CN111309145B - Electrostatic force touch rendering method based on physiological and physical modeling - Google Patents

Electrostatic force touch rendering method based on physiological and physical modeling Download PDF

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CN111309145B
CN111309145B CN202010081102.6A CN202010081102A CN111309145B CN 111309145 B CN111309145 B CN 111309145B CN 202010081102 A CN202010081102 A CN 202010081102A CN 111309145 B CN111309145 B CN 111309145B
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孙晓颖
张淼
刘国红
武秋爽
刘健余
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Jilin University
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Abstract

The invention provides an electrostatic force touch rendering method based on physiological and physical modeling, and belongs to the field of virtual reality and human-computer interaction. Determining a first speed, a first normal force and a first tangential force of a finger across the surface of the real material; determining a second speed of the finger across the surface of the electrostatic force tactile reproduction device and a second normal force; carrying out model training by using an artificial intelligence algorithm, and determining a physiological and physical mapping relation f (t); determining a driving tangential force according to the second speed, the second normal force and the physiological physical mapping relation f (t); determining the transient voltage V (t) of an excitation signal applied by the electrostatic force touch representation device to render real materials according to the driving tangential force. The invention establishes a physiological and physical mapping relation and a mapping relation between the tangential force of the finger stroking the touch reappearing device and the excitation signal, and realizes the texture rendering of the real material on the touch reappearing device. The method has no operation restriction and strong universality and expandability.

Description

Electrostatic force touch rendering method based on physiological and physical modeling
Technical Field
The invention relates to the field of virtual reality and man-machine interaction, in particular to an electrostatic force touch rendering method based on physiological physical modeling.
Background
The touch representation is a leading-edge technology in the current human-computer interaction and virtual reality field, can provide better immersive experience for people, and focuses more on human-computer interactivity compared with the traditional vision and hearing. The electrostatic touch reappearance technology is a research hotspot and key point of international touch reappearance at present, and has wide development prospect in the fields of education and teaching, commercial display, medical treatment, entertainment and the like.
The electrostatic force touch reappearing technology is mainly used for providing touch experience by adjusting friction force between a finger and a touch reappearing panel according to the coulomb's law and utilizing the electrostatic force attraction principle. The electrostatic force touch reappearing panel is composed of a glass bottom plate, a transparent conducting polar plate and a transparent insulating film. When an excitation signal is applied to the conductive plate, the positive and negative charges inside the insulating film periodically change, thereby generating a periodically changing electric field. When a finger touches the reproduction panel, the finger periodically carries positive and negative charges due to charge induction, and coulomb force interaction between the charges enables the finger to sense the action of touch force. The friction force borne by the fingertip can be changed through parameter control of the excitation signal, and then different touch experiences are generated. The haptic rendering algorithm is a guarantee for further improving the sense of reality of haptic rendering on the basis of a haptic rendering device. In recent years, with the increasing attention of enterprises and research institutions at home and abroad to the touch reproduction technology, the research on the touch reproduction rendering method of the multimedia terminal is continuously developed.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: in the existing haptic rendering algorithm related to the electrostatic force haptic rendering device, a rendering algorithm based on physical modeling needs to establish an accurate mathematical model for a specific rendering device, the calculation amount is large, and the rendering effect is not ideal due to over simplification of the physical model. Based on a data-driven rendering algorithm, a universal relation between physical touch and touch feeling is not established in the existing method, namely a physical model is only used for rendering experimental data, most rendering methods are based on the condition of limiting the contact between a finger and a touch screen, experience feeling is poor, and in addition, rendering capability results of the rendering algorithm are often limited by the experimental data, and the rendering algorithm is not universal and extensible.
Disclosure of Invention
The invention provides an electrostatic force touch rendering method based on physiological physical modeling, which realizes the rendering of textures of real materials on an electrostatic force touch rendering device by establishing a physiological physical mapping relation of speed, normal force and tangential force when a finger slides over the real materials and a mapping relation between the tangential force and an excitation signal when the finger slides over a touch rendering device. The method has the advantages of no operation restriction, wide rendering range, strong universality and expandability and good rendering effect.
The technical scheme adopted by the invention is that the method comprises the following steps:
(1) determining a first velocity v of the finger across the surface of the real material1First normal force FN1And a first tangential force Ff1；
(2) Determining a second velocity v of the finger across the surface of the electrostatic force haptic rendering device2And a second normal force FN2；
(3) According to a first speed v1First normal force FN1And a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine a physiological physical mapping relation f (t);
(4) according to a second speed v2Second normal force FN2And determining a driving tangential force F (t) according to the physiological and physical mapping relation F (t)f；
(5) According to the driving tangential force FfDetermining the transient voltage V (t) of the excitation signal applied when the electrostatic force touch reappearing device renders real materials.
