CN112091950A - Robot kinematic parameter identification method based on hybrid genetic simulated annealing algorithm - Google Patents
Robot kinematic parameter identification method based on hybrid genetic simulated annealing algorithm Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/02—Programme-controlled manipulators characterised by movement of the arms, e.g. cartesian coordinate type
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J17/00—Joints
- B25J17/02—Wrist joints
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
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Abstract
The invention discloses a robot kinematics parameter identification method based on a hybrid genetic simulated annealing algorithm, which comprises the steps of constructing a kinematics model of a robot and an error model of the robot, mixing the two performance advantages of the genetic algorithm and the simulated annealing algorithm for use, and realizing accurate identification of model errors by utilizing the global search capability of the genetic algorithm and the local search capability of the simulated annealing algorithm so as to correct various parameters of the robot model and improve the absolute positioning accuracy of the tail end of the robot; the optimal solution obtained by the genetic algorithm is directly input into the simulated annealing algorithm, so that the method has global optimization capability, can jump out the local optimal solution, and accurately identifies the error model parameters.
Description
Technical Field
The invention relates to the technical field of robot kinematics calibration, in particular to a robot kinematics parameter identification method based on a hybrid genetic simulated annealing algorithm.
Background
With the application and development of the robot offline programming technology, the robot calibration technology is regarded as one of the key technologies for the practicability of the offline programming technology, and is paid more and more attention and researched by many researchers. According to the calibration process of the robot, a proper kinematic model and a proper calibration measurement method are selected, which is the premise of robot calibration, and on the basis, calibration data are processed to realize error parameter identification and correction, which is the aim of robot calibration.
The robot kinematics modeling method is various, different kinematics models have great influence on the accuracy of the tail end of the robot, the more parameters describing the kinematics models are, the more accurate the established kinematics models are, but more redundant parameters can be introduced into the models, and the kinematics calculation and the kinematics parameter identification of the robot are not facilitated.
In practical situations, the relationship between the robot end error and the kinematic parameter error is non-linear, but the kinematic parameter identification is usually performed based on a linear error model, so that the identification deviation is caused. Second-order error terms of the parameters are mostly ignored, but the influence can be reduced through multiple iterations, and high identification precision can be achieved. The most common method for identifying kinematic parameters is a least square method, disturbance information does not need to be considered in the method, but the number of equations is required to be larger than the number of identification parameters, so that more measurement data points and larger calculated amount are required, a reasonable track needs to be designed, and certain limitation is realized; the Meigao Ming and the like use a genetic algorithm to identify kinematic parameters, but the genetic algorithm is easy to fall into local optimization; although the simulated annealing algorithm can jump out the local optimal solution for global optimization, the requirement on the initial solution is high, and if the initial solution is not good, a long time is needed for identification to obtain accurate parameters.
Disclosure of Invention
In order to overcome the defects and shortcomings in the prior art, the invention provides a robot kinematic parameter identification method based on a hybrid genetic simulation annealing algorithm, which is used for identifying a robot kinematic error model, optimizing in the global range of solution, jumping out of the local optimal solution and shortening the calculation time.
The second purpose of the invention is to provide a robot kinematic parameter identification system based on a hybrid genetic simulated annealing algorithm.
A third object of the present invention is to provide a storage medium.
It is a fourth object of the invention to provide a computing device.
In order to achieve the purpose, the invention adopts the following technical scheme:
a robot kinematic parameter identification method based on a hybrid genetic simulated annealing algorithm comprises the following steps:
constructing a kinematic model of the robot and an error model of the robot;
setting the size of a population, and randomly generating an initial population;
calculating the fitness of each individual in the population by taking the reciprocal of the x, y and z error square sum of the actual position and the theoretical position of the tail end of the robot as a fitness function;
outputting the optimal individual as an initial solution of a simulated annealing algorithm by adopting a fitness function;
initializing parameters of a simulated annealing algorithm, and calculating the current fitness;
randomly generating a next solution in the neighborhood of the current solution, and calculating the fitness of the next solution;
calculating the difference between the fitness of the next solution and the fitness of the current solution, if the difference is greater than zero, accepting the new solution as the current solution according to the Metropolis criterion, and otherwise, directly accepting the new solution as the current solution;
and if the internal loop iteration times are not reached, the fitness of the next solution is calculated, the set multiple of the current temperature is taken as the next temperature to reduce the temperature when the iteration times are reached, the lowest temperature is reached, the optimal solution is output, the parameter identification is completed, and each error item is obtained.
