A friction identification method based on moss growth algorithm

Abstract

The present invention belongs to the field of robotics, and more specifically, is a friction identification method based on a moss growth algorithm. Step 1: Design a motion trajectory; Step 2: Execute the motion trajectory of step 1 in position control mode, collect robot speed and torque data, and extract the robot's friction torque; Step 3: Filter the collected friction torque; Step 4: Establish a coupling model of friction and Coriolis force; Step 5: Use the moss growth algorithm to identify the parameters of the friction and Coriolis force coupling model; Step 6: Eliminate the Coriolis force model to obtain the final friction model. The present invention avoids the problem of traditional genetic algorithm parameter identification easily falling into local optimal solutions, thereby improving identification efficiency and accuracy.

Description

Technical Field

[0001] The invention belongs to the field of robotics technology, and in particular to a friction identification method based on a moss growth algorithm. Background Art

[0002] In the Fourth Industrial Revolution, where artificial intelligence and machine automation are at the core, robots occupy a crucial strategic position. The application of robots in manufacturing, particularly in human-robot collaboration scenarios, poses further challenges in collecting or estimating the forces involved in interactions between robots and humans. Due to the difficulty and cost of integrating sensors, sensorless force estimation methods are sometimes used to estimate the forces involved in interactions between robots and the physical world. However, these force estimation methods are often affected by the uncertainty of robot friction, necessitating accurate friction modeling. The selection and establishment of mathematical friction models and the identification of their parameters are key technologies in robot friction modeling. However, current friction modeling suffers from the following technical challenges: the traditional Strbeck friction model exhibits discontinuities at zero crossings, making the resulting discontinuous voltage difficult to support in servo mechanisms and impacting the actual implementation of control signals. Furthermore, the coupling of Coriolis forces associated with uniform reciprocating motion of a single joint at high speeds is difficult to separate during friction force extraction. Linear least squares methods struggle to identify nonlinear friction models, and traditional genetic algorithms are prone to falling into local optima. Summary of the Invention

[0003] To solve the above problems, the present invention provides a friction identification method based on the moss growth algorithm, which can improve the continuity of the friction model, avoid the problem of easily falling into the local optimal solution during the traditional genetic algorithm parameter identification process, and improve the identification efficiency and accuracy.

[0004] The technical solution of the present invention is described as follows in conjunction with the accompanying drawings:

[0005] A friction identification method based on a moss growth algorithm comprises the following steps:

[0006] Step 1: Design a motion trajectory;

[0007] Step 2: Execute the motion trajectory of step 1 in position control mode, collect the speed and torque data of the robot, and extract the friction torque of the robot;

[0008] Step 3: Filter the collected friction torque;

[0009] Step 4: Establish a coupling model of friction and Coriolis force;

[0010] Step 5: Use the moss growth algorithm to identify the parameters of the friction and Coriolis force coupling model;

[0011] Step 6: Eliminate the Coriolis force model to obtain the final friction model.

[0012] Furthermore, the specific method of step one is as follows:

[0013] 11) For any joint i of the robot, make any joint perform a single-joint uniform reciprocating motion; that is, lock the other joints and make only joint i rotate at a uniform speed;

[0014] 12) The speed range for uniform motion is set to include low speed and high speed ranges. The low speed range should have more speed sampling points than the high speed range. The joint motion range should be based on fully stimulating the robot's motion characteristics and not exceeding the robot's joint motion limit, and move in both clockwise and counterclockwise directions.

[0015] Furthermore, the specific method of step 2 is as follows:

[0016] 21) The robot's speed and torque data are collected from the motor encoder position differential and the motor torque output current sampling, and converted into motor output torque using the following formula:

[0017] τ m =I m K t N m (1)

[0018] Where, τ m is the equivalent output torque of the motor; I m is the motor sampling current; K t is the torque constant of the servo motor; N m is the single joint transmission ratio;

[0019] 22) Calculate the friction torque of the robot and extract it using the following formula:

[0020]

[0021] Where, τ m1 and τ m2 are the joint torques collected for the clockwise and counterclockwise motions of a single joint of the robot; τ f is the extracted friction torque; is the joint angular velocity of the robot.

[0022] Furthermore, the specific method of step three is as follows:

[0023] The collected friction torque is filtered using Butterworth filtering.

