A method and device for calculating the friction force of skin large deformation, and a storage medium

Abstract

The application discloses a skin large deformation friction model calculation method and device and a storage medium, wherein the method comprises the following steps: performing a pressure experiment on the skin by using a test pressure head to obtain elastic parameters of the skin; collecting skin contact images and skin deformation point cloud images when the test pressure head is in friction with the skin surface; processing the collected skin contact images by using a color clustering algorithm to obtain skin contact information; processing the collected skin deformation point cloud images to obtain skin bulging deformation information; and correcting a binomial friction model according to the elastic parameters, the skin contact information and the skin bulging deformation information. The application can accurately consider large skin three-dimensional deformation caused by a real external force, provides a mechanical model for human-machine contact motion, so that appropriate control strategies can be timely formulated, and thus the effect of human-machine contact operation is improved. The application can be widely applied to the field of biological tribology.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biofriction, and in particular to a method and device for calculating the friction of skin under large deformation, and a storage medium. BACKGROUND

[0002] Skin is a kind of biological soft material with multi-layer nonlinear elastic structure, and its complex mechanical behavior is of great significance in many fields, including intelligent automated massage, wearable sensors, medicine and cosmetics, etc. In particular in the field of robot massage, the massage head of the robot directly contacts with the human skin, applies pressure and moves the skin and the soft tissue underneath to achieve the effect of relieving fatigue and promoting recovery. The viscoelasticity, creep and hysteresis of the skin make the skin and the tissue underneath be compressed when subjected to normal force, and the stretching of the skin in tangential motion causes local skin accumulation, which not only affects the control accuracy of the robot motion, but also can cause pain and discomfort, thereby affecting the massage effect. Therefore, it is crucial to establish an accurate skin friction model for improving the effect of robot massage.

[0003] Current researches usually focus on the skin friction mechanics behavior under low normal force, where the normal force is usually no more than 1N and the indentation depth is generally within 10mm, resulting in small deformation. However, the skin accumulation phenomenon under the combined action of high normal force and friction has not been thoroughly studied. In the past few decades, a large number of studies have been devoted to a deeper understanding and simulation of the biomechanical behavior of skin, although the Hertz contact theory provides a basis for this, but due to its assumption of material uniformity and isotropy, it is only applicable to small deformation and cannot accurately describe the large deformation behavior of skin that may occur in actual applications. Therefore, a more refined model is needed to reflect the contact mechanics behavior of skin in actual situations.

[0004] In the study of skin biomechanics, the related theory of contact mechanics is widely used in the modeling of the mechanical behavior of skin and soft tissue. The most classic Hertz contact theory and its derived binomial friction model provide researchers with tools. For example, M. Kwiatkowska in his study “Friction and deformation behaviour of human skin” used the binomial friction model to calculate the results of reciprocating sliding tests on the skin of the human forearm using a spherical probe, and observed the “bow wave” phenomenon formed by the skin in front of the probe, indicating that even under the condition of friction not exceeding 0.5N and skin deformation not exceeding 3mm, the response of the skin is extremely complex.

[0005] Therefore, in the design and development of more advanced skin contact applications, such as robot massage technology, establishing an accurate mechanical model that is more suitable for large deformation scenarios is the key to improving operational efficiency and user experience. SUMMARY

[0006] To at least partially solve one of the technical problems existing in the prior art, the purpose of the present application is to provide a skin large deformation friction force model calculation method, device and storage medium.

[0007] The first technical solution adopted by the present application is:

[0008] A skin large deformation friction force model calculation method, comprising the following steps:

[0009] Performing a pressure experiment on the skin by a test indenter to obtain the elastic parameters of the skin;

[0010] Collecting skin contact images and skin deformation point cloud images when the test indenter and the skin surface are in friction;

[0011] Processing the collected skin contact images using a color clustering algorithm to obtain real skin contact information;

[0012] Processing the collected skin deformation point cloud images to obtain skin bulging deformation information;

[0013] Correcting the binomial friction model according to the elastic parameters, the skin contact information and the skin bulging deformation information.