As a preferred technical solution, after the fitness of each individual in the population is calculated, the method further includes the steps of binary coding, population evolution and binary decoding, and specifically includes:
carrying out binary coding on each individual in the population;
setting selection probability, crossover probability and variation probability, firstly carrying out selection operation on chromosomes after binary coding, then carrying out single-point crossover operation on the selected chromosomes, and then carrying out single-point bitwise negation operation on the crossed chromosomes to obtain an evolved population;
and judging whether the maximum iterative evolution times are reached, calculating the fitness of each individual in the population if the maximum iterative evolution times are not reached, and performing binary decoding on each chromosome in the result population if the maximum iterative evolution times are reached.
As a preferable technical solution, the number of iterations is reduced by taking a set multiple of the current temperature as the next temperature, and the set multiple is set to 0.9.
According to the preferable technical scheme, the robot adopts a six-degree-of-freedom robot, and the kinematic model of the robot adopts an MD-H kinematic model.
As a preferred technical solution, the step of establishing the joint axis reference coordinate system by the MD-H kinematic model includes:
determining the direction of a z axis, the origin of a coordinate system and the direction of an x axis;
determining the z-axis direction: a coordinate system established at the joint i is named as a coordinate system i-1, and if the joint i is a rotary shaft joint, the direction of a z axis is consistent with the axis of a rotary shaft of the joint; if the joint i is a moving joint, the moving direction of the joint i is determined as the direction of the z-axis;
determining the origin and the x-axis direction of a coordinate system:
when two adjacent joint axes zi-1And ziWhen the straight line is a non-coplanar straight line, there is only one shortest common perpendicular line between the two axes, and the direction of the x axis is the shortest common perpendicular line from zi-1Direction ziThe origin of the coordinate system is the intersection point of the z-axis and the x-axis;
when two adjacent joint axes zi-1And ziWhen the joint coordinate system is parallel lines, a plurality of common perpendicular lines with the same length exist between the two axes, the common perpendicular line intersected with the original point of the previous joint coordinate system is selected as a shortest common perpendicular line, and the direction of the x axis is the shortest common perpendicular line from zi-1Direction ziThe origin of the coordinate system is the intersection point of the z-axis and the x-axis;
when two adjacent joint axes zi-1And ziWhen the two axes are perpendicular to each other, the normal of the plane where the two axes are located is selected as a common perpendicular line, the direction of the x axis is the direction of the common perpendicular line, and the origin of a coordinate system is the intersection of the common perpendicular line and the two axesPoint;
determining the direction of the y-axis: the y-axis direction is determined by the right hand rule.
Preferably, the MD-H kinematic model adds a rotation beta around a y axis on each joint coordinate system in a classical D-H model, and uses the beta when two adjacent joint axes are paralleliSubstituted by diAt this time diIs zero; when the adjacent two joint axes are not parallel, define betaiIs zero;
constructing a homogeneous transformation matrix of an adjacent joint reference coordinate system as follows:
and obtaining an error model of the robot according to the homogeneous transformation matrix of each joint, wherein the error model is expressed as:
PG-P＝MθΔθ+MαΔα+MaΔa+MdΔd+MβΔβ
Δθ＝[Δθ1,Δθ2,…,Δθ6]'
Δα＝[Δα1,Δα2,…,Δα6]'
Δa＝[Δa1,Δa2,…,Δa6]'
Δd＝[Δd1,Δd2,…,Δd6]'
Δβ＝Δβ3
wherein c represents cos (), s represents sin (), and PGRepresenting the actual position of the robot end in a base coordinate system established under the laser tracker, P representing the theoretical position of the end determined from the kinematic model of the robot and the joint angles, Mθ、Mα、Ma、Md、MβCoefficient matrices Δ θ, Δ α, Δ a, Δ d, Δ β, respectively.