[0024] Furthermore, the specific method of step 4 is as follows:

[0025] The coupling model of friction and Coriolis force is established as follows:

[0026]

[0027] Where, It is the stribeck phenomenon; It is the Coulomb phenomenon; It is a viscosity phenomenon; is the Coriolis force coupling term; where f s1 、f s2 、f s3 、f c1 、f c2 、f v 、f c3 is a diagonal matrix with positive parameters.

[0028] Furthermore, the specific method of step five is as follows:

[0029] 51) The evolutionary direction of a population is determined by a mechanism called wind direction determination, which uses a method to divide the population;

[0030] 52) Two new reproduction strategies, spore dispersal and double reproduction, are proposed for exploration and development.

[0031] 53) The cryptogenetic mechanism is used to modify individual solutions to avoid the algorithm falling into local optimality.

[0032] Furthermore, the specific method of step 51) is as follows:

[0033] The wind direction determination mechanism expression is given by the following algorithm:

[0034]

[0035] Where M best The best moss individual in population X is the jth dimension value of all individuals in the population and the number of mosses is M. best Compare the j-th dimension value and divide the population into DX j1 and DX j2 Two parts:

[0036] And compare the largest number of sets, expressed as follows:

[0037]

[0038] Where, count is the number of moss individuals;

[0039] After multiple splits, the resulting set is:

[0040]

[0041] Where, d n For the number of times to be divided, take d n = dim / 4 and d n Not less than 1; p j is the jth random number, ranging from (1, 2, ..., dim), and conforms to the equation:

[0042]

[0043] The wind direction always tends to blow from divX to M best Region, define wind direction as:

[0044]

[0045] Where dirX is defined as the individual pointer M in divX. best The set of vectors is expressed as follows:

[0046] dirX={M best -M i |M i ∈divX}, (9).

[0047] Furthermore, the specific method of step 52) is as follows:

[0048] The spore diffusion search for spore diffusion reproduction is given by the following expression:

[0049]

[0050] Where, is the new moss individual obtained by the diffusion of the i-th moss; r1 is a random number in the range of (0,1); d1 is a constant parameter, set to 0.2;

[0051] Spores travel a long distance in steady wind conditions and a short distance in turbulent wind conditions. The step lengths are given by the following formulas:

[0052] step1=w·(r2-0.5)·E, (11)

[0053]

[0054] Where r2 and r3 are random numbers between (0, 1); E is the wind intensity; and β is the ratio of the number of individuals in divX to the total number of individuals in X, which are given by the following formulas:

[0055]

[0056] Where FEs is the current number of iterations; MaxFEs is the maximum number of iterations;

[0057] The bidirectional propagation of double reproduction is given by the following expression:

[0058]

[0059] Where, is the i-th new individual; for The jth particle in ; j is a random integer not exceeding the maximum dimension of the individual; r4 is a random number in the range (0,1); d2 is a constant, set to 0.5; act and step3 are given by the following expressions:

[0060]

[0061] step3=0.1·(r6-0.5)·E, (17)

[0062] In the formula, r5 is a random number (0,1) and M best Same dimensions.

[0063] Furthermore, the specific method of 53) is as follows:

[0064] Every ten mutations of a moss individual Mi triggers the cryptobiotic search mechanism, searching for the best individual among the historically mutated moss individuals. Finally, the amplitude of the best individual obtained is given to the moss individual Mi, and subsequent operations are continued.

[0065] Among them, create moss individuals, the dimension is equal to the parameter matrix f s1 、f s2 、f s3 、f c1 、f c2 、f v 、f c3 Dimension, the dimension is 7, and the fitness function is defined as:

[0066]

[0067] Where S is the fitness value of moss individual; L fi is the actual value of the coupling between the friction torque and the Coriolis force of the i-th group; MGO stbi is the estimated value of the model of group i; n is the number of speed sampling groups;

[0068] Furthermore, the specific method of step six is ​​as follows:

[0069] 62) Eliminating the Coriolis force model is expressed as setting q = 0 in formula (3), thus obtaining a pure friction model.

[0070] The beneficial effects of the present invention are:

[0071] 1) In terms of robot friction modeling, the present invention conducts targeted modeling and identification based on the different friction characteristics of the robot in different speed ranges, solving the problem of discontinuity of the stribeck friction model at zero crossing and the coupling of Coriolis force and inertial force at high speed, which causes errors in friction force acquisition.

