending trajectory to achieve multi-dimensional force detection. The OmniTact system at the University of California, San Diego (2020) is a typical representative, which uses a hemispherical cilium array combined with a fisheye lens to support three-dimensional force decoupling and large-area tactile mapping. The advantages of this scheme include three-dimensional force detection capability and array scalability, but there are still key technical bottlenecks: limited by camera pixel density, micro-force detection sensitivity only reaches the 0.5 mN level; the system is sensitive to environmental lighting conditions and requires strict light-shielded environments to work stably; in addition, high-precision algorithms rely on GPU acceleration processing, making it difficult to implement low-power deployment in embedded devices, limiting the practical application of industrial scenarios.

[0027] To solve the technical bottlenecks of the prior art, such as insufficient micro-force detection sensitivity, limited direction recognition dimension, and poor real-time dynamic perception, the embodiment of the present application provides a bionic hair tactile sensor and application, which comprises a substrate and a bionic cilium array arranged on the substrate; the bionic cilium array comprises a plurality of array-arranged flexible bionic cilia, and the bottom of the flexible bionic cilium is provided with a marker layer; an optical system is integrated in the substrate, and the optical system comprises an imaging module and a micro-prism, the micro-prism reflects the deformation of the marker layer at the bottom of the flexible bionic cilium to the imaging module; the imaging module is used for capturing deformation data of the marker layer and sending the deformation data to a deep learning algorithm module to realize three-dimensional force decoupling and resolution. The present application realizes high-resolution multi-dimensional force detection and direction detection through the change of the marker points caused by the deformation of the cilia, and expands to sliding perception and multi-modal object recognition functions by using array design.

[0028] The embodiment of the present application provides a bionic hair tactile sensor, which is optimized by a sub-pixel level optical flow tracking algorithm and a high frame rate CMOS sensor (≥1000 fps), realizes 0.1 mN level resolution, meets the micro-force perception needs of scenes such as surgical robots, and improves the micro-force detection sensitivity; at the same time, the present application designs a multi-view imaging system (double camera layout) and a defocus depth detection algorithm, combines three-dimensional displacement tracking of the marker points at the top of the cilia, achieves three-dimensional force direction recognition accuracy error <5°, breaks through the limitation of the existing two-dimensional direction detection, and realizes accurate decoupling of three-dimensional force direction. In addition, the present application adopts a lightweight dynamic time warping (DTW) algorithm and a hardware parallel acceleration architecture, compresses the sliding detection response time to <10 ms, supports real-time tactile feedback control of robots, and enhances the real-time performance of dynamic response.

[0029] The embodiments of the present application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art can understand that in the embodiments of the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, the technical solutions claimed by the present application can be implemented even without these technical details and various changes and modifications based on the following embodiments.

[0030] As shown in Figure 1 The embodiment of the present application provides a bionic hair tactile sensor, which comprises a substrate 1 and a bionic cilium array arranged on the substrate 1; the bionic cilium array comprises a plurality of array-arranged flexible bionic cilia 2, and the bottom of the flexible bionic cilium 2 is provided with a marker layer; an optical system is integrated in the substrate 1, and the optical system comprises an imaging module 3 and a micro-prism, the micro-prism reflects the deformation of the marker layer at the bottom of the flexible bionic cilium 2 to the imaging module 3; the imaging module 3 is used for capturing deformation data of the marker layer and sending the deformation data to a deep learning algorithm module to realize three-dimensional force decoupling and resolution.

[0031] The sensor provided in this application adopts a design combining a biomimetic cilia array with an optical system. Based on the biological skin and hair sensing mechanism, this application uses nylon 6 (400 micrometers in diameter, 8 millimeters in height) as the biomimetic cilia material to construct a high-density cilia array (5×5 arrangement). The spacing between the biomimetic cilia is 3 millimeters, effectively avoiding entanglement. Mechanical consistency is ensured through a thermoforming process, and a marking material (black liquid silicone) is coated on the bottom 2 millimeters of the biomimetic cilia as a marking layer (optical marker). This marking layer has a Shore hardness of 20A and a thickness of 200 micrometers. A high-contrast (reflectivity difference >85%) trackable feature is achieved through an ultraviolet curing process. The biomimetic cilia array is arranged in a 5×5 angled layout (15° tilt), meaning the cilia are arranged at a 15° tilt angle, effectively reducing physical interference between adjacent cilia and improving the ability to detect independent deformation in dense environments.