In order to achieve the second object, the present invention adopts the following technical solutions:
a robot kinematic parameter identification system based on a hybrid genetic simulated annealing algorithm comprises: the system comprises an error model construction module, an initial population construction module, a population individual fitness calculation module, an initial solution construction module, a fitness calculation module, a difference value calculation judgment module and an internal loop iteration number judgment module;
the error model building module is used for building a kinematic model of the robot and an error model of the robot;
the initial population building module is used for setting the size of a population and randomly generating an initial population;
the population individual fitness calculation module is used for calculating the fitness of each individual in the population by taking the reciprocal of the x, y and z error square sum of the actual position and the theoretical position of the tail end of the robot as a fitness function;
the initial deconstruction modeling block is used for outputting an optimal individual as an initial solution of the simulated annealing algorithm by adopting a fitness function;
the fitness calculation module is used for initializing parameters of a simulated annealing algorithm and calculating the current fitness;
randomly generating a next solution in the neighborhood of the current solution, and calculating the fitness of the next solution;
the difference value calculation and judgment module is used for calculating the difference value between the fitness of the next solution and the fitness of the current solution, if the difference value is larger than zero, the new solution is accepted as the current solution according to the Metropolis criterion, and if the difference value is not larger than zero, the new solution is directly accepted as the current solution;
the internal loop iteration frequency judging module is used for judging whether the internal loop iteration frequency is reached, if the internal loop iteration frequency is not reached, the calculation of the fitness of the next solution is carried out, the iteration frequency is reached, the set multiple of the current temperature is taken as the next temperature to be cooled, the lowest temperature is reached, the optimal solution is output, parameter identification is completed, and each error item is obtained.
In order to achieve the third object, the present invention adopts the following technical solutions:
a storage medium storing a program which, when executed by a processor, implements the above-described hybrid genetic simulated annealing algorithm-based robot kinematic parameter identification method.
In order to achieve the fourth object, the present invention adopts the following technical means:
a computing device comprises a processor and a memory for storing a program executable by the processor, wherein when the processor executes the program stored in the memory, the robot kinematic parameter identification method based on the hybrid genetic simulation annealing algorithm is realized.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the method utilizes the performance advantages of the genetic algorithm and the simulated annealing algorithm to be mixed and used, and utilizes the global search capability of the genetic algorithm and the local search capability of the simulated annealing algorithm to realize the accurate identification of the model error so as to correct all parameters of the robot model and improve the absolute positioning accuracy of the tail end of the robot; the optimal solution obtained by the genetic algorithm is directly input into the simulated annealing algorithm, so that the method has global optimization capability, can jump out the local optimal solution, and accurately identifies the error model parameters.
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Fig. 1 is a schematic flow chart of a robot kinematic parameter identification method based on a hybrid genetic simulated annealing algorithm according to the embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further 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 intended to limit the invention.
Examples
The embodiment provides a robot kinematic parameter identification method based on a hybrid genetic simulated annealing algorithm, which comprises the following steps:
firstly, establishing an MD-H kinematic model of a six-degree-of-freedom robot, wherein the establishment of a coordinate system is the selection of coordinate axis directions and an origin, and the D-H model establishes a joint axis reference coordinate system by the following method:
(1) establishment of Z-axis direction
A coordinate system established at the joint i is named as a coordinate system i-1, and if the joint i is a rotary shaft joint, the direction of a z axis is consistent with the axis of a rotary shaft of the joint; if the joint i is a moving joint, the moving direction of the joint i is determined as the direction of the z-axis;
(2) establishment of coordinate system origin and x-axis direction
Three geometrical relationships between the axes of two adjacent joints can occur: the non-coplanar straight lines and the parallel lines are mutually vertical. Therefore, the origin and the x-axis direction of the coordinate system are considered in three cases;
when two adjacent joint axes zi-1And ziWhen the straight line is a non-coplanar straight line, there is only one shortest common perpendicular line between the two axes, and the direction of the x axis is the shortest common perpendicular line from zi-1Direction ziThe origin of the coordinate system is the intersection of the z-axis and the x-axis.
When two adjacent joint axes zi-1And ziWhen the joint coordinate system is parallel lines, innumerable common perpendicular lines with the same length exist between the two axes, the common perpendicular line intersected with the original point of the previous joint coordinate system is selected as a shortest common perpendicular line, and the direction of the x axis is the shortest common perpendicular line from zi-1Direction ziThe origin of the coordinate system is the intersection of the z-axis and the x-axis.