[0072] 2) In terms of robot friction model identification, this invention addresses the nonlinear characteristics of robot friction models. Linear identification methods cannot accurately obtain model parameters. Traditional identification methods, such as genetic algorithms, suffer from slow identification speeds and a tendency to get stuck in local optimal solutions. Applying the moss growth optimization algorithm to friction identification improves identification efficiency and avoids this problem of getting stuck in local optimal solutions during the identification process. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0074] Figure 1 It is the overall flow chart of the present invention;

[0075] Figure 2 Flowchart of cryptobiotic mechanism for moss growth algorithm;

[0076] Figure 3 Flowchart of the moss growth algorithm;

[0077] Figure 4 This is the effect diagram of robot friction model identification. DETAILED DESCRIPTION

[0078] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0079] Example 1

[0080] See Figure 1 This embodiment provides a friction identification method based on a moss growth algorithm, comprising the following steps:

[0081] Step 1: Design a motion trajectory, as follows:

[0082] 11) For any joint i of the robot, make any joint perform a single-joint uniform reciprocating motion; that is, lock the other joints and make only joint i rotate at a uniform speed;

[0083] 12) The speed range of uniform motion should be as comprehensive as possible, including both low-speed and high-speed ranges. More speed sampling points should be set in the low-speed range. The joint motion range should be based on fully stimulating the robot's motion characteristics and not exceed the robot's joint motion limit, and move in both clockwise and counterclockwise directions.

[0084] Step 2: Execute the motion trajectory of step 1 in position control mode, collect the robot's speed and torque data, and extract the robot's friction torque, as follows:

[0085] 21) The robot's speed and torque data are collected from the motor encoder position differential and the motor torque output current sampling, and converted into motor output torque using the following formula:

[0086] τ m =I m K t N m (1)

[0087] Where, τ m is the equivalent output torque of the motor; I m is the motor sampling current; K t is the torque constant of the servo motor; N m is the single joint transmission ratio;

[0088] 22) Calculate the friction torque of the robot and extract it using the following formula:

[0089]

[0090] Where, τ m1 and τ m2 are the joint torques collected for the clockwise and counterclockwise motions of a single joint of the robot; τ f is the extracted friction torque; is the joint angular velocity of the robot.

[0091] Step 3: Filter the collected friction torque as follows:

[0092] The collected friction torque is filtered using Butterworth filtering.

[0093] Step 4: Establish a coupling model of friction and Coriolis force, as follows:

[0094] The coupling model of friction and Coriolis force is established as follows:

[0095]

[0096] Where, It is the stribeck phenomenon; It is the Coulomb phenomenon; It is a viscosity phenomenon; is the Coriolis force coupling term; where f s1 、f s2 、f s3 、f c1 、f c2 、f v 、f c3 is a diagonal matrix with positive parameters.

[0097] Step 5: Refer to Figure 3 , using the moss growth algorithm to identify the parameters of the friction and Coriolis force coupling model; the moss growth algorithm is a metaheuristic algorithm that first determines the evolutionary direction of the population through a mechanism called wind direction determination, which uses a method to divide the population. Furthermore, drawing on the experience of sexual and asexual reproduction in mosses, two new reproduction strategies, spore diffusion reproduction and double reproduction, are proposed for exploration and development. Finally, the cryptobacterial mechanism changes the traditional metaheuristic algorithm's method of directly modifying individual solutions, avoiding the algorithm from falling into local optimality, as follows:

[0098] 51) The evolutionary direction of a population is determined by a mechanism called wind direction determination, which uses a method to divide the population;

[0099] The wind direction determination mechanism expression is given by the following algorithm:

[0100]

[0101] Where M best The best moss individual in population X is the jth dimension value of all individuals in the population and the number of mosses is M. best Compare the j-th dimension value and divide the population into DX j1 and DX j2 Two parts:

[0102] And compare the largest number of sets, expressed as follows:

[0103]

[0104] Where, count is the number of moss individuals;

[0105] After multiple splits, the resulting set is:

[0106]

[0107] Where, d n For the number of times to be divided, take d n = dim / 4 and d n Not less than 1; p j is the jth random number, ranging from (1, 2, ..., dim), and conforms to the equation:

[0108]

[0109] The wind direction always tends to blow from divX to M best Region, define wind direction as:

[0110]

[0111] Where dirX is defined as the individual pointer M in divX. best The set of vectors is expressed as follows:

[0112] dirX={M best -M i |M i ∈divX}, (9).