In the step (3) of the present invention, the first speed v is used1First normal force FN1And a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine a physiological-physical mapping relation f (t), and specifically comprising the following steps:
applying a first normal force FN1Dividing the n first normal forces F into n segments at a fixed intervalN1The interval is fixed to be n first normal component forces FN1_nWherein n is an integer greater than or equal to 1;
at n first normal force components FN1_nAccording to n first normal forces F respectivelyN1First speed v corresponding to interval1With a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine n segmented physiological-physical mapping relations fn(t)。
In the step (3) of the present invention,according to a first speed v1First normal force FN1And a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine a physiological-physical mapping relation f (t), and specifically comprising the following steps:
at a first speed v1Divided into n segments at fixed intervals and dividing n first speeds v1Interval fixed value is n first speed components v1_nWherein n is an integer greater than or equal to 1;
at n first velocity components v1_nAccording to n first speeds v, respectively1The first normal force F corresponding to the intervalN1With a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine n segmented physiological-physical mapping relations fn(t)。
In the step (3) of the present invention, the first speed v is used1First normal force FN1And a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine a physiological-physical mapping relation f (t), and specifically comprising the following steps:
at a first speed v1And a first normal force FN1Separately or simultaneously as input variables, the first tangential force Ff1As an output parameter, carrying out model training by using an artificial intelligence algorithm to obtain a physiological-physical mapping relation f (t);
in the step (3), the artificial intelligence algorithm is an RBF neural network algorithm.
In the step (4) of the present invention, the second speed v is used2Second normal force FN2And determining a driving tangential force F (t) according to the physiological and physical mapping relation F (t)fThe method specifically comprises the following steps:
judging the current second normal force FN2When the second normal force F is takenN2Equal to mth first normal component FN1_mThen, m is a positive integer less than or equal to n, according to the corresponding m-th segmented physiological-physical mapping relation fm(t) and the current second speed v2Determining the current driving tangential force Ff；
When the second normal force FN2Is taken from the value ofm first normal component force FN1_mFirst normal component force F of m-1N1_m-1According to the corresponding m-th segment physiological and physical mapping relation fm(t) and the (m-1) th segment physiological-physical mapping relation fm-1(t) and the current second speed v2Determining the mth drive tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1And according to the m-th driving tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1Determining the current driving tangential force Ff。
In the step (4) of the present invention, the second speed v is used2Second normal force FN2And determining a driving tangential force F (t) according to the physiological and physical mapping relation F (t)fThe method specifically comprises the following steps:
judging the current second speed v2When the second speed v is equal to2Is equal to the m-th first velocity component v1_mThen, m is a positive integer less than or equal to n, according to the corresponding m-th segmented physiological-physical mapping relation fm(t) and the current second normal force FN2Determining the current driving tangential force Ff；
When the second speed v2Is taken at the mth first velocity component v1_mFirst velocity component v of m-11_m-1According to the corresponding m-th segment physiological and physical mapping relation fm(t) and the (m-1) th segment physiological-physical mapping relation fm-1(t) and the current second normal force FN2Determining the mth drive tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1And according to the m-th driving tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1Determining the current driving tangential force Ff。
In the step (5) of the present invention, the tangential force F is driven according tofDetermining an excitation signal transient voltage V (t) applied when the electrostatic force touch representation device renders a real material, specifically comprising:
calculating to obtain transient voltage V (t) of the excitation signal according to the following formula;
Ff＝μAV2(t)+μFN2
wherein A is the circuit coefficient of the electrostatic force touch reappearance device, and mu is the friction coefficient of the screen material of the electrostatic force touch reappearance device.
In the step (5) of the present invention, the tangential force F is driven according to the driving forcefDetermining an excitation signal transient voltage V (t) applied when the electrostatic force touch representation device renders a real material, specifically comprising:
determining a second tangential force F when the finger is stroked across the surface of the electrostatic force haptic reproduction device when the electrostatic force haptic reproduction device applies the excitation signal Y (t)f2According to a second tangential force Ff2And corresponding excitation signal Y (t) for determining the first mapping relation f1(t) and according to the driving tangential force FfAnd a first mapping relation f1(t) determining the transient voltage V (t) of the excitation signal applied when the electrostatic force touch representation device renders real materials.