[0014] Further, the step of performing a pressure experiment on the skin by a test indenter to obtain the elastic parameters of the skin comprises:

[0015] Starting a robot to move the test indenter to the surface of the experimental object to determine the starting point as the position where the test indenter just contacts the skin and no deformation occurs;

[0016] Controlling the test indenter to perform a normal indentation operation, using a six-axis sensor to record the relationship between the normal force and the indentation depth, and generating a normal force-indentation depth curve ;

[0017] Calculating the curve slope of the unloading stage in the obtained normal force-indentation depth curve ; ;

[0018] According to the curve slope , the corresponding effective elastic modulus is calculated, and the viscoelastic hysteresis loss fraction is calculated.

[0019] Further, the calculation formula of the effective elastic modulus is:

[0020]

[0021] The viscoelastic hysteresis loss fraction The calculation formula is:

[0022]

[0023] In the formula, represents the contact area, represents the hysteresis energy loss, represents the elastic hysteresis energy.

[0024] Further, the test indenter is a cylinder, and the contact area , wherein R is the radius of the test indenter.

[0025] Further, the test indenter is made of a transparent material with smooth surface, and the test indenter is provided with an image acquisition device;

[0026] The skin contact image and the skin deformation point cloud image when the test indenter rubs against the skin surface are collected, and the collection includes:

[0027] Starting the robot to move the test indenter to the surface of the experimental object, and determining the starting point as the position where the test indenter just contacts the skin and no deformation occurs;

[0028] Controlling the test indenter to move downward until the normal force reaches a specified force, and then controlling the test indenter to move tangentially while keeping the normal force constant;

[0029] The six-axis sensor records the three-dimensional force and the TCP coordinates ;

[0030] Collecting the skin contact image between the skin and the test indenter, and the skin deformation point cloud image at the front end of the test indenter.

[0031] Further, the skin contact image collected is processed by a color clustering algorithm to obtain skin contact information, including:

[0032] The skin contact image is processed by creating a mask to obtain information of the test indenter part;

[0033] The image after the mask is subjected to color space conversion and pixel clustering to obtain a plurality of clusters;

[0034] The number of pixels in each cluster is counted, and the color and number are combined together; according to the V value (the depth of color) in the HSV color space, the order is sorted in ascending order, and the order obtained is the mask area, the non-contact area in the indenter, and the contact area in the indenter;

[0035] The contact area of the contact area is calculated as the skin contact information.

[0036] Further, the collected skin deformation point cloud image is processed to obtain skin bulge deformation information, including:

[0037] The skin deformation point cloud image is point cloud cropped, only keeping the part of the point cloud in front of the end of the moving direction of the pressure head as the region of interest;

[0038] Point cloud registration is performed to unify the point cloud coordinate origin of each frame to the tangential movement start time ;

[0039] According to the registered point cloud, the skin bulge deformation information is calculated: define as the height of the front end of the test pressure head in contact with the test pressure head at a certain time, the calculation formula is: ; define as the height difference between the front end of the test pressure head and the end of the test pressure head at a certain time, the calculation formula is: ; wherein is the z coordinate of TCP, is the highest point coordinate of the front end of the test pressure head in direct contact with the test pressure head, is the front end of the test pressure head.

[0040] Further, the binomial friction model is modified according to the elastic parameters, the skin contact information and the skin bulge deformation information, including:

[0041] The binomial friction model considers that the skin friction is composed of adhesion component and deformation component:

[0042]

[0043] In the formula, is the deformation component, is the adhesion component; is the viscoelastic hysteresis loss fraction, is the inherent interface shear strength, is the pressure coefficient, is the effective elastic modulus; is the radius of the test pressure head, is the skin Poisson's ratio, is the normal load;

[0044] The binomial friction model is modified in combination with the obtained skin contact information and skin bulge deformation information:

[0045]

[0046] In the formula, is the height difference between the front end of the test pressure head and the end of the test pressure head, is the height of the front end of the test pressure head in contact with the test pressure head. is a mass coefficient, is a proportionality coefficient; is an indentation contact radius.

