CN111123945A - Hybrid control-based biped robot gait track generation method and application - Google Patents
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Abstract
The invention relates to a biped robot gait track generation method based on hybrid control and application thereof. Compared with the prior art, the method has the advantages of higher track precision, simple algorithm, quickness and the like.
Description
Technical Field
The invention relates to a robot walking control method, in particular to a biped robot gait track generation method based on hybrid control and application thereof.
Background
The intelligent service robot is mostly applied to non-definite and non-structured environments, and the motion of the robot cannot be planned by a track presetting method, so that the robot is required to have the capability of automatically planning a motion track in a dynamic environment, the online adjustment of gait can be realized in real time according to the change of an external environment, and the intelligent service robot has better environmental adaptability. The human can deal with various complex and unknown environments, and the corresponding walking gait is formed according to the change of the environments.
At present, a robot walking control method based on a bionic motion mechanism is widely concerned, and is applied to motion control of a robot through analysis and engineering simulation of animal motion modes, wherein a control method of a central mode generator (CPG) is a typical representative of bionic control. The greatest advantage of CPG control is adaptability, however, the main difficulty of CPG control is the mapping relationship between the modulation of parameters and the trajectory of the robot. At present, some documents combine fuzzy control and bionic control to further improve the walking adaptability of the robot based on the bionic control, and generate variable walking parameters such as stride and walking speed. The method mainly modulates relevant parameters of a CPG network model on line through fuzzy control, thereby realizing the control of certain joints of the robot, such as the walking of the robot, the rotation of a robot fish, the movement of a snake-shaped robot, the step length adjustment of the robot and the like, the interaction with the walking environment of the robot body is less, and the environment information can not be effectively coupled to a control system for real-time feedback. At present, some walking control methods consider feedback obtained from environmental information to modulate adjustable factors in a robot control system, such as a certain parameter in a CPG model and the position of the center of mass of the robot, and the methods make good progress in the aspect of adaptive walking of the robot and successfully control the robot to perform an up-slope and down-slope walking experiment. At present, the methods mainly seek an optimal solution by designing a feedback link and then performing experimental fitting or an optimization algorithm on feedback parameters in the feedback link. The main limitation of the former method is that the time cost of the experiment is high, and the main defect of the latter method is that the optimization algorithm carries out parameter optimization aiming at a specific environment, so that the adaptability is low.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a hybrid control-based gait track generation method of a biped robot with timely feedback and high track precision and application thereof.
The purpose of the invention can be realized by the following technical scheme:
a biped robot gait track generation method based on hybrid control combines fuzzy control and CPG bionic control, takes environment information fed back by real-time pose in the walking process of a robot as input quantity of a fuzzy control system, obtains the walking gait track information of the robot based on the fuzzy control system, and generates a corresponding gait track through a CPG model based on the gait track information.
Further, the environment information includes a slope angle and a concave-convex height.
Further, the gait track information comprises walking stride, average walking speed and foot lifting height.
Further, the fuzzy control system is constructed based on a T-S fuzzy control system, a trigonometric function is used as a membership function, a fuzzy rule is constructed according to a least square method, and deblurring is achieved through a gravity center weighted average method.
Further, in the fuzzy rule adopted in the fuzzy control system, the input field is divided into four parts, each part is represented by a first-order curved surface RSM, and the formula of the RSM of each part is as follows:
RSMi＝yi＝ai1x1+ai2x2+…+aikxk+ai(k+1)
wherein i is 1,2,3,4, yiFor the prediction obtained by the fuzzy control system, xkAs an input variable, aikCoefficients corresponding to first order surfaces.
Further, the coefficients of the first order surface are determined by the following formula:
wherein the content of the first and second substances,
to estimate the coefficients, X is the input variable matrix and Y is the true output matrix.
Further, the deblurring formula adopted in the fuzzy control system is as follows:
wherein, yiPredicted values, w, for the fuzzy control systemiIs the weight, n is the number of output quantities.
Further, the fuzzy control system adopts a data set size of 3kAnd +/-R, k is the input quantity number of the fuzzy control system, and R is a set integer.
