CN118081745A - Arc welding robot path optimization method - Google Patents
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Abstract
The invention discloses an arc welding robot path optimization method, which comprises the following steps: step S1, acquiring arc welding target point data, wherein the target point data comprises geometric coordinate data and gesture data; step S2, simplifying arc welding target points; s3, establishing a mathematical model by taking the shortest total distance as an optimization target; s4, solving an arc welding robot path by utilizing a genetic algorithm; and S5, creating a welding program according to the solved arc welding robot path. Compared with the prior art, the invention can further optimize the path of the arc welding robot and improve the production efficiency.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing, in particular to an arc welding robot path optimization method.
Background
With the development of the age, more and more industrial robots are used for replacing workers to execute special work tasks in severe or even harmful working environments, so that the labor intensity of the workers is reduced, the production efficiency is improved, and the working environments are improved. In the face of complex operating conditions, such as the automatic welding of large steel structural components by industrial robots, the welding sequence is generally determined from human experience. However, in actual operation, a large number of ineffective travel paths will appear, which greatly affects the welding efficiency of the equipment.
In order to save the running time of the robot, the disclosed document 1 (welding robot three-dimensional path planning research [ J ]. Mechatronics, 2009,15 (8): 85-87,105) based on genetic algorithm) converts the robot path planning problem into an optimized mathematical problem, and the optimized path can obviously shorten the operation time. Because the traditional optimization algorithm is used for solving, the solution converges too early in the process of the iteration, and the solution is trapped in a local optimization. For this reason, publication 2 (welding path planning for body-in-white robot based on genetic algorithm [ J ]. University of the same university (natural science edition), 2011,39 (4): 576-580,598.) globally optimizes the robot path planning problem by means of genetic algorithm, and finds the shortest running path of the robot, but the mathematical model established by it obviously does not conform to the actual production situation because the change of the posture of the robot is not considered.
Disclosure of Invention
In order to solve the technical problems, the invention provides an arc welding robot path optimization method, which comprises the following steps:
Step S1, acquiring arc welding target point data, wherein the target point data comprises geometric coordinate data and gesture data;
step S2, simplifying arc welding target points;
S3, establishing a mathematical model by taking the shortest total distance as an optimization target;
S4, solving an arc welding robot path by utilizing a genetic algorithm;
And S5, creating a welding program according to the solved arc welding robot path.
Preferably, in the step S1, the geometric coordinate data is extracted from a three-dimensional model of the workpiece and the fixture.
Preferably, in the step S1, the attitude data is obtained by quantization using euler angles and quaternions.
Preferably, in the step S1, the step of acquiring arc welding target point data is as follows: step S11, establishing a three-dimensional model of the workpiece and the clamp; step S12, establishing a workpiece coordinate system and a tool coordinate system of the robot; step S13, determining all welding seams and corresponding postures of welding guns according to process requirements; s14, extracting the welding start point coordinates and the end point coordinates of each welding line by taking a workpiece coordinate system as a reference; s15, taking an average value of the coordinates of the starting point and the end point of the welding seam, and recording the average value as the coordinate of the middle point of the welding seam; s16, calculating a spatial rotation angle of a workpiece coordinate system and a tool coordinate system according to the posture of the welding gun, and recording the spatial rotation angle as a Euler angle of the welding gun of each welding joint; step S17, converting the Euler angle of the welding gun into a quaternion according to a preset formula;
preferably, in the step S2, the method for simplifying the arc welding target point is as follows: simplifying the starting point and the end point of each welding line to be the middle point of the welding line, and simplifying the 3D space coordinates of all welding points to be 2D plane coordinates.
Preferably, in the step S4, the specific step of solving the arc welding robot path by using a genetic algorithm is as follows: step S41, carrying out chromosome coding on each feasible path; step S42, setting an initial population; step S43, constructing a fitness function; step S44, determining genetic operator strategies; step S45, solving an arc welding robot path.
Compared with the prior art, the invention can further optimize the path of the arc welding robot and improve the production efficiency.
Drawings
The accompanying drawings are intended to illustrate the general features of methods, structures and/or materials used in accordance with certain exemplary embodiments of the invention, and supplement the description in this specification. The drawings of the present invention, however, are schematic illustrations that are not to scale and, thus, may not be able to accurately reflect the precise structural or performance characteristics of any given embodiment, the present invention should not be construed as limiting or restricting the scope of the numerical values or attributes encompassed by the exemplary embodiments according to the present invention. The invention is described in further detail below with reference to the attached drawings and detailed description:
FIG. 1 is a schematic diagram of steps of an arc welding robot path optimization method according to an embodiment;
FIG. 2 is a schematic diagram of a gun posture change;
FIG. 3 is a schematic diagram of a chromosome coding method based on sequential expression;
FIG. 4 is a schematic diagram of an example of a cross operation;
FIG. 5 is a schematic diagram of a variation operation example;
fig. 6 is a schematic view of the escalator guide rail structure;
FIG. 7 is a schematic diagram of the upper rail assembly optimization results;
FIG. 8 is a schematic diagram of the lower track assembly optimization results.
Detailed Description
Other advantages and technical effects of the present invention will become more fully apparent to those skilled in the art from the following disclosure, which is a detailed description of the present invention given by way of specific examples. The invention may be practiced or carried out in different embodiments, and details in this description may be applied from different points of view, without departing from the general inventive concept. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. The following exemplary embodiments of the present invention may be embodied in many different forms and should not be construed as limited to the specific embodiments set forth herein. It should be appreciated that these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the technical solution of these exemplary embodiments to those skilled in the art.