[0032] Figure 2 The surface morphology of the biomimetic hair tactile sensor is shown, from Figure 2 As can be seen, bionic hair is inserted into the surface of electronic skin, forming the surface morphology of the bionic hair tactile sensor; Figure 3 The image shows the morphology of hair follicles on the back surface of the biomimetic hair tactile sensor. Figure 3 It shows the hair follicle structure at the base of the hair. When the hair surface is stimulated by external factors, the internal hair follicle structure will shift. The bottom camera captures this change and inputs it into a deep learning network to analyze the external stimuli. Figure 4 The diagram illustrates how tactile information is derived by recognizing the displacement of hair follicles based on the principle of visual-tactile perception. The hair follicle structure is characterized by computer vision, making its features more obvious.

[0033] Breaking through the limitations of traditional CMOS sensor layouts, the optical system of this application can employ a miniature endoscope optical system. The sensing layer uses a medical-grade flexible endoscope (2.9 mm in diameter, 120° field of view) as the core imaging unit, thus replacing the traditional CMOS sensor. This is achieved through a dual-channel fiber optic bundle to separate illumination and imaging paths. Specifically, the illumination channel integrates a high-brightness LED (5500K color temperature, 50-2000 lux dynamic range illuminance). For the imaging channel, a miniature prism group (45°±2° reflection angle) maps the three-dimensional deformation of the biomimetic cilia bottom marker layer to the imaging module, which can employ a 1280×720 resolution image sensor. The support structure uses a 3D-printed titanium alloy hollow bracket (70% porosity) to provide mechanical support, allowing the sensor to maintain optical alignment accuracy (deviation <5 μm) even with a bending radius ≥8 mm, balancing lightweight design and mechanical strength.

[0034] It should be noted that the optical sensor can be replaced by a camera: in the present application, an embedded camera is used to capture the deformation data of the surface marker layer. As an alternative, other types of optical sensors (e.g. CMOS sensors or miniature imaging modules) can be used to achieve the same image capture function. These sensors can be replaced in terms of different sizes, performance and cost, without affecting the overall function and accuracy of the system.

[0035] The deformation detection system is based on monocular vision three-dimensional reconstruction technology. Through the improved EPnP algorithm combined with nonlinear optimization, the two-dimensional image captured by the endoscope is converted into three-dimensional displacement data of the cilia. The feature tracking uses ORB descriptor (500 feature points are extracted per frame), with a matching accuracy of ±1.5 pixels, achieving an axial displacement resolution of ±8 microns (corresponding to 0.2 millinewton force detection). The dynamic anti-interference module integrates a six-axis IMU sensor (BMI160, sampling rate 1000 Hz), which uses gyroscope and acceleration data to compensate for image blur caused by mechanical vibration in real time. The light adaptive system uses a PID control algorithm to adjust the brightness of the LED (50-2000 lux), which still maintains an illumination fluctuation of less than 5% in the strong interference environment of the surgical shadowless lamp. Temperature drift suppression is achieved through closed-loop compensation by a PT1000 high-precision sensor, ensuring that the temperature drift rate is less than 0.01% / °C within the range of -20°C to 60°C.

[0036] In some embodiments, the material of the flexible biomimetic cilium 2 is PA6; the diameter of each flexible biomimetic cilium 2 is 400 microns, and the height is 8 millimeters. PA6 is polyamide 6, i.e. nylon 6.

[0037] In some embodiments, the density of the flexible biomimetic cilium array is 10-25 roots / cm².

[0038] In some embodiments, the biomimetic cilium array is formed by a nylon particle hot pressing process, and the surface is activated by oxygen plasma to improve the adhesion of the marker layer; the hot pressing process conditions are: 280°C die pressing, pressure holding time 90 seconds, and cooling gradient 3°C / s.

[0039] In some embodiments, the marker layer is immersed in a marker material by a micro-groove coating device, and a stable coating is formed after curing by a nano-UV LED array.

[0040] Specifically, the preparation process of the sensor provided in the application is as follows: the flexible biomimetic cilium is formed by a nylon particle hot pressing process (molding at 280 DEG C, pressure maintaining for 90 seconds, and cooling gradient of 3 DEG C per second), and the surface is activated by oxygen plasma (80-watt power processing for 90 seconds) to improve the adhesion of the silica gel marking layer (marking points). The marking layer is made by immersing black liquid silica gel at a speed of 0.5 mm / s through a micro-groove coating device, and a stable coating is formed by curing for 30 seconds through a 395-nanometer ultraviolet LED array (intensity of 80 milliwatts per square centimeter). The laser-assisted centering technology is used in the optical system integration stage, and the double-channel optical fiber bundle is used to realize the separation of illumination and imaging, so as to ensure that the coaxiality error of the endoscope and the optical fiber bundle is less than or equal to 5 microns, and the V-shaped groove precise butt joint makes the optical fiber insertion loss less than 0.5 decibels. The final assembly realizes IP68 level waterproof performance through epoxy resin packaging, and can resist 1-meter water depth for 1-hour continuous immersion.