The invention also provides a walking control method of the biped robot, which adopts the gait track generation method of the biped robot to generate the gait track and controls the walking of the biped robot based on the gait track.
Further, the gait track information of each step in the gait track is mapped to the amplitude and the phase angle of the CPG model, and CPG bionic control is achieved.
Compared with the prior art, the invention obtains the feedback quantity of the environmental information from the pose information of the robot and obtains the expected walking gait track of the robot through the T-S fuzzy control system, and has the following beneficial effects:
(1) the robot can be controlled to generate a suitable gait track according to the change of the environmental information by considering the environmental information, the feedback is timely, and the track precision is improved.
(2) The walking parameters (stride, foot lifting height, walking speed and the like) of the robot are directly modulated according to the change of the environmental information, rather than controlling a certain adjustable factor in the network, the physical significance of the modulation parameters is clear, and researchers can conveniently modulate the walking gait of the robot.
(3) The T-S fuzzy control system is used for mapping the environment information feedback quantity into an expected walking gait track of the robot, and compared with an optimization algorithm, different environment information can be more conveniently and rapidly mapped into the gait track, so that the walking environment adaptability of the robot is improved.
(4) The fuzzy control system designed by the invention needs the data set to be 3kAbout one, the number of data sets is smaller, and reduction is realizedThe difficulty of building a data set is addressed.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a link biped robot model in an embodiment;
FIG. 3 is a walking environment of the link robot designed in the embodiment;
FIG. 4 is an analysis diagram of walking environment in the embodiment;
FIG. 5 is a flowchart illustrating the method for obtaining environmental feedback information by the link robot according to an embodiment;
FIG. 6 is a schematic diagram of the link robot detecting a slope angle (left) and a degree of concavity and convexity (right) in the embodiment;
FIG. 7 is a schematic diagram illustrating division of membership functions corresponding to detection of a slope angle (left) and a degree of concavity and convexity (right) by a link robot in an embodiment;
FIG. 8 is a flow chart of the present invention for creating a fuzzy control data set;
FIG. 9 is a diagram illustrating correspondence between fuzzy rules and data sets in an embodiment;
FIG. 10 is a diagram illustrating the effect of fuzzy control on walking stride (left), average walking speed (middle), and foot raising height (right) established in the example;
fig. 11 shows a gait track adaptive walking result of the biped robot walking based on the fuzzy control in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1
The invention provides a biped robot gait track generation method based on hybrid control, which combines Fuzzy control and CPG bionic control (Fuzzy-CPG hybrid control), takes environment information fed back by real-time pose in the walking process of a robot as input quantity of a Fuzzy control system, obtains walking track information of the robot walking based on the Fuzzy control system, generates gait tracks based on the gait track information, and establishes a mapping relation between the environment feedback information and the gait tracks. The overall technical scheme of the method is shown in figure 1.
The real-time pose comprises pose information of the support foot and the swing foot in the walking process of the biped robot. The environmental information comprises a slope angle and a concave-convex height, and the walking road condition of the robot can be mapped. The gait track information includes walking stride, average walking speed and foot lifting height.
The fuzzy control system is constructed based on a T-S fuzzy control system, a trigonometric function is used as a membership function, a fuzzy rule is constructed according to a least square method, and deblurring is realized through a gravity center weighted average method. The fuzzy control system is mainly divided into the following 5 sub-steps:
a. determining fuzzy control system input and output variables
And taking the environment information feedback quantity as the input quantity of the fuzzy control system, and taking the walking gait track of the robot as the output quantity of the fuzzy control system.
b. Designing membership functions
The membership function is designed for each dimension input quantity of the fuzzy control system, the membership function adopted in the embodiment is a trigonometric function, and the corresponding membership division number is determined according to the change of the input quantity.
c. Building data sets
If there are k input quantities, the number of data sets needs to be about 3kThis number can be adjusted according to the degree of difficulty in acquiring the actual data set and the degree of variation in the input amount. The flow chart for establishing the data set is shown in fig. 8, and the walking parameters required by the robot are determined according to the input quantity (environment information) determined in the data set, so that the robot can normally walk in each environment.
d. Defining fuzzy rules
The invention constructs the fuzzy rule in the fuzzy system according to the least square method. The input field is divided into four parts, each part is represented by a first-order curved surface RSM, and the formula of the RSM of each part is as follows:
RSMi＝yi＝ai1x1+ai2x2+…+aikxk+ai(k+1)
wherein i is 1,2,3,4, yiFor the prediction obtained by the fuzzy control system, xkAs an input variable, aikCoefficients corresponding to first order surfaces.