In step (5) of the present invention, the first mapping relationship f1And (t) is a nonlinear mapping relation based on a NARX neural network model.
The tangential force is widely considered as the physical parameter which is most relevant to the roughness feeling influence of the finger on the texture of the object when the finger slides across the surface of the object, therefore, the invention provides an electrostatic force touch rendering method based on the physical modeling of the speed, the pressure and the tangential force when the finger slides across the surface of the object.
The invention has the following beneficial effects: the method adopts a data-driven technical means, establishes a mapping relation between a normal force and a sliding speed when a finger slides a real material and a tangential force sensed by the finger, establishes a physiological and physical model of finger sliding action and finger perception touch, and also establishes a mapping relation between the tangential force and an excitation signal when the finger slides a touch reappearing device, thereby realizing texture rendering of the real material on the electrostatic force touch reappearing device. The method has the advantages of no operation restriction, wide rendering range, and strong universality and expandability.
Drawings
FIG. 1 is a flow diagram of an electrostatic force haptic rendering method based on physiological physical modeling according to one embodiment of the present invention;
FIG. 2 is a schematic topology diagram of an RBF neural network according to one embodiment of the present invention;
FIG. 3 is a schematic diagram of a physiological-physical mapping of an electrostatic force haptic rendering method based on physiological-physical modeling according to an embodiment of the present invention;
FIG. 4 is a schematic topology diagram of a NARX neural network according to one embodiment of the present invention;
fig. 5 is a schematic diagram of a first mapping relationship of an electrostatic force haptic rendering method based on physiological physical modeling according to an embodiment of the present invention.
Detailed Description
Comprises the following steps:
(1) determining a first velocity v of the finger across the surface of the real material1First normal force FN1And a first tangential force Ff1；
(2) Determining a second velocity v of the finger across the surface of the electrostatic force haptic rendering device2And a second normal force FN2；
(3) According to a first speed v1First normal force FN1And a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine a physiological physical mapping relation f (t);
(4) according to a second speed v2Second normal force FN2And determining a driving tangential force F (t) according to the physiological and physical mapping relation F (t)f；
(5) According to the driving tangential force FfDetermining the transient voltage V (t) of the excitation signal applied when the electrostatic force touch reappearing device renders real materials.
In the step (3) of the present invention, the first speed v is used1First normal force FN1And a first tangential force Ff1Performing model training by using artificial intelligence algorithm to determineThe physio-physical mapping relationship f (t) specifically includes:
applying a first normal force FN1Dividing the n first normal forces F into n segments at a fixed intervalN1The interval is fixed to be n first normal component forces FN1_nWherein n is an integer greater than or equal to 1;
at n first normal force components FN1_nAccording to n first normal forces F respectivelyN1First speed v corresponding to interval1With a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine n segmented physiological-physical mapping relations fn(t)。
In the step (3) of the present invention, the first speed v is used1First normal force FN1And a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine a physiological-physical mapping relation f (t), and specifically comprising the following steps:
at a first speed v1Divided into n segments at fixed intervals and dividing n first speeds v1Interval fixed value is n first speed components v1_nWherein n is an integer greater than or equal to 1;
at n first velocity components v1_nAccording to n first speeds v, respectively1The first normal force F corresponding to the intervalN1With a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine n segmented physiological-physical mapping relations fn(t)。
In the step (3) of the present invention, the first speed v is used1First normal force FN1And a first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine a physiological-physical mapping relation f (t), and specifically comprising the following steps:
at a first speed v1And a first normal force FN1Separately or simultaneously as input variables, the first tangential force Ff1As an output parameter, carrying out model training by using an artificial intelligence algorithm to obtain a physiological-physical mapping relation f (t);
in the step (3), the artificial intelligence algorithm is an RBF neural network algorithm.