[0113] 52) Two new reproduction strategies, spore dispersal and double reproduction, are proposed for exploration and development.

[0114] The spore diffusion search for spore diffusion reproduction is given by the following expression:

[0115]

[0116] Where, is the new moss individual obtained by the diffusion of the i-th moss; r1 is a random number in the range of (0,1); d1 is a constant parameter, set to 0.2;

[0117] Spores travel a long distance in steady wind conditions and a short distance in turbulent wind conditions. The step lengths are given by the following formulas:

[0118] step1=w·(r2-0.5)·E, (11)

[0119]

[0120] Where r2 and r3 are random numbers between (0, 1); E is the wind intensity; and β is the ratio of the number of individuals in divX to the total number of individuals in X, which are given by the following formulas:

[0121]

[0122] Where FEs is the current number of iterations; MaxFEs is the maximum number of iterations;

[0123] The bidirectional propagation of double reproduction is given by the following expression:

[0124]

[0125] Where, is the i-th new individual; for The jth particle in ; j is a random integer not exceeding the maximum dimension of the individual; r4 is a random number in the range (0,1); d2 is a constant, set to 0.5; act and step3 are given by the following expressions:

[0126]

[0127] step3=0.1·(r6-0.5)·E, (17)

[0128] In the formula, r5 is a random number (0,1) and M best Same dimensions.

[0129] 53) The cryptogenetic mechanism is used to modify individual solutions, which prevents the algorithm from falling into local optimality;

[0130] Every ten mutations of a moss individual Mi triggers the cryptobiotic search mechanism, searching for the best individual among the historically mutated moss individuals. Finally, the amplitude of the best individual obtained is given to the moss individual Mi, and subsequent operations are continued.

[0131] Figure 2 This is a flowchart of the specific algorithm flow, where record represents the number of iterations of the record, Mi is the i-th individual of the moss population, rMi_record represents the Mi mutant individuals of the record-th generation, rec_num is the maximum value of record, T is the maximum number of iterations, and rMi_best is the best generation of individuals in this cryptobiotic mechanism search.

[0132] Among them, create moss individuals, the dimension is equal to the parameter matrix f s1 、f s2 、f s3 、f c1 、f c2 、f v 、f c3 Dimension, the dimension is 7, and the fitness function is defined as:

[0133]

[0134] Where S is the fitness value of moss individual; L fi is the actual value of the coupling between the friction torque and the Coriolis force of the i-th group; MGO stbi is the estimated value of the model of group i; n is the number of speed sampling groups;

[0135] Step 6: Eliminate the Coriolis force model to obtain the final friction model, as follows:

[0136] Eliminating the Coriolis force modeling is expressed as setting q=0 in formula (3), thus obtaining a pure friction model.

[0137] Example 2

[0138] This embodiment experimentally verifies the robot friction model parameter identification method based on the improved moss growth algorithm proposed in the first embodiment.

[0139] The robot collects position, velocity, and torque information of a single joint at a speed of -2rad / s to 2rad / s. The parameter identification effect is as follows: Figure 4 shown.

[0140] The above experimental results show that the friction identification method based on the moss growth algorithm proposed in the present invention can effectively identify the friction model of the robot, and the model has a good fitting effect.