Determining the coefficient of each first-order curved surface according to the established data set by using a least square method, and when the error epsilon is minimum, selecting the coefficient at the moment as the coefficient of the corresponding curved surface RSM, wherein the solving method of the coefficient of the first-order curved surface is as follows:
defining the true value of output quantity corresponding to input variable as Y, the true value Y and the predicted value Y of fuzzy systemiThe error between is epsilon, the following equation is satisfied:
Y＝XA+ε
wherein:
defining:
if the error ε is minimized, then L is minimized, i.e., satisfies the following equation:
the coefficients for obtaining the first order surface are:
wherein the content of the first and second substances,
to estimate the coefficients, X is the input variable matrix and Y is the true output matrix.
e. Deblurring
And obtaining the output variable of the fuzzy control system by a gravity center weighted average method.
The deblurring formula is:
wherein, yiPredicted values, w, for the fuzzy control systemiIn order to be the weight, the weight is,
n is the number of output quantities.
Example 2
The embodiment provides a walking control method of a biped robot, which adopts the gait track generation method of the biped robot described in embodiment 1 to generate gait tracks, controls the walking of the biped robot based on the gait tracks, and specifically maps the gait track information of each step in the gait tracks into the amplitude and phase angle of a CPG model to realize CPG bionic control.
The basic learning model of the CPG model is the following equation:
where η is the learning rate, ω is the fundamental angular velocity, and n represents the order harmonic.
The CPG model can complete the learning of the y' (t) periodic signal by the above formula, and the output of the model is as follows:
for the self-learning CPG model, the amplitude r in the above equation is determinednAnd phase angle phinThe output P of the system can be determined, and the phase phinDepending on the frequency ω, the frequency can be obtained from the walking stride and walking speed:
amplitude rnThe amplitude r of the CPG model can be obtained by fuzzy control according to the gait track information mapping, namely by determining the gait track informationnAnd phase angle phinAnd further controlling the CPG model to generate an adjustable output signal according to the expected gait track.
The present embodiment uses a biped link robot as an example to perform gait trajectory control. As shown in fig. 2, the bipedal link robot model has the following steps:
(1) determining walking environment of robot and walking track information of robot walking
a. Determining a robot walking environment
The walking environment of the connecting rod robot is designed as shown in figure 3, the slope of the terrain where the robot walks is changed to 0-10-5-12-0deg, a pit of-0.03 m is formed on a slope of 10deg, and a bump of 0.05m is formed on a slope of 12 deg.C, each section of the terrain has two parameters, namely a slope angle α and a bump height delta h, and as shown in figure 4, the bump is set to be parallel to the corresponding slope, if the two parameters are both 0, the terrain walks in a flat manner, if the slope angle is 0, the terrain is a flat land with unchanged bumps and pits, further if the bumps are connected, the terrain is a step terrain, and if the bump height delta h is 0, the bump is a flat slope.
b. Robot walking control
In this embodiment, the walking gait track information of the biped robot is mainly determined as the walking stride, the average walking speed and the foot lifting height, the walking control model is Fuzzy-CPG, that is, the mapping relationship between the walking gait track information and the CPG model parameters is established through Fuzzy control, and then the control of the link robot by a set of walking gait track information is realizedAnd (5) walking. Setting initial gait track parameter as walking stride Ds0.36m, average pace speed VavIs 1.16m, and the foot lifting height AmIs 0.15 m.
(2) Obtaining pose information
Since the link robot has no sensor, the pose information of the link robot, i.e., the position coordinates (x) of the robot swing foot and the support foot in the reference coordinate system, is mainly used in this embodimentswft1,yswft1)(xswft2,yswft2) And (x)stft1,yswft1)(xstft1,yswft1) As shown in fig. 6.