In the step (4) of the present invention, the second speed v is used2Second normal force FN2And determining a driving tangential force F (t) according to the physiological and physical mapping relation F (t)fThe method specifically comprises the following steps:
judging the current second normal force FN2When the second normal force F is takenN2Equal to mth first normal component FN1_mThen, m is a positive integer less than or equal to n, according to the corresponding m-th segmented physiological-physical mapping relation fm(t) and the current second speed v2Determining the current driving tangential force Ff；
When the second normal force FN2Is at the mth first normal component FN1_mFirst normal component force F of m-1N1_m-1According to the corresponding m-th segment physiological and physical mapping relation fm(t) and the (m-1) th segment physiological-physical mapping relation fm-1(t) and the current second speed v2Determining the mth drive tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1And according to the m-th driving tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1Determining the current driving tangential force Ff。
In the step (4) of the present invention, the second speed v is used2Second normal force FN2And determining a driving tangential force F (t) according to the physiological and physical mapping relation F (t)fThe method specifically comprises the following steps:
judging the current second speed v2When the second speed v is equal to2Is equal to the m-th first velocity component v1_mThen, m is a positive integer less than or equal to n, according to the corresponding m-th segmented physiological-physical mapping relation fm(t) and the current second normal force FN2Determining the current driving tangential force Ff；
When the second speed v2Is taken at the mth first velocity component v1_mFirst velocity component v of m-11_m1, respectively according to the corresponding m-th segmented physiological and physical mapping relation fm(t) and the (m-1) th segment physiological-physical mapping relation fm-1(t) and the current second normal forceFN2Determining the mth drive tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1And according to the m-th driving tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1Determining the current driving tangential force Ff。
In the step (5) of the present invention, the tangential force F is driven according tofDetermining an excitation signal transient voltage V (t) applied when the electrostatic force touch representation device renders a real material, specifically comprising:
calculating to obtain transient voltage V (t) of the excitation signal according to the following formula;
Ff＝μAV2(t)+μFN2
wherein A is the circuit coefficient of the electrostatic force touch reappearance device, and mu is the friction coefficient of the screen material of the electrostatic force touch reappearance device.
In the step (5) of the present invention, the tangential force F is driven according to the driving forcefDetermining an excitation signal transient voltage V (t) applied when the electrostatic force touch representation device renders a real material, specifically comprising:
determining a second tangential force F when the finger is stroked across the surface of the electrostatic force haptic reproduction device when the electrostatic force haptic reproduction device applies the excitation signal Y (t)f2According to a second tangential force Ff2And corresponding excitation signal Y (t) for determining the first mapping relation f1(t) and according to the driving tangential force FfAnd a first mapping relation f1(t) determining the transient voltage V (t) of the excitation signal applied when the electrostatic force touch representation device renders real materials.
In step (5) of the present invention, the first mapping relationship f1And (t) is a nonlinear mapping relation based on a NARX neural network model.
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
For better understanding of the present invention, the electrostatic force haptic rendering method based on physiological physical modeling provided by the embodiment of the present invention is described in detail below with reference to fig. 1 to 5. It should be noted that these examples are not intended to limit the scope of the present disclosure.
Fig. 1 is a flowchart illustrating an electrostatic force haptic rendering method based on physiological physical modeling according to an embodiment of the present invention, in which an electrostatic force haptic rendering method 100 based on physiological physical modeling includes steps 110 to 150.
Step 110, determining a first velocity v of a finger across a surface of a real material1First normal force FN1And a first tangential force Ff1。
In this step, a first speed v of the finger across the surface of the real material1The current position (x) of the finger on the surface of the real material can be obtained through a speed measuring device or a positioning device, and specifically, the current position (x) of the finger on the surface of the real material can be obtained through the positioning device in real timei，yi) Last time position (x)i-1，yi-1) And a time interval t for determining in real time a time-varying first velocity v of the finger across the surface of the real material1In particular, the first speed v1The calculation formula of (2) is as follows:
Figure BDA0002380199510000071
first normal force F when finger is stroked across the surface of real materialN1And a first tangential force Ff1Can be measured in real time by a multi-dimensional force sensor arranged at the bottom of the real material.
Step 120, determining a second velocity v of the finger across the surface of the electrostatic force haptic rendering device2And a second normal force FN2。
In this step, a second velocity v of the finger across the surface of the electrostatic force tactile reproduction device2The current time position (x ') of the finger on the surface of the electrostatic force tactile representation device can be obtained by a speed measuring device or a positioning device, and particularly the current time position (x ') of the finger on the surface of the electrostatic force tactile representation device can be obtained in real time by the positioning device 'i，y’i) Last time position (x'i-1，y’i-1) And a time interval t' for determining in real time a second velocity v of the finger as it moves across the surface of the electrostatic force tactile reproduction device2In particular, v2The calculation formula of (2) is as follows:
Figure BDA0002380199510000081
second normal force F when a finger is stroked across the surface of the electrostatic force haptic reproduction deviceN2Can be measured in real time by a multi-dimensional force sensor disposed at the bottom of the electrostatic force tactile reproduction apparatus.