[0047] The second technical solution adopted by the present application is:

[0048] A device for calculating the friction force of large skin deformation, comprising:

[0049] At least one processor;

[0050] At least one memory for storing at least one program;

[0051] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0052] The third technical solution adopted by the present application is:

[0053] A computer readable storage medium, wherein a processor executable program is stored, the processor executable program is used to execute the above method when executed by a processor.

[0054] The present application has the beneficial effects that the friction force calculation method proposed by the present application is suitable for different working conditions in human-machine contact motion. The method can accurately consider the large three-dimensional deformation of the skin caused by the real external force, and provides a mechanical model for human-machine contact motion, so as to timely develop appropriate control strategies, thereby improving the effect of human-machine contact work. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following introduces the drawings of the related technical solutions in the embodiments of the present application or the prior art. It should be understood that the drawings in the following introduction are only for the convenience of clearly describing part of the embodiments in the technical solutions of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0056] Figure 1 is a flowchart of a skin large deformation friction force model calculation method in the present application;

[0057] Figure 2 is a structural schematic diagram of an interactive system for measuring skin friction deformation behavior in the present application;

[0058] Figure 3 is a schematic diagram of collecting skin characteristic index and calculating modeling in the present application;

[0059] Figure 4is a method schematic diagram of a contact area of a contact area in an embodiment of the present application;

[0060] Figure 5 is a method schematic diagram of point cloud registration in an embodiment of the present application;

[0061] Figure 6 is a schematic diagram of a friction force model based on large deformation of skin in an embodiment of the present application.

[0062] Figure 2 in the figure: 1-mechanical arm, 2-six-dimensional force sensor, 3-depth camera, 4-endoscope camera, 5-massage pressure head, 6-skin, 7-control host. DETAILED DESCRIPTION

[0063] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application. For the step numbers in the following embodiments, they are only set for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0064] In the description of the present application, it should be understood that the orientation description, such as the orientation or position relationship indicated by up, down, front, back, left, right, etc. is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0065] In the description of the present application, several meanings are one or more, and multiple meanings are two or more than two, greater than, less than, more than, etc. are not included in the number, and above, below, etc. are included in the number. If it is described as first, second, it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the order of indicated technical features. In addition, "and / or" describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B can represent: A exists alone, A and B exist together, and B exists alone. The character " / " generally represents that the front and rear associated objects have an "or" relationship.

[0066] In the description of the present application, the words such as arrangement, installation, connection, etc. should be understood broadly unless otherwise explicitly limited, and the person skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution.

[0067] Term explanation:

[0068] TCP: Tool Center Point, i.e. tool center point. TCP is the working point of a specific tool on a robot or a mechanical arm, which is usually the point of contact between the tool and the working object. In the present application, it refers to the vertex of the massage pressure head at the end of the mechanical arm.

[0069] In order to solve the existing technical problems, the present application adopts a set of computer image technology system, which is specially used for capturing and analyzing the deformation of skin friction. On this basis, we further fuse the existing binomial friction model to construct a new calculation method for accurately calculating the friction force under the condition of large deformation of skin in human-computer interaction.

[0070] As shown in Figure 1 , the present embodiment provides a skin large deformation friction force model calculation method, which comprises the following steps:

[0071] Step S1: Perform pressure experiment on the skin by the test pressure head to obtain the elastic parameters of the skin.

[0072] In some embodiments, referring to Figure 2 , the test pressure head is installed at the end of the mechanical arm, which is made of transparent material with smooth surface and can be realized in various shapes. An endoscope camera and a depth camera are installed on the test pressure head, the endoscope camera is arranged inside the test pressure head, and the depth camera is arranged outside the test pressure head. A six-dimensional force sensor is also installed between the end of the mechanical arm and the test pressure head.