(3) Determining an amount of environmental information feedback
The specific environment information feedback quantity is determined through the pose information of the biped robot, the walking road surface condition of the robot can be reflected through the feedback quantity, the specific flow is shown in figure 5, and the slope angle and the concave-convex degree of the terrain where the robot is located are detected according to the flow.
a. Slope angle detection
Firstly, the slope angles α corresponding to the supporting legs and the free legs of the connecting rod robot are detectedswftAnd αstftThe calculation formula is as follows:
since the main concern is the corresponding slope angle at which the robot is about to reach the ground, α is definedslope＝αswft。
b. Bump height detection
Define the position of the start of the slope as xslopeThen, the height of the concave-convex is:
Δh＝[(yswft-ystft)-(xslope-xstft)*tanαstft-(xswft-xslope)*tanαswft]*cosαslope
wherein (x)swft,yswft) And (x)stft,yswft) Position coordinates of the robot swing foot and the support foot in a reference coordinate system, α, respectivelyslopeTo detect the resulting slope angle, it is shown in fig. 6. Since the slope angle and the height of the concave-convex are both small, GE is assumed to be BC.
If αswft≠αswftThe connecting rod robot is explained to be at the junction of the slope surface with one foot and the flat ground with the other foot, and the formula is assumed
If αswft＝αswftWhen the equation is 0, the link robot travels on the flat ground, and the equation is simplified to Δ h and yswft-ystft。
If αswft＝αswftNot equal to 0, the connecting rod robot walks on the slope, and the formula is simplified as follows:
Δh＝[(yswft-ystft)-(xswft-xstft)*tanαslope]*cosαslope
(4) design fuzzy control system
A fuzzy control system is designed, the fuzzy control system is established on the basis of a T-S fuzzy control system, a membership function is designed aiming at input quantity, a fuzzy rule is defined aiming at a fuzzy reasoning process, and finally, an output result is deblurred to obtain corresponding output quantity.
a. Determining fuzzy control system input and output variables
Amount of environmental information feedback (slope angle α)slopeAnd the degree of concavity and convexity Δ h) as the input quantity of the fuzzy control system, and the walking gait track (walking stride α) of the robotslopeAverage pace αslopeLifting height αslope) As a fuzzy control system output.
b. Designing membership functions
The input is the bank angle and unsmooth degree in this embodiment, the membership degree function to bank angle and unsmooth degree design is shown in fig. 7, because the bank angle compares unsmooth degree of change degree more, the design bank angle has 5 membership degrees, the language variable that corresponds is VS (very little), S (little), N (normal), B (big), VB (very big), unsmooth degree has 4 membership degrees, the language variable that corresponds is VS (very little), S (little), B (big), VB (very big).
c. Building data sets
In this embodiment, if the number k of input quantities is 2, the number of data sets needs to be about 9. Considering that the slope angle in the embodiment is more varied than the degree of concavity and convexity, finally 8 data sets are selected, and the data sets are determined as shown in table 1. Fig. 8 shows a flow chart for creating a data set, where num is 9, and the required walking parameters are determined according to the input amount determined in the data set.
TABLE 1
d. Defining fuzzy rules
In this embodiment, the number of input quantities is 2, and the input quantity is x1And x2The input field is divided into four parts, each part is composed of a first-order curved surface (RSM)1～RSM4) It is shown that the coefficients of each first order surface are 3, and the formula of RSM is as follows:
RSMi＝yi＝ai1x1+ai2x2+ai3
wherein y isiAnd i is a predicted value obtained by the fuzzy system, 2,3 and 4.
Determining the coefficients of each first order surface from the established data set using a least squares method as:
TABLE 2
With RSM4For example, RSM can be obtained from FIG. 9, Table 1 and Table 24If the corresponding data set is Run 4,5,7,8 in table 1, then Y and X in the above formula can be determined to be
Similarly, the expression of the first-order curved surface in this embodiment can be obtained as follows:
RSM1＝y1＝-0.1971x1-0.1011x2+0.36
RSM2＝y2＝-0.1971x1-0.7956x2+0.36
RSM3＝y3＝-0.2038x1-0.0610x2+0.3606
RSM4＝y4＝-0.1901x1-0.7558x2+0.3594
e. deblurring
Obtaining the output variable of the fuzzy control system by a gravity center weighted average method: system parameters of robot walking.