Step 130, according to the first speed v1First normal force FN1And a first tangential force Ff1And carrying out model training by using an artificial intelligence algorithm to determine the physiophysical mapping relation f (t).
In an optional embodiment, step 130 may specifically include:
step 1301, applying the first normal force FN1Dividing the n first normal forces F into n segments at a fixed intervalN1The interval is fixed to be n first normal component forces FN1_nWherein n is an integer greater than or equal to 1;
at n first normal force components FN1_nAccording to n first normal forces F respectivelyN1First speed v corresponding to interval1With a first tangential force Ff1Determining n segmented physio-physical mapping relations fn(t)。
In particular, according to the measured first normal force FN1The data features are subjected to segmented values, for example: assuming the measured first normal force FN1The range of the first normal force F can be 0-3NN1Segmenting at a pitch value of 0.5N to obtain N segments with respect to the first normal force FN1And the first normal force F is applied according to a certain valueN1And taking values according to the section.
Specifically, taking the above as an example, the value taking manner may be: taking values according to the maximum value of the range of the interval, namely the mth first normal component force FN1_mCan be as follows: fN_m0.5N by m. The value taking mode can also be as follows: n first normal forces FN1And respectively carrying out principal component analysis on the intervals, and finding out a point with the highest relative frequency of data as a normal component value of the interval.
In an optional embodiment, step 130 may further include:
step 1302, transferring the first speed v1Divided into n segments at fixed intervals and dividing n first speeds v1Interval fixed value is n first speed components v1_nWherein n is an integer greater than or equal to 1;
at n first velocity components v1_nAccording to n first speeds v, respectively1The first normal force F corresponding to the intervalN1With a first tangential force Ff1Determining n segmented physio-physical mapping relations fn(t)。
In particular, according toMeasured first velocity v1The data features are subjected to segmented values, for example: assuming a measured first speed v1Can be in the range of 0 to 100mm/s, and the measured first velocity v can be measured1Segmented according to a pitch value of 1mm/s to obtain n segments with respect to a first speed v1And the first speed v is set according to a certain value mode1And taking values according to the section.
Specifically, taking the above as an example, the value taking manner may be: taking values according to the maximum value of the range of the interval, i.e. the mth first velocity component v1_mCan be as follows: v. of1_m1 mm/s. The value taking mode can also be as follows: at n first speeds v1And respectively carrying out principal component analysis on the intervals, and finding out the point with the highest relative frequency of data as the value of the velocity component in the interval.
In an optional embodiment, step 130 may further include:
step 1303, comparing the first speed v1And a first normal force FN1Separately or simultaneously as input variables, the first tangential force Ff1And (5) as an output parameter, carrying out model training by using an artificial intelligence algorithm to obtain a physiophysical mapping relation f (t).
In particular, the first speed v may be simultaneously set1First normal force FN1As an input variable, a first tangential force Ff1And (3) as an output parameter, carrying out model training by using an artificial intelligence algorithm to determine a physiophysical mapping relation f (t).
Specifically, the n segmented physiological-physical mapping relationships f determined in step 1301 can be further determinedn(t) at n first normal force components FN1_nUnder the condition of (1), respectively applying n first normal forces FN1First speed v corresponding to interval1As input variable, the corresponding first tangential force Ff1As an output parameter, performing model training by using an artificial intelligence algorithm to determine n segmented physiological-physical mapping relations fn(t)。
Specifically, the n segmented physio-physical mapping relationships f determined in step 1302 can be further determinedn(t) at n firstVelocity component v1_nUnder the condition of (1), n first speeds v are respectively set1The first normal force F corresponding to the intervalN1As input variable, the corresponding first tangential force Ff1As an output parameter, performing model training by using an artificial intelligence algorithm to determine n segmented physiological-physical mapping relations fn(t)。
In an alternative embodiment, the artificial intelligence algorithm is an RBF neural network algorithm.
Specifically, the RBF (Radial Basis Function) neural network is used as a feedforward neural network with excellent performance, can approach any nonlinear Function with high precision, and has a compact topological structure and global approximation capability. Fig. 2 shows a topology diagram of the RBF neural network. The RBF neural network is divided into three layers, namely an input layer, a hidden layer and an output layer. The RBF neural network may be expressed as follows:
Figure BDA0002380199510000091
wherein x is an input vector, y (x, w) is an output vector of the RBF neural network, and wiFor weight, I is the number of hidden layer neurons, and the number of I can be determined according to the actual rendered real material characteristics. c. CiIs a central vector, | x-ci| is the distance to the center and H is a radial basis function, preferably a gaussian radial basis function. And adopts a gradient descent optimization method to carry out on wiAnd (6) updating.