When two adjacent joint axes zi-1And ziWhen the two axes are perpendicular to each other, the normal of the plane where the two axes are located is selected as a common perpendicular line, the direction of the x axis is the direction of the common perpendicular line, and the origin of the coordinate system is the intersection point of the common perpendicular line and the two axes.
The direction of the y-axis is determined by the right hand rule.
A joint axis reference coordinate system is established by the method, and 4 parameters for describing the model are arranged between the joint i-1 and the joint i: alpha is alphai、θi、aiAnd di；αiIs the angle of twist, i.e. zi-1Axis and ziAngle of axis thetaiIs the angle of articulation, i.e. xi-1Axis and xiAngle of axis aiIs the length of the connecting rod, zi-1Axis and ziCommon perpendicular to the axis, diIs the pi offset representation xi-1Axial direction ziThe shaft being moved to xiDistance of the shaft.
Then, the joint axis i-1 is referenced to the seatThe transformation of the reference system into the reference coordinate system of the joint axis i can be described as follows: the coordinate system i-1 winds around z firsti-1Axis of rotation thetaiThen along zi-1Axial translation diThen along xi-1Axial translation aiFinally wound around xi-1Rotation of the shaft alphai. The MD-H model adds a rotation beta around a y axis on each joint coordinate system in the classical D-H model, and uses the beta when two adjacent joint axes are paralleliSubstituted by diAt this time diIs zero; when the adjacent two joint axes are not parallel, define betaiIs zero. At this time, the homogeneous transformation matrix of the reference coordinate systems of the adjacent joints is as follows:
wherein c represents cos () and s represents sin ().
The error model of the robot obtained according to the homogeneous transformation matrix at each joint is shown in the following formula:
PG-P＝MθΔθ+MαΔα+MaΔa+MdΔd+MβΔβ
PGdenotes an actual position of a base coordinate system established by a robot tip under a laser tracker, P denotes a tip theoretical position obtained from a robot kinematic model and a joint angle, and Δ θ ═ Δ θ1,Δθ2,…,Δθ6]'，Δα＝[Δα1,Δα2,…,Δα6]'，Δa＝[Δa1,Δa2,…,Δa6]'，Δd＝[Δd1,Δd2,…,Δd6]'，Δβ＝Δβ3All are the errors of the parameters to be identified of the robot MD-H model, Mθ，Mα，Ma，Md，MβCoefficient matrixes of delta theta, delta alpha, delta a, delta d and delta beta respectively, and calibrating the robot error model, namely solving the coefficient matrixes of delta theta, delta alpha, delta a, delta d and delta beta;
secondly, performing parameter identification on the above errors by using a hybrid genetic simulated annealing algorithm, as shown in fig. 1, and describing the implementation steps of the algorithm in detail as follows:
step 1: taking the 25 parameter errors as individuals of a population in the genetic algorithm, setting the size of the population to be 50, and randomly initializing the population;
step 2: calculating the fitness of each individual in the population by taking the reciprocal of the x, y and z error square sum of the actual position and the theoretical position of the tail end of the robot as a fitness function;
and step 3: carrying out binary coding on each individual in the population;
and 4, step 4: setting the selection probability to be 0.5, the cross probability to be 0.8 and the mutation probability to be 0.1, firstly carrying out selection operation on the chromosomes after binary coding by using a roulette mode, then carrying out single-point cross operation on the selected chromosomes, and then carrying out single-point bitwise negation operation on the crossed chromosomes to obtain a new population;
and 5: judging whether the maximum iterative evolution times 100 are reached, if not, entering the step 2, and if so, entering the step 6;
step 6: and (4) carrying out binary decoding on each chromosome in the result population, and outputting the optimal individual as an initial solution of the simulated annealing algorithm by utilizing the fitness function.
The following is the step of simulating the annealing algorithm, wherein the simulated annealing algorithm comprises two cycles, the termination criteria of the two cycles are described, and then the specific steps of annealing are described.
Inner loop termination criteria: the inner loop termination criteria is responsible for controlling the number of candidate solutions generated at different temperatures, also known as the Metropolis sampling stability criteria. Commonly employed inner loop termination criteria include:
1. the change of new solutions obtained by a plurality of continuous searches is small;
2. checking whether the average value of the objective function is basically maintained stable;
3. sampling is performed at regular intervals.