(5) Fuzzy control system mapping
Amount of environmental information feedback (slope angle α)slopeAnd the concave-convex degree delta h) as the input quantity of the fuzzy control system, and the walking gait track (walking stride D) of the robotsAverage pace speed VavHeight A of lifting feetm) And (4) as an output quantity of the fuzzy control system, obtaining a gait track adaptive to the environment according to the fuzzy control system established in the step (4). The mapping effect achieved by the fuzzy control system in this embodiment is shown in fig. 10.
If the feedback quantity of the environmental information is the slope angle αslope12deg, and the concave-convex degree delta h is 0.05m, the gait track information can be obtained as the walking stride Ds0.2818m, average pace speed Vav1.0667m and a foot lifting height Am0.1726 m.
(6) Environmentally adaptive walking
Inputting the expected gait track obtained in the step (5) into the step (1), controlling the robot to realize biped walking adaptive to the environment, wherein the walking effect of the robot is shown in fig. 11.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions that can be obtained by a person skilled in the art through logic analysis, reasoning or limited experiments based on the prior art according to the concept of the present invention should be within the protection scope determined by the present invention.
Claims (10)
1. A biped robot gait track generation method based on hybrid control is characterized in that fuzzy control and CPG bionic control are combined, environmental information fed back by a robot through a real-time pose in the walking process is used as input quantity of a fuzzy control system, walking gait track information of the robot is obtained based on the fuzzy control system, and a corresponding gait track is generated through a CPG model based on the gait track information.
2. The hybrid control-based biped robot gait trajectory generation method according to claim 1, characterized in that the environment information includes a slope angle and a concavo-convex height.
3. The hybrid control-based biped robot gait trajectory generation method of claim 1, wherein the gait trajectory information includes walking stride, average walking speed and foot lift height.
4. The gait track generation method of the biped robot based on hybrid control as claimed in claim 1, wherein the fuzzy control system is constructed based on a T-S fuzzy control system, a trigonometric function is used as a membership function, a fuzzy rule is constructed according to a least square method, and deblurring is realized by a gravity center weighted average method.
5. The gait trajectory generation method of a biped robot based on hybrid control according to claim 4, characterized in that in the fuzzy rule adopted in the fuzzy control system, the input field is divided into four parts, each part is represented by a first-order curved surface RSM, and the formula of the RSM of each part is as follows:
RSMi＝yi＝ai1x1+ai2x2+…+aikxk+ai(k+1)
wherein i is 1,2,3,4, yiFor the prediction obtained by the fuzzy control system, xkAs an input variable, aikCoefficients corresponding to first order surfaces.
6. The hybrid control-based biped robot gait trajectory generation method according to claim 5, characterized in that the coefficients of the first order surface are determined by the following formula:
wherein the content of the first and second substances,
to estimate the coefficients, X is the input variable matrix and Y is the true output matrix.
7. The hybrid control-based biped robot gait trajectory generation method according to claim 4, characterized in that the deblurring formula adopted in the fuzzy control system is:
wherein, yiPredicted values, w, for the fuzzy control systemiIs the weight, n is the number of output quantities.
8. The hybrid control-based biped robot gait trajectory generation method according to claim 4, characterized in thatIn that the fuzzy control system employs a data set size of 3kAnd +/-R, k is the input quantity number of the fuzzy control system, and R is a set integer.
9. A biped robot walking control method, characterized in that the method generates a gait trajectory by using the biped robot gait trajectory generation method according to claim 3, and controls the walking of the biped robot based on the gait trajectory.
10. The biped robot walking control method according to claim 9, wherein the gait track information of each step in the gait track is mapped to amplitude and phase angle of a CPG model to realize CPG bionic control.
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| CN113011458A (en) * | 2021-02-19 | 2021-06-22 | 华南理工大学 | Load-maneuvering exoskeleton human motion intention identification method and exoskeleton system |
| WO2022160811A1 (en) * | 2021-01-28 | 2022-08-04 | 歌尔股份有限公司 | Footed robot motion trajectory tracking method and device, and readable storage medium |
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