Specifically, the RBF neural network is trained after being designed, the measured data is divided into a training set and a test set, and the training set is used for training parameters of the RBF neural network model. Sample center vector c may be determined using K-means clusteringiThe objective function can be determined by MSE (mean square error), the equation is as follows:
Figure BDA0002380199510000101
wherein m is the number of samples of the training set,
Figure BDA0002380199510000102
is a predicted value of a sample, yiIs the actual value of the sample. And using gradient descent method or L-M algorithm to pair wiThe updating is performed such that after each update the value of the objective function becomes smaller. And the fitting accuracy is verified through the test set.
More specifically, as illustrated in fig. 3, it may be determined that the input vector of the RBF neural network is x ═ v according to step 1301FN1]The output vector of the RBF neural network is y (x, w) ═ Ff1]。
More specifically, n segmented physio-physical mapping relations f may also be determined according to step 1301n(t) at n first normal force components FN1_nAccording to n first normal forces F respectivelyN1Determining the input vector of the corresponding n RBF neural networks as xn＝[v1]And the output vectors of the n RBF neural networks are yn(xn,w)＝[Ff]。
More specifically, n segmented physio-physical mappings f may also be determined according to step 1302n(t) at n first velocity components v1_nUnder the condition of (1), n first speeds v are respectively set1Determining the input vector of the corresponding n RBF neural networks as xn＝[FN1]And the output vectors of the n RBF neural networks are yn(xn,w)＝[Ff]。
Step 140, according to the second speed v2Second normal force FN2And determining a driving tangential force F (t) according to the physiological and physical mapping relation F (t)f。
In an optional embodiment, step 140 may specifically include:
1401, judging the current second normal force FN2When the second normal force F is takenN2Equal to mth first normal component FN1_mThen, m is a positive integer less than or equal to n, according to the corresponding m-th segmented physiological-physical mapping relation fm(t) and the current second speed v2Determining the current driveTangential force Ff；
When the second normal force FN2Is at the mth first normal component FN1_mFirst normal component force F of m-1N1_m-1According to the corresponding m-th segment physiological and physical mapping relation fm(t) and the (m-1) th segment physiological-physical mapping relation fm-1(t) and the current second speed v2Determining the mth drive tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1And according to the m-th driving tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1Determining the current driving tangential force Ff。
In an optional embodiment, step 140 may specifically include:
1402, judging the current second speed v2When the second speed v is equal to2Is equal to the m-th first velocity component v1_mThen, m is a positive integer less than or equal to n, according to the corresponding m-th segmented physiological-physical mapping relation fm(t) and the current second normal force FN2Determining the current driving tangential force Ff；
When the second speed v2Is taken at the mth first velocity component v1_mFirst velocity component v of m-11_m-1According to the corresponding m-th segment physiological and physical mapping relation fm(t) and the (m-1) th segment physiological-physical mapping relation fm-1(t) and the current second normal force FN2Determining the mth drive tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1And according to the m-th driving tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1The current driving tangential force Ff is determined.
Step 150, depending on the driving tangential force FfDetermining the transient voltage V (t) of the excitation signal applied when the electrostatic force touch reappearing device renders real materials.
In an optional embodiment, step 150 may specifically include:
step 1501, calculating to obtain an excitation signal transient voltage V (t) according to the following formula;
Ff＝μAV2(t)+μFN2
namely:
Figure BDA0002380199510000111
wherein A is the circuit coefficient of the electrostatic force touch reappearance device, and mu is the friction coefficient of the screen material of the electrostatic force touch reappearance device.
In an optional embodiment, step 150 may specifically include:
step 1502, determining a second tangential force F when the finger is stroked across the surface of the electrostatic force tactile reproduction apparatus while the electrostatic force tactile reproduction apparatus is applying the excitation signal Y (t)f2According to a second tangential force Ff2And corresponding excitation signal Y (t) for determining the first mapping relation f1(t) and according to the driving tangential force FfAnd a first mapping relation f1(t) determining the transient voltage V (t) of the excitation signal applied when the electrostatic force touch representation device renders real materials.
In an alternative embodiment, the first mapping relation f1(t) may be a non-linear mapping relationship based on a NARX neural network model.