[0041] It should be noted that different types of marking layers can be used. The application uses black silica gel marks and uses ultraviolet lithography technology to make patterns. Alternative solutions can use different types of marking materials (such as transparent film, polymer or metal film) and different pattern making technologies (such as laser etching, hot forming, etc.). These changes can adapt to different application requirements, such as use in extreme environments or smaller devices.

[0042] In some embodiments, the marking material is one of liquid silica gel, polymer film or metal film.

[0043] In some embodiments, the Shore hardness of the marking layer is 20A, and the thickness is 200 microns.

[0044] In some embodiments, the bottom of each flexible biomimetic cilium is provided with a marking layer, and the thickness of the marking layer is 2 millimeters.

[0045] In some embodiments, the imaging module is one of an embedded camera, a flexible endoscope, a CMOS sensor or a miniature camera.

[0046] In addition, the application also provides an application of the biomimetic hair tactile sensor in the field of sensing micro-force operation and complex contact state of intelligent robots.

[0047] The biomimetic hair tactile sensor provided in the application adopts a rigid substrate (E≥1 GPa), a flexible top (E≤100 kPa) and a fluorescent / high-reflective marking layer, and integrates an optical system of a flexible endoscope and a polarization filter, with a diameter of ≤3 mm and a field coverage rate of ≥90%, so as to achieve a three-dimensional force direction recognition accuracy error of <5°, thereby breaking through the existing two-dimensional direction detection limitation.

[0048] Compared with the prior art, the bionic hair tactile sensor provided by the application has the advantages that:

[0049] (1) Higher tactile perception accuracy and complexity.

[0050] The prior art usually relies on traditional resistance or capacitance sensors to perceive tactile signals, which are insufficient in capturing complex tactile actions (such as pinching and sliding), especially in force distribution and direction perception. In contrast, the application can accurately capture the deformation of the surface marking layer and analyze the distribution of three-dimensional force by combining visual tactile perception principles with deep learning algorithms, and can more meticulously identify complex tactile interactions, such as sliding, pinching, and other actions. This visual-based tactile perception technology can greatly improve the recognition accuracy of the system for complex actions.

[0051] (2) Three-dimensional force decoupling capability is superior to the prior art.

[0052] Traditional force sensors (such as resistance and piezoelectric sensors) usually cannot effectively decouple different directions and types of force, resulting in limited tactile perception information, especially for accurate measurement of multi-dimensional force. However, the application can effectively decouple and distinguish three-dimensional force by combining image data captured by a camera with deep learning algorithms, greatly improving the accuracy and detail of force perception. Compared with the prior art, the application not only can identify simple pressure, but also can accurately analyze complex force distribution and sliding, pinching, and other interactive methods, solving the limitations of traditional force sensors.

[0053] (3) Higher environmental adaptability and robustness.

[0054] The prior art is often limited by the light or noise of the sensor itself, especially in environments with large changes in light conditions, the recognition accuracy may be greatly reduced. However, the application uses dynamic brightness adjustment and image enhancement technology to effectively resist the interference of environmental light changes, improving the stability and robustness of the system in complex environments. This is much better than the resistance or capacitance sensors in the prior art, which are usually sensitive to light changes and cannot be adjusted or optimized by software means.

[0055] (4) Multi-task learning capability and data utilization efficiency.

[0056] The force perception and tactile recognition in the prior art often need to design independent systems or models respectively, while the application adopts a deep learning architecture (such as ResNeXt and R(2+1)D convolutional network), which can realize multi-task learning of tactile action recognition and three-dimensional force perception, effectively improving the data utilization efficiency and the adaptability of the model. Through a unified deep learning framework, the application can complete multiple tasks in a single system, avoiding the waste of computing resources and performance bottlenecks caused by task separation in traditional technologies.

[0057] (5) Improvement of cost-effectiveness.

[0058] Traditional flexible electronic skin sensors require multiple individual sensor components (such as pressure sensors, temperature sensors, etc.), which not only increase the complexity of the system, but also bring high cost. However, the application uses a combination of visual perception and deep learning to concentrate the tasks that originally required multiple sensors in an integrated system, reducing hardware costs while simplifying system design, improving system integration and reliability.

[0059] (6) Better user interaction experience.

[0060] Although existing electronic skin technology can provide certain tactile perception capabilities, it often cannot achieve rich emotional interaction or high-quality human-computer interaction. However, the application realizes fine tactile recognition and three-dimensional force decoupling, which not only can recognize simple tactile actions, but also can distinguish different emotional interactions (such as caressing, pinching, etc.), making the emotional performance of robots or interactive devices more natural and flexible, greatly improving the user's interaction experience.