[0073] As an optional implementation, step S1 comprises the following steps:

[0074] S11, select the test pressure head, which is a cylinder with a diameter of 12mm and made of transparent resin with smooth surface. Start the robot to move the test pressure head to the surface of the experimental object, and determine the starting point as the position where the test pressure head just contacts the skin and no deformation occurs.

[0075] S12, perform normal indentation operation, the indentation depth is specified as 5mm, 10mm, 15mm and 20mm, and each specified depth is repeated 3 times. Use the six-dimensional sensor to record the relationship between the normal force and the indentation depth, and generate the normal force-indentation depth curve , the result is shown in Figure 3 .

[0076] S13, calculate the curve slope of the unloading phase in the normal force-indentation depth curve obtained in step S12 . Calculate the viscoelastic hysteresis loss fraction according to the formula , wherein the viscoelastic hysteresis loss fraction is calculated according to the formula . Calculate the contact area according to the formula , wherein R is the radius of the indenter.

[0077] S14, finally, calculate the corresponding effective elastic modulus according to the formula . With the experimental data example Figure 3 , the finally obtained effective elastic modulus is 47337, 132388, 434883, and 1127121 Pa at 5, 20, 15, and 20 mm, respectively.

[0078] Step S2: collect the skin contact image and the skin deformation point cloud image when the test indenter is in friction with the skin surface.

[0079] As an optional embodiment, step S2 includes the following steps:

[0080] S21, start the robot to move the test indenter to the surface of the experimental object, and determine the starting point as the position where the test indenter is in contact with the skin and no deformation occurs.

[0081] S22, control the test indenter to descend to a normal force of a specified force, which is set to 2N, 4N, 6N, 8N, 10N, 12N, and 14N. Then, control the test indenter to move tangentially while keeping the normal force constant. The six-axis sensor records the three-dimensional force and the TCP position . The endoscope camera records the contact image of the skin and the indenter, and the depth camera records the skin deformation point cloud image at the front end of the indenter.

[0082] Step S3: process the collected skin contact image using a color clustering algorithm to obtain the true skin contact information.

[0083] As an optional embodiment, step S3 includes the following steps:

[0084] S31, referring to Figure 4 , Figure 4 , take the skin contact image at a certain time as an example. Create a mask to retain the information of the indenter part. First, create a completely black mask image with the same size as the input image (640*480). Then, define a circular area on the mask image, which covers the test indenter part in the input image.

[0085] ​​​S32, color space conversion and pixel clustering are performed on the image after masking. First, the image after masking is converted from BGR color space to HSV color space. Then, the image is converted into a one-dimensional array and clustered using the K-means algorithm, with the number of clusters set to 3.

[0086] S33, the number of pixels in each cluster is counted, and the color and number are combined. According to the V value (the depth of color) in the HSV color space, the order is ascending, and the order obtained is the mask area, the non-contact area in the pressure head, and the contact area in the pressure head. The number of pixels in each cluster is , , .The data of the example in Figure 4 is: 280055, 11865, 15280 pixels.

[0087] S34, calculate the contact area of the contact area. First, according to the proportion of the contact area in the pressure head, the contact area of the contact area is calculated. The total contact area is calculated by the pressure head radius R. Then, according to the formula

[0088]

[0089] the real contact area 63.663mm 2 .

[0090] Step S4: process the collected skin deformation point cloud image to obtain skin bulging deformation information.

[0091] As an optional implementation, step S4 includes the following steps:

[0092] S41, point cloud clipping is performed, only the part of the point cloud in the moving direction of the front end of the pressure head is retained as the region of interest.

[0093] S42, point cloud filtering is performed, and Euclidean cluster extraction algorithm is used to remove discrete point cloud clusters.