Specifically, the NARX (nonlinear autoregressive with external input model) neural network is a well-structured dynamic neural network, which introduces the output vector of the BP neural network into the input vector through external feedback after the output vector is delayed and maintained. As shown in fig. 4, which is a topology diagram of the RBF neural network, the NARX neural network can be expressed as follows:
y[n+1]＝F(y[n],…,y[n-ky+1],x[n],…,x[n-kx+1])
in the formula, y [ n +1 ]]For the output prediction value, x [ n ]],…,x[n-kx+1]Representing inputs from outside the network, y n, for present and past input values],…,y[n-ky+1]For the delay value of the output, F is a non-linear function of its argument. The value of k and the number of implied layers of the NARX neural network can be determined according to the performance of the actual electrostatic force tactile reproduction device and the applied excitation signal y (t).The excitation signal y (t) may be a carrier signal.
In particular, the NARX neural network is trained after it has been designed, the measured excitation signal y (t) -the second tangential force Ff2The data pairs are divided into training sets and test sets, and the training sets are used for training the parameters of the NARX neural network model. The objective function can be determined by MSE (mean square error), the equation is as follows:
Figure BDA0002380199510000121
wherein m is the number of samples of the training set,
Figure BDA0002380199510000122
is a predicted value of a sample, yiIs the actual value of the sample. And the weight of the neural network is updated by adopting a gradient descent method or an L-M algorithm, so that the value of the target function is smaller after each update. And the fitting accuracy is verified through the test set.
And the driving tangential force F obtained according to step 240fAnd the first mapping relation f1(t) determining the transient voltage V (t) of the excitation signal applied when the electrostatic force touch representation device renders real materials. Specifically, as shown in FIG. 5, a tangential force F will be drivenfUsing the trained NARX neural network model as an input, recursive computation is performed on the excitation signal transient voltage v (t) as an output.
According to the electrostatic force touch rendering method based on the physiological and physical modeling, disclosed by the embodiment of the invention, the mapping relation among the normal force and the sliding speed of the finger when the finger slides through the real material and the tangential force sensed by the finger is established, so that a physiological and physical model of the finger sliding action and the finger sensing touch is established, and an important theoretical basis is provided for the subsequent research of a touch rendering method through physical signals. And the mapping relation between the speed and the normal force of the finger when the touch screen slides and the driving tangential force sensed by the finger on the touch screen is established through the physiological physical model, and the mapping relation between the driving tangential force and the transient voltage of the excitation signal applied when the electrostatic force touch representation device renders real materials is established by taking the mapping relation as a connection, so that the texture rendering of the real materials by the electrostatic force touch representation device is realized. The method has the advantages of no operation restriction, wide rendering range, strong universality and expandability and good rendering effect.
It is to be understood that the invention is not limited to the particular arrangements and instrumentality described in the above embodiments and shown in the drawings. For convenience and brevity of description, detailed description of a known method is omitted here, and for the specific working processes of the system, the module and the unit described above, reference may be made to corresponding processes in the foregoing method embodiments, which are not described herein again.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (9)

1. An electrostatic force touch rendering method based on physiological physical modeling is characterized by comprising the following steps:
(1) determining a first velocity v of the finger across the surface of the real material1First normal force FN1And a first tangential directionForce Ff1；
(2) Determining a second velocity v of the finger across the surface of the electrostatic force haptic rendering device2And a second normal force FN2；
(3) According to said first speed v1Said first normal force FN1And the first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm, wherein the artificial intelligence algorithm is a RBF neural network algorithm and determines a physio-physical mapping relation f (t);
(4) according to said second speed v2Said second normal force FN2And the physiological physical mapping relation F (t) is determined, and the driving tangential force F is determinedf；
(5) According to said driving tangential force FfDetermining an excitation signal transient voltage V (t) applied when the electrostatic force tactile reproduction apparatus renders the real material.