[0141] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A friction identification method based on moss growth algorithm, characterized in that: The following steps are involved: Step 1: Design a motion trajectory; Step 2: Execute the motion trajectory of step 1 in position control mode, collect the speed and torque data of the robot, and extract the friction torque of the robot; Step 3: Filter the collected friction torque; Step 4: Establish a coupling model of friction and Coriolis force; Step 5: Use the moss growth algorithm to identify the parameters of the friction and Coriolis force coupling model; Step 6: Eliminate the Coriolis force model to obtain the final friction model; The specific method of step 4 is as follows: The coupling model of friction and Coriolis force is established as follows: Where, It is the stribeck phenomenon; It is the Coulomb phenomenon; It is a viscosity phenomenon; is the Coriolis force coupling term; where f s1 、f s2 、f s3 、f c1 、f c2 、f v 、f c3 is a diagonal matrix with positive parameters; The specific method of step five is as follows: 51) The evolutionary direction of the population is determined by a mechanism called wind direction determination, which uses a method to divide the population as follows: The wind direction determination mechanism expression is given by the following algorithm: Where M best The best moss individual in population X is the jth dimension value of all individuals in the population and the number of mosses is M. best Compare the j-th dimension value and divide the population into DX j1 and DX j2 Two parts: And compare the largest number of sets, expressed as follows: Where, count is the number of moss individuals; After multiple splits, the resulting set is: Where, d n For the number of times to be divided, take d n = dim / 4 and d n Not less than 1; p j is the jth random number, ranging from (1, 2, ..., dim), and conforms to the equation: The wind direction always tends to blow from divX to M best Region, define wind direction as: Where dirX is defined as the individual pointer M in divX. best The set of vectors is expressed as follows: dirX={M best -M i |M i ∈divX}, (9) 52) Two new reproduction strategies, spore diffusion reproduction and double reproduction, are proposed for exploration and development, as follows: The spore diffusion search for spore diffusion reproduction is given by the following expression: Where, is the new moss individual obtained by the diffusion of the i-th moss; r1 is a random number in the range of (0,1); d1 is a constant parameter, set to 0.2; Spores travel a long distance in steady wind conditions and a short distance in turbulent wind conditions. The step lengths are given by the following formulas: step1=w·(r2-0.5)·E, (11) Where r2 and r3 are random numbers between (0, 1); E is the wind intensity; and β is the ratio of the number of individuals in divX to the total number of individuals in X, which are given by the following formulas: Where FEs is the current number of iterations; MaxFEs is the maximum number of iterations; The bidirectional propagation of double reproduction is given by the following expression: Where, is the i-th new individual; for The jth particle in ; j is a random integer not exceeding the maximum dimension of the individual; r4 is a random number in the range (0,1); d2 is a constant, set to 0.5; act and step3 are given by the following expressions: step3=0.1·(r6-0.5)·E, (17) In the formula, r5 is a random number (0,1) and M best The dimensions are the same; 53) The cryptogenetic mechanism is used to modify individual solutions to avoid the algorithm falling into local optimality, as follows: Every ten mutations of a moss individual Mi triggers the cryptobiotic search mechanism, searching for the best individual among the historically mutated moss individuals. Finally, the amplitude of the best individual is given to the moss individual Mi, and subsequent operations are continued; Among them, create moss individuals, the dimension is equal to the parameter matrix f s1 、f s2 、f s3 、f c1 、f c2 、f v 、f c3 Dimension, the dimension is 7, and the fitness function is defined as: Where S is the fitness value of moss individual; L fi is the actual value of the coupling between the friction torque and the Coriolis force of the i-th group; MGO stbi is the estimated value of the model of group i; n is the number of speed sampling groups.

2. The friction identification method based on moss growth algorithm according to claim 1, characterized in that: The specific method of step one is as follows: 11) For any joint i of the robot, make any joint perform a single-joint uniform reciprocating motion; that is, lock the other joints and make only joint i rotate at a uniform speed; 12) The speed range for uniform motion is set to include low speed and high speed ranges. The low speed range should have more speed sampling points than the high speed range. The joint motion range should be based on fully stimulating the robot's motion characteristics and not exceeding the robot's joint motion limit, and move in both clockwise and counterclockwise directions.

3. The friction identification method based on moss growth algorithm according to claim 1, characterized in that: The specific method of step 2 is as follows: 21) The robot's speed and torque data are collected from the motor encoder position differential and the motor torque output current sampling, and converted into motor output torque using the following formula: t m =I m K t N m (1) Where, τ m is the equivalent output torque of the motor; I m is the motor sampling current; K t is the torque constant of the servo motor; N m is the single joint transmission ratio; 22) Calculate the friction torque of the robot and extract it using the following formula: Where, τ m1 and τ m2 are the joint torques collected for the clockwise and counterclockwise motions of a single joint of the robot; τ f is the extracted friction torque; is the joint angular velocity of the robot.

4. The friction identification method based on moss growth algorithm according to claim 1, characterized in that: The specific method of step three is as follows: The collected friction torque is filtered using Butterworth filtering.

5. The friction identification method based on moss growth algorithm according to claim 1, characterized in that: The specific method of step six is ​​as follows: Eliminating the Coriolis force modeling is expressed as setting q=0 in formula (3), thus obtaining a pure friction model.

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