The algorithm herein chooses criterion 3 as the inner loop termination criterion of the algorithm.
Outer loop termination criteria: also known as algorithm termination criteria, commonly used methods include:
1. setting the total cycle number of the external cycle;
2. setting a threshold for temperature termination;
3. the optimal solution is kept unchanged within a long iteration number in the algorithm operation process;
4. and checking whether the entropy of the system is stable.
The present embodiment employs criterion 2.
The simulated annealing algorithm steps of the hybrid genetic simulated annealing algorithm are described as follows:
and 7: initializing parameters of a simulated annealing algorithm, wherein the initial temperature is 20, the end temperature is 0.05, the maximum number of internal circulation is 10, and the fitness function of the annealing step is the same as that of the previous step, and calculating the current fitness;
and 8: randomly generating a next solution in the neighborhood of the current solution, and calculating the fitness of the next solution;
and step 9: calculating the difference between the fitness of the next solution and the current solution fitness, accepting the new solution as the current solution according to Metropolis criterion when the difference is larger than zero, and otherwise directly accepting the new solution as the current solution;
i.e. greater than zero, by probability
Accepting the new solution as the current solution, or directly accepting the new solution as the current solution, wherein E (x)new) Fitness for the new solution, E (x)old) The fitness of the old solution is shown, and T is the current temperature;
step 10: if the iteration number of the internal loop is not reached, the step 8 is carried out, and when the iteration number is reached, 0.9 times of the current temperature is taken as the next temperature for cooling, and the step 11 is carried out;
step 11: and outputting the optimal solution to complete parameter identification when the lowest temperature is reached, and obtaining each error item.
The robot model error parameter identification is completed through the 11 steps.
The embodiment utilizes the performance advantages of the genetic algorithm and the simulated annealing algorithm to be mixed and used, and utilizes the global search capability of the genetic algorithm and the local search capability of the simulated annealing algorithm to realize the accurate identification of the model error, so as to correct all parameters of the robot model and improve the absolute positioning accuracy of the tail end of the robot; the optimal solution obtained by the genetic algorithm is directly input into the simulated annealing algorithm, so that the method has global optimization capability, can jump out the local optimal solution, and accurately identifies the error model parameters.
The embodiment also provides a robot kinematic parameter identification system based on a hybrid genetic simulated annealing algorithm, which includes: the system comprises an error model building module, an initial population building module, a population individual fitness calculating module, a binary coding module, an evolved population building module, an iterative evolution frequency judging module, a fitness calculating module, a difference value calculating and judging module and an internal circulation iteration frequency judging module;
in this embodiment, the error model building module is configured to build a kinematic model of the robot and an error model of the robot;
in this embodiment, the initial population building module is configured to set a population size and randomly generate an initial population;
in this embodiment, the population individual fitness calculation module is configured to calculate the fitness of each individual in the population by using a reciprocal of a sum of squares of x, y, and z errors of an actual position and a theoretical position of a robot end as a fitness function;
in this embodiment, the binary encoding module is configured to perform binary encoding on each individual in the population;
in this embodiment, the evolved population constructing module is configured to set a selection probability, a crossover probability, and a variation probability, perform selection operation on a binary-coded chromosome, perform single-point crossover operation on the selected chromosome, and perform single-point bitwise negation operation on the crossed chromosome to obtain an evolved population;
in this embodiment, the iterative evolution frequency judging module is configured to judge whether the maximum iterative evolution frequency is reached, calculate fitness of each individual in the population if the maximum iterative evolution frequency is not reached, perform binary decoding on each chromosome in the resultant population if the maximum iterative evolution frequency is reached, and output an optimal individual as an initial solution of the simulated annealing algorithm by using a fitness function;
in this embodiment, the fitness calculation module is configured to initialize parameters of a simulated annealing algorithm and calculate the current fitness;
randomly generating a next solution in the neighborhood of the current solution, and calculating the fitness of the next solution;
in this embodiment, the difference calculation and judgment module is configured to calculate a difference between the fitness of the next solution and the fitness of the current solution, and if the difference is greater than zero, accept the new solution as the current solution according to Metropolids criteria, otherwise, directly accept the new solution as the current solution;
in this embodiment, the internal loop iteration number determining module is configured to determine whether the number of internal loop iterations is reached, shift to calculating the fitness of the next solution if the number of internal loop iterations is not reached, decrease the temperature by taking a set multiple of the current temperature as the next temperature when the number of iterations is reached, output the optimal solution when the lowest temperature is reached, complete parameter identification, and obtain each error item.