2. The electrostatic force haptic rendering method based on physiological physical modeling according to claim 1, wherein: in the step (3), the first speed v is determined according to1Said first normal force FN1And the first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine a physiological-physical mapping relation f (t), and specifically comprising the following steps:
applying the first normal force FN1Dividing the force into n segments at a fixed interval and applying n first normal forces FN1The interval is fixed to be n first normal component forces FN1_nWherein n is an integer greater than or equal to 1;
at n of said first normal force components FN1_nAccording to n first normal forces F respectivelyN1The first speed v corresponding to a section1With said first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine n segmented physiological-physical mapping relations fn(t)。
3. The electrostatic force haptic rendering method based on physiological physical modeling according to claim 1Characterised in that in said step (3), it is carried out according to said first speed v1Said first normal force FN1And the first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine a physiological-physical mapping relation f (t), and specifically comprising the following steps:
setting the first speed v1Divided into n segments at regular intervals and dividing n of said first speeds v1Interval fixed value is n first speed components v1_nWherein n is an integer greater than or equal to 1;
at n of the first velocity components v1_nAccording to n first speeds v, respectively1The first normal force F corresponding to the intervalN1With said first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine n segmented physiological-physical mapping relations fn(t)。
4. Electrostatic force haptic rendering method based on physiological physical modeling according to any of claims 1-3, wherein the first velocity v is a function of1Said first normal force FN1And the first tangential force Ff1Carrying out model training by using an artificial intelligence algorithm to determine a physiological-physical mapping relation f (t), and specifically comprising the following steps:
setting the first speed v1And said first normal force FN1Separately or simultaneously as input variables, the first tangential force Ff1And performing model training by using an artificial intelligence algorithm as an output parameter to obtain the physiophysical mapping relation f (t).
5. The electrostatic force haptic rendering method based on physiological physical modeling according to claim 1, wherein in the step (4), the second velocity v is determined according to2Said second normal force FN2And the physiological physical mapping relation F (t) is determined, and the driving tangential force F is determinedfThe method specifically comprises the following steps:
judging the current second normal force FN2When the second normal force F is greater than the first normal forceN2Equal to mth first normal component FN1_mM is a positive integer less than or equal to n, according to the m-th segmented physiological-physical mapping relation fm(t) and the current said second speed v2Determining the current said driving tangential force Ff；
When the second normal force FN2Is at the mth first normal component FN1_mFirst normal component force F of m-1N1_m-1According to the corresponding m-th segment physiological and physical mapping relation fm(t) and the (m-1) th segment physiological-physical mapping relation fm-1(t) and the current second speed v2Determining the mth drive tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1And according to said m-th driving tangential force Ff_mAnd the m-1 th driving tangential force Ff_m-1Determining the current said driving tangential force Ff。
6. The electrostatic force haptic rendering method based on physiological physical modeling according to claim 1, wherein in the step (4), the second velocity v is determined according to2Said second normal force FN2And the physiological physical mapping relation F (t) is determined, and the driving tangential force F is determinedfThe method specifically comprises the following steps:
judging the current second speed v2When said second speed v is greater than or equal to2Is equal to the m-th first velocity component v1_mM is a positive integer less than or equal to n, according to the m-th segmented physiological-physical mapping relation fm(t) with said current second normal force FN2Determining the current said driving tangential force Ff；
When the second speed v is2Is taken at the mth first velocity component v1_mFirst velocity component v of m-11_m-1According to the corresponding m-th segment physiological and physical mapping relation fm(t) and the (m-1) th segment physiological-physical mapping relation fm-1(t) and the current second normal force FN2Determining the mth drive tangential force Ff_mAnd m-1 th driving tangential force Ff_m-1Root of Chinese angelicaAccording to the m-th driving tangential force Ff_mAnd the m-1 th driving tangential force Ff_m-1Determining the current said driving tangential force Ff。
7. The electrostatic force haptic rendering method based on physiological physical modeling according to claim 1, wherein in the step (5), the driving tangential force F is based onfDetermining an excitation signal transient voltage v (t) applied by the electrostatic force tactile representation device when rendering the real material, specifically comprising:
calculating the transient voltage V (t) of the excitation signal according to the following formula;
Ff＝μAV2(t)+μFN2
wherein A is the circuit coefficient of the electrostatic force touch reappearance device, and mu is the friction coefficient of the screen material of the electrostatic force touch reappearance device.
8. Electrostatic force haptic rendering method based on physiological physical modeling, according to claim 7, characterized in that said driving tangential force F is a function offDetermining an excitation signal transient voltage v (t) applied by the electrostatic force tactile representation device when rendering the real material, specifically comprising:
determining a second tangential force F when a finger is stroked across the surface of the electrostatic force haptic reproduction device when the electrostatic force haptic reproduction device applies an excitation signal Y (t)f2According to said second tangential force Ff2And the corresponding excitation signal Y (t) for determining a first mapping relation f1(t) and according to said driving tangential force FfAnd the first mapping relation f1(t) determining an excitation signal transient voltage V (t) applied when the electrostatic force tactile reproduction apparatus renders the real material.
9. The electrostatic force haptic rendering method based on physiological physical modeling according to claim 8, wherein the first mapping relationship f1And (t) is a nonlinear mapping relation based on a NARX neural network model.
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