[0094] S43, point cloud smoothing processing is performed, and the moving least squares method is used to smooth the point cloud. Based on the idea of local fitting, the neighborhood of each point is fitted to a surface.

[0095] ​S44. Perform point cloud registration. To calculate skin bulges during the indentation head's movement, point cloud registration is necessary. Since the 3D camera is fixed to the end of the robotic arm, its movement causes the origin of the coordinate system to change in each frame of the point cloud image. See also... Figure 5 To unify the coordinates of the point cloud origin to the start time of tangential movement in each frame Perform the following steps:

[0096] Since the 3D camera is fixed to the end of the robotic arm, the pressure head TCP coordinates can be used. Inferring the coordinates of the camera origin Therefore, it is possible to determine the shooting time of each frame. Find the TCP coordinates of the pressure head at the corresponding moment. And thus inferred Camera origin coordinates at time .

[0097] Therefore The frame point cloud data at any given time can be obtained through TCP coordinates of the moment and TCP coordinates of the moment Three-axis transformation to Camera coordinate origin at time .

[0098] S45. Calculate skin elevation and deformation characteristics: Definition The height of the contact point between the tip of the indenter and the indenter at a certain moment is calculated by the formula. ,definition The difference between the height of the indenter's front end and the height of its rear end at a given moment is given by... Calculated.

[0099] in The z-coordinate of the TCP. These are the coordinates of the highest point where the front end of the pressure head directly contacts the pressure head. This refers to the height of the protrusion at the front end of the pressure head.

[0100] Step S5: Correct the binary friction model based on elastic parameters, skin contact information, and skin bulge deformation information.

[0101] As an optional implementation, step S5 includes the following steps:

[0102] S51. The binomial friction model is a theoretical framework used to analyze and calculate the frictional force between skin and a rigid body contact surface. This model assumes that the frictional force consists of two independent and non-interacting components:

[0103] a) Adhesion friction component: This component reflects the interfacial shear effect, i.e. the energy dissipated during the intermittent bonding and breaking process of the molecular attraction between the two sliding surfaces.

[0104] b) Deformation friction component: This part of the friction force is generated by the viscoelastic deformation of the skin or soft tissue under the probe tip when sliding, usually showing energy loss caused by ploughing and incomplete recovery of viscoelastic deformation.

[0105] The current bivariate friction model is based on the Hertz contact theory, which is mainly suitable for describing the contact between hard objects and elastic bodies. In the bivariate friction model, the skin is regarded as a soft tissue, and the ploughing effect is often ignored in the calculation of the deformation friction component, while the focus is mainly on the energy dissipation caused by viscoelastic deformation. Greenwood and Tabor provide the theoretical basis for the calculation of this component.

[0106]

[0107] wherein, is the viscoelastic hysteresis loss fraction, is the inherent interfacial shear strength, is the pressure coefficient, is the effective elastic modulus.

[0108] S52, see Figure 6 in combination with the real contact area obtained in step S3 and the deformation characteristics obtained in S4 and correct the bivariate friction model:

[0109]

[0110] wherein, is the mass coefficient, related to the mass of the protuberance, is the proportional coefficient.

[0111] S53, fit the corrected bivariate friction model with the tangential force data obtained in step S2 to obtain and values.

[0112] Taking the experimental data of a certain part at a sliding speed of 10 mm / s as an example, the calculated is 290.7322, is 0.050795. Referring to Table 1, the model deviation of the result calculated using the conventional hertz contact-based binomial friction model is up to 19.11%, and the maximum model deviation obtained by the calculation method of the skin large deformation friction force model based on human-machine contact proposed by the present application is not more than 2.98%.

[0113] Table 1

[0114]

[0115] Note: W is the normal load; is the measured maximum friction force; represents the total friction force calculated by the binomial friction model; is the result of the adhesion component calculated according to the calculation method proposed by the present application; is the result of the deformation component calculated according to the calculation method proposed by the present application; is the result of the total friction force calculated according to the calculation method proposed by the present application.