The embodiment also provides a storage medium, which may be a storage medium such as a ROM, a RAM, a magnetic disk, an optical disk, or the like, where one or more programs are stored, and when the program is executed by a processor, the method for identifying the kinematic parameters of the robot based on the hybrid genetic simulation annealing algorithm is implemented.
The embodiment also provides a computing device, which may be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet computer, or other terminal devices with a display function, and the computing device includes a processor and a memory, where the memory stores one or more programs, and when the processor executes the programs stored in the memory, the method for identifying the kinematic parameters of the robot based on the hybrid genetic simulation annealing algorithm is implemented.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (9)
1. A robot kinematic parameter identification method based on a hybrid genetic simulated annealing algorithm is characterized by comprising the following steps:
constructing a kinematic model of the robot and an error model of the robot;
setting the size of a population, and randomly generating an initial population;
calculating the fitness of each individual in the population by taking the reciprocal of the x, y and z error square sum of the actual position and the theoretical position of the tail end of the robot as a fitness function;
outputting the optimal individual as an initial solution of a simulated annealing algorithm by adopting a fitness function;
initializing parameters of a simulated annealing algorithm, and calculating the current fitness;
randomly generating a next solution in the neighborhood of the current solution, and calculating the fitness of the next solution;
calculating the difference between the fitness of the next solution and the fitness of the current solution, if the difference is greater than zero, accepting the new solution as the current solution according to the Metropolis criterion, and otherwise, directly accepting the new solution as the current solution;
and if the internal loop iteration times are not reached, the fitness of the next solution is calculated, the set multiple of the current temperature is taken as the next temperature to reduce the temperature when the iteration times are reached, the lowest temperature is reached, the optimal solution is output, the parameter identification is completed, and each error item is obtained.
2. The method for identifying kinematic parameters of a robot based on the hybrid genetic simulated annealing algorithm according to claim 1, further comprising the steps of binary coding, population evolution and binary decoding after the fitness of each individual in the population is calculated, and specifically comprising the steps of:
carrying out binary coding on each individual in the population;
setting selection probability, crossover probability and variation probability, firstly carrying out selection operation on chromosomes after binary coding, then carrying out single-point crossover operation on the selected chromosomes, and then carrying out single-point bitwise negation operation on the crossed chromosomes to obtain an evolved population;
and judging whether the maximum iterative evolution times are reached, calculating the fitness of each individual in the population if the maximum iterative evolution times are not reached, and performing binary decoding on each chromosome in the result population if the maximum iterative evolution times are reached.
3. The method for identifying kinematic parameters of a robot based on hybrid genetic simulated annealing algorithm according to claim 1, wherein the number of iterations is reduced by taking a set multiple of the current temperature as the next temperature, and the set multiple is set to 0.9.
4. The method for identifying the kinematic parameters of the robot based on the hybrid genetic simulation annealing algorithm as claimed in claim 1, wherein the robot adopts a six-degree-of-freedom robot, and the kinematic model of the robot adopts an MD-H kinematic model.