[0061] In order to experimentally verify the bionic hair tactile sensor provided by the application, COMSOL Multiphysics software is used for simulation verification and mechanical response simulation. The simulation experiment conditions are: a single cilium (diameter 500 microns, height 5 millimeters) deflects 1° under tangential force. Experimental results: the error is less than 5% compared with the actual measurement; the sensor array output matching degree is 92%.

[0062] ANSYS finite element analysis is used for mechanical response simulation, and the model is simplified as a flexible beam (elastic modulus 50 kilopascals). The experimental results are: maximum stress 12.3 megapascals (cilium root), actual measured breaking strength ≥ 15 megapascals; theoretical sensitivity 6.63 microtesla / millinewton, actual measurement error 7.7%.

[0063] In order to characterize the elastic modulus of the device, a tensile machine is used for tensile test, and then the elastic modulus is calculated by means of the corresponding physical formula; the elastic modulus of the bionic hair is as shown in Figure 5 .

[0064] According to the technical solutions, the application provides a bionic hair touch sensor and application. The sensor comprises a substrate and a bionic cilium array arranged on the substrate. The bionic cilium array comprises a plurality of array-arranged flexible bionic cilia. The bottom of the flexible bionic cilium is provided with a marking layer. An optical system is integrated in the substrate. The optical system comprises an imaging module and a micro prism. The micro prism reflects the deformation of the marking layer at the bottom of the flexible bionic cilium to the imaging module. The imaging module is used to capture the deformation data of the marking layer and send the deformation data to a deep learning algorithm module to realize three-dimensional force decoupling and resolution. The application realizes high-resolution multi-dimensional force detection and direction detection through the change of the marking points caused by the deformation of the cilia. The array design is used to expand to the functions of sliding perception and multi-modal object recognition.

[0065] Those skilled in the art can understand that the above-mentioned embodiments are specific embodiments for implementing the application, and in actual application, various changes can be made in form and details without departing from the spirit and scope of the application. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the application. Therefore, the protection scope of the application should be limited by the scope defined in the claims.

Claims

1. A biomimetic hair touch sensor, characterized by, The bionic hair touch sensor comprises a substrate and a bionic hair array arranged on the substrate. The substrate is a rigid substrate with an elastic modulus of ≥1 GPa. The bionic hair array comprises a plurality of array-arranged flexible bionic hairs, the bottom of each flexible bionic hair is provided with a marking layer, the bionic hair array is arranged in a 5x5 array, and the flexible bionic hairs are arranged at an inclination angle of 15°. The flexible bionic hairs are grafted on the substrate, and the bottom of the bionic hair array forms a hair follicle structure, when the hair surface is stimulated by external stimulation, the internal hair follicle structure is displaced. An optical system is integrated in the substrate, the optical system comprises an imaging module and a micro-prism, the micro-prism reflects the deformation of the marking layer at the bottom of the flexible bionic hair to the imaging module, the imaging module is used for capturing the deformation data of the marking layer and sending the deformation data to a deep learning algorithm module to realize three-dimensional force decoupling and resolution. The imaging module adopts a flexible endoscope, the optical system further comprises an LED lamp, the LED lamp provides an illumination channel, the imaging module and the micro-prism provide an imaging channel, and the illumination path and the imaging path are separated. The material of the flexible bionic hair is PA6. The diameter of each flexible bionic hair is 400 microns, and the height is 8 millimeters. The marking layer is formed by immersing a marking material in a micro-groove coating device and curing the marking material by a nano-UV LED array to form a stable coating.

2. The bionic hair touch sensor according to claim 1, wherein, The density of the flexible bionic hair array is 10-25 hairs / cm2.

3. The bionic hair touch sensor according to claim 1, wherein, The bionic hair array is formed by a nylon particle hot pressing process, and the surface is activated by oxygen plasma to improve the adhesion of the marking layer; the hot pressing process conditions are: die pressing at 280℃, pressure holding time of 90 seconds, and cooling gradient of 3℃ / second.

4. The bionic hair touch sensor according to claim 1, wherein, The marking material is one of liquid silicone, polymer film or metal film.

5. The bionic hair touch sensor according to claim 1, wherein, The Shore hardness of the marking layer is 20A, and the thickness is 200 microns.

6. The bionic hair touch sensor according to claim 1, wherein, The bottom of each flexible bionic hair is provided with a marking layer, and the thickness of the marking layer is 2 millimeters.

7. Application of the bionic hair touch sensor according to any one of claims 1-6 in the field of intelligent robot sensing of micro-force operation and complex contact state.

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