[0116] The embodiment also provides a skin large deformation friction force model calculation device, comprising:

[0117] at least one processor;

[0118] at least one memory for storing at least one program;

[0119] When the at least one program is executed by the at least one processor, the at least one processor implements Figure 1 the method shown.

[0120] The skin large deformation friction force model calculation device of the embodiment can execute the skin large deformation friction force model calculation method provided by the method embodiment of the present application, can execute the implementation steps of any combination of the method embodiment, and has the corresponding functions and advantages of the method.

[0121] The embodiment also provides a storage medium storing instructions or programs that can execute the skin large deformation friction force model calculation method provided by the method embodiment of the present application, and when the instructions or programs are executed, any combination of the implementation steps of the method embodiment can be executed, and the corresponding functions and advantages of the method are possessed.

[0122] In some alternative embodiments, the function / operations mentioned in the block diagrams can not occur in the order mentioned in the operational illustrations. For example, depending on the involved function / operation, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in reverse order, depending upon the functionality / operations involved. Furthermore, embodiments presented and described in the flowcharts are only examples of implementing the present application. Alternative embodiments can be implemented where various operations are changed, omitted, and / or added. For example, the order of the operations can be changed, and sub-operations described as part of a larger operation can be implemented independently.

[0123] Furthermore, although the present application is described in the context of functional modules, it is to be understood that one or more of the functions and / or features described can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It will also be appreciated that detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation of the modules, in light of the attributes, functions, and internal relationships of the various functional modules disclosed herein, will be apparent to one of ordinary skill in the art, given the benefit of this disclosure. Accordingly, the present application is not limited to the specific embodiments illustrated herein, but is applicable for use in all domains of engineering consistent with the technical concepts disclosed herein. Furthermore, the disclosed specific concepts are merely illustrative and not intended to limit the scope of the present application, which is defined by the appended claims and their equivalents.

[0124] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the part of the technical solutions that make essential contributions to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0125] The logic and / or steps represented in the flow diagrams or otherwise described herein, for example, can be embodied in non-transitory computer- readable medium using any combination of hardware, software, and / or firmware. The logic and / or steps can be implemented using any of various computer- readable media for storing or transmitting this computer-readable instructions, such as magnetic storage media (e.g., hard disks), optical storage media (e.g., CD-ROMs, DVDs), nonvolatile memory storage media (e.g., ROMs, EPROMs, EEPROMs), and / or flash memory devices.

[0126] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via 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.

[0127] It should be understood that aspects of the application can be implemented in hardware, software, firmware, or combinations thereof. In the above-described embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and so forth.

[0128] In the above-described description of the present specification, the description referring to the terms "one embodiment," "another embodiment," or "some embodiments," and the like, means that the particular feature, structure, material, or characteristic being described in connection with the embodiment or example is included in at least one embodiment or example of the present application. The illustrative appearances of the above-described terms in various places in the specification are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0129] While the embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary and are not to be taken as limiting the scope of the application. The scope of the application is defined by the claims and their equivalents.

[0130] The above is the specific description of the preferred embodiment of the application, but the application is not limited to the above-mentioned embodiments, and those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the application, and these equivalent modifications or replacements are all included in the scope defined by the claims of the application.