5. The hybrid genetic simulated annealing algorithm-based robot kinematics parameter identification method according to claim 4, wherein the step of establishing the joint axis reference coordinate system by the MD-H kinematics model comprises:
determining the direction of a z axis, the origin of a coordinate system and the direction of an x axis;
determining the z-axis direction: a coordinate system established at the joint i is named as a coordinate system i-1, and if the joint i is a rotary shaft joint, the direction of a z axis is consistent with the axis of a rotary shaft of the joint; if the joint i is a moving joint, the moving direction of the joint i is determined as the direction of the z-axis;
determining the origin and the x-axis direction of a coordinate system:
when two adjacent joint axes zi-1And ziWhen the straight line is a non-coplanar straight line, there is only one shortest common perpendicular line between the two axes, and the direction of the x axis is the shortest common perpendicular line from zi-1Direction ziThe origin of the coordinate system is the intersection point of the z-axis and the x-axis;
when two adjacent joint axes zi-1And ziWhen the two axes are parallel, a plurality of common perpendicular lines with the same length exist between the two axes, and the joint is selected to be the previous jointThe common perpendicular line intersecting the origin of the coordinate system is taken as the shortest common perpendicular line, and the direction of the x axis is the shortest common perpendicular line from zi-1Direction ziThe origin of the coordinate system is the intersection point of the z-axis and the x-axis;
when two adjacent joint axes zi-1And ziWhen the two axes are perpendicular to each other, the normal of the plane where the two axes are located is selected as a common perpendicular line, the direction of the x axis is the direction of the common perpendicular line, and the origin of a coordinate system is the intersection point of the common perpendicular line and the two axes;
determining the direction of the y-axis: the y-axis direction is determined by the right hand rule.
6. The method for identifying kinematic parameters of a robot based on hybrid genetic simulation annealing algorithm of claim 4, wherein the MD-H kinematic model adds a rotation β around the y-axis to each joint coordinate system in the classical D-H model, and when two adjacent joint axes are parallel, the rotation β is usediSubstituted by diAt this time diIs zero; when the adjacent two joint axes are not parallel, define betaiIs zero;
constructing a homogeneous transformation matrix of an adjacent joint reference coordinate system as follows:
and obtaining an error model of the robot according to the homogeneous transformation matrix of each joint, wherein the error model is expressed as:
PG-P＝MθΔθ+MαΔα+MaΔa+MdΔd+MβΔβ
Δθ＝[Δθ1,Δθ2,…,Δθ6]'
Δα＝[Δα1,Δα2,…,Δα6]'
Δa＝[Δa1,Δa2,…,Δa6]'
Δd＝[Δd1,Δd2,…,Δd6]'
Δβ＝Δβ3
wherein c represents cos (), s represents sin (), and PGRepresenting the actual position of the robot end in a base coordinate system established under the laser tracker, P representing the theoretical position of the end determined from the kinematic model of the robot and the joint angles, Mθ、Mα、Ma、Md、MβCoefficient matrices Δ θ, Δ α, Δ a, Δ d, Δ β, respectively.
7. A robot kinematic parameter identification system based on a hybrid genetic simulated annealing algorithm is characterized by comprising the following components: the system comprises an error model construction module, an initial population construction module, a population individual fitness calculation module, an initial solution construction module, a fitness calculation module, a difference value calculation judgment module and an internal loop iteration number judgment module;
the error model building module is used for building a kinematic model of the robot and an error model of the robot;
the initial population building module is used for setting the size of a population and randomly generating an initial population;
the population individual fitness calculation module is used for calculating the fitness of each individual in the population by taking the reciprocal of the x, y and z error square sum of the actual position and the theoretical position of the tail end of the robot as a fitness function;
the initial deconstruction modeling block is used for outputting an optimal individual as an initial solution of the simulated annealing algorithm by adopting a fitness function;
the fitness calculation module is used for initializing parameters of a simulated annealing algorithm and calculating the current fitness;
randomly generating a next solution in the neighborhood of the current solution, and calculating the fitness of the next solution;
the difference value calculation and judgment module is used for calculating the difference value between the fitness of the next solution and the fitness of the current solution, if the difference value is larger than zero, the new solution is accepted as the current solution according to the Metropolis criterion, and if the difference value is not larger than zero, the new solution is directly accepted as the current solution;
the internal loop iteration frequency judging module is used for judging whether the internal loop iteration frequency is reached, if the internal loop iteration frequency is not reached, the calculation of the fitness of the next solution is carried out, the iteration frequency is reached, the set multiple of the current temperature is taken as the next temperature to be cooled, the lowest temperature is reached, the optimal solution is output, parameter identification is completed, and each error item is obtained.
8. A storage medium storing a program, wherein the program, when executed by a processor, implements the hybrid genetic simulated annealing algorithm-based robot kinematics parameter identification method according to any of claims 1-6.
9. A computing device comprising a processor and a memory for storing a program executable by the processor, wherein the processor, when executing the program stored by the memory, implements the hybrid genetic simulated annealing algorithm-based robot kinematics parameter identification method according to any of claims 1-6.
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