Claims

1. A method of calculating a friction model of large deformations of skin, characterized in that, The method comprises the following steps: performing a pressure experiment on the skin by a test indenter to obtain an elasticity parameter of the skin; collecting a skin contact image and a skin deformation point cloud image when the test indenter rubs against the skin surface; processing the collected skin contact image by using a color clustering algorithm to obtain skin contact information; processing the collected skin deformation point cloud image to obtain skin bulging deformation information; modifying a bivariate friction model according to the elasticity parameter, the skin contact information and the skin bulging deformation information; the modification of the bivariate friction model according to the elasticity parameter, the skin contact information and the skin bulging deformation information comprises: the bivariate friction model considers that skin friction is composed of a sticking component and a deformation component: wherein, is the deformation component, is the adhesion component; is the viscoelastic hysteresis loss fraction, is the intrinsic interfacial shear strength, is the pressure coefficient, is the effective elastic modulus; is the radius of the test indenter, is the Poisson's ratio of the skin, is the normal loading force; the bivariate friction model is modified in combination with the obtained skin contact information and the skin bulging deformation information: wherein is the height difference between the nose of the test indenter and the end of the test indenter, is the height of the tip of the test indenter at the point of contact with the test indenter; is the mass coefficient, is the proportionality coefficient; is the contact radius of the indentation.

2. The method of claim 1, wherein, the pressure experiment on the skin by the test indenter to obtain the elasticity parameter of the skin comprises: starting a robot to move the test indenter to the surface of an experimental object to determine a starting point as a position where the test indenter just contacts the skin and no deformation is generated; The control test indenter performs a normal indentation operation, records the relationship between the normal force and the indentation depth, and generates a normal force-indentation depth curve ; the curve slope of the unload phase in the calculated normal force-indentation depth curve ;​ According to the slope of the curve The corresponding effective elastic modulus is calculated And the viscoelastic hysteresis loss fraction is calculated .

3. The method of claim 2, wherein, The effective elastic modulus The formula for calculating the effective elastic modulus is: The viscoelastic hysteresis loss fraction The formula for calculating is: wherein represents the contact area, represents the hysteresis energy loss, represents the elastic hysteresis energy.

4. The method of claim 3, wherein, The test indenter is a cylinder with a contact area where R is the radius of the test indenter.

5. The method of claim 1, wherein, the test indenter is made of a transparent material with a smooth surface, and the test indenter is provided with an image collection device; the collection of the skin contact image and the skin deformation point cloud image when the test indenter rubs against the skin surface comprises: starting a robot to move the test indenter to the surface of an experimental object to determine a starting point as a position where the test indenter just contacts the skin and no deformation is generated; controlling the test indenter to move tangentially while keeping the normal force constant; Recording the three-dimensional forces of the test indenter and TCP coordinates ; collecting a skin contact image between the skin and the test indenter, and a skin deformation point cloud image at the front end of the test indenter.

6. The method of claim 1, wherein, the processing of the collected skin contact image by using a color clustering algorithm to obtain skin contact information comprises: processing the skin contact image by creating a mask to obtain information of the test indenter; performing color space conversion and pixel clustering on the image after the mask to obtain a plurality of clusters; counting the number of pixels in each cluster and combining the color and the number together; according to the V value in the HSV color space, the obtained order is the mask area, the non-contact area in the indenter, and the contact area in the indenter in ascending order; calculating the contact area of the contact area as the skin contact information.

7. The method of claim 1, wherein, the processing of the collected skin deformation point cloud image to obtain skin bulging deformation information comprises: performing point cloud clipping on the skin deformation point cloud image to only keep the part of the point cloud at the front end of the indenter in the moving direction as a region of interest. Point cloud registration is performed to unify the point cloud coordinate origin of each frame to the tangential movement start time ; According to the registered point cloud, the skin bump deformation information is calculated: define The height of the front end of the test indenter in contact with the test indenter at a certain moment is calculated by the formula: ; define The height difference between the front end of the test indenter and the end of the test indenter at a certain moment is calculated by the formula: ; wherein The z coordinate of the TCP, The highest point coordinate of the front end of the test indenter in direct contact with the test indenter, The front end of the test indenter bump height.

8. A device for calculating a friction model of large deformations of skin, characterized in that comprise: at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor implements the method of any one of claims 1-7.

9. A computer readable storage medium having stored therein a program which is executable by a processor, characterized in that, The program executable by the processor, when executed by the processor, is used to perform the method of any one of claims 1-7.

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