The embodiment provides an arc welding robot path optimization method, which comprises the following steps:
Step S1, acquiring arc welding target point data, wherein the target point data comprises geometric coordinate data and gesture data;
step S2, simplifying arc welding target points;
S3, establishing a mathematical model by taking the shortest total distance as an optimization target;
S4, solving an arc welding robot path by utilizing a genetic algorithm;
And S5, creating a welding program according to the solved arc welding robot path.
In the step S1, the geometric coordinate data is extracted from a three-dimensional model of the workpiece and the fixture. And the attitude data are obtained by quantization through Euler angles and quaternions.
Under specific working conditions such as welding, the robot must operate according to the technological requirements, namely, the welding gun and the welding plane are guaranteed to be in an included angle of 45 degrees as far as possible, and meanwhile, the robot does not interfere with a workpiece or a clamp. Therefore, during the running process of the robot, the posture of the welding gun is changed continuously when the welding gun passes through each target point. As shown in fig. 2, the spatial pose of the fixed-point rotation of the object can be quantified by using euler angles, that is, the pose of the robot can be intuitively quantified by rotating euler angles γ, β and α around the Z axis, the Y axis and the X axis, respectively.
In the robot program, the pose of the target point is represented by a quaternion. If the Euler angle of the target point is known, the quaternion (q 1,q2,q3,q4) can be calculated by using the following preset formula:
Wherein alpha, beta and gamma are Euler angles of the welding gun, and q 1、q2、q3、q4 is four elements.
The step S1, the specific step of acquiring arc welding target point data is as follows:
Step S11, establishing a three-dimensional model of the workpiece and the clamp;
Step S12, establishing a workpiece coordinate system O and a tool coordinate system O' of the robot;
Step S13, determining all welding seams h i and corresponding postures of welding guns according to process requirements;
Step S14, extracting the welding start point coordinates of each welding seam h i by taking the workpiece coordinate system O as a reference And endpoint coordinates/>
Step S15, taking an average value of the coordinates of the starting point and the end point of the welding seam, and marking the average value as the coordinate (x i,yi,zi) of the midpoint of the welding seam;
Step S16, calculating the space rotation angle of a workpiece coordinate system O and a tool coordinate system O' according to the welding gun posture, and recording the space rotation angle as a Euler angle (alpha i,βi,γi) of the welding gun of each welding seam h i;
step S17, converting the Euler angle (alpha i,βi,γi) of the welding gun into a quaternion according to a preset formula
As the number of robot target points increases, the calculation amount of solving the path optimization problem increases dramatically. Therefore, the path optimization problem needs to be simplified first, and then an optimization mathematical model is established, so that the time of global optimization iteration can be reduced. In the step S2, the method for simplifying the arc welding target point is as follows: the starting point and the end point of each welding line are simplified to be the middle point of the welding line, and the 3D space coordinates of all welding points are simplified to be 2D plane coordinates due to small change of the coordinates of each welding point in the height direction.
The welding robot running path is: starting from the origin, the welding seam passes through the midpoint of each welding seam in a specific gesture, and finally returns to the origin. Taking the shortest total distance as an optimization target, in step S3, the mathematical model is as follows:
d(θi+1,θi)＝λ[(αi+1-αi)+(βi+1-βi)+(γi+1-γi)]
Wherein the method comprises the steps of D (theta i+1,θi) is Euler angle change of a welding gun between two welding spots, lambda is a conversion coefficient for converting a rotation angle into a linear motion distance, L is an arc welding robot path, and alpha i、βi、γi is Euler angle of the welding gun.
In the genetic algorithm of step S4, a feasible path is expressed as a chromosome in the initial population, and then the population is evolved through selection, crossover and mutation operations, and the population more suitable for the environment is selected, so that the shortest path is solved.
The step S4 is a specific step of solving the arc welding robot path by utilizing a genetic algorithm;
Step S41, performing chromosome coding on each feasible path. Each possible path will be denoted as a chromosome, consisting mainly of a starting point and welded nodes travelled through, the welded nodes in the path being called genes of the chromosome. Aiming at the path planning problem, the embodiment adopts sequential expression to encode feasible solutions. As can be seen from FIG. 3, the encoding method can intuitively arrange the welded nodes into a chromosome according to the sequence.
Step S42, initial population setting. Genetic algorithms evolve from an initial population, which is typically composed of a combination of randomly generated chromosomes. The size of the initial population needs to be determined based on the number of genes in the chromosome (number of welds) and computational experience. The scale setting of the initial population is not suitable to be too small, so that the diversity of the population is reduced, and the genetic algorithm is converged to a local optimal solution too early; meanwhile, the calculation efficiency of optimizing solving is prevented from being reduced due to the fact that the initial population is too large.
And S43, constructing a fitness function. For the path optimization problem, the objective function is to find the minimum total length of the robot path. In genetic algorithms, individuals are evaluated for fitness, and fitness functions must be in a maximized and non-negative form. Thus, the inverse of the objective function L can be taken as the fitness function, i.e. f=1/L, according to the mathematical model described above.
And S44, determining genetic operator strategies. According to the nature of the path optimization problem and the characteristics of the chromosome coding, the embodiment iterates the population by using three genetic operators of selection, crossover and mutation, and the specific strategy is described as follows.
1. Selection operator
The selection is to select some individuals from the existing population with a certain probability as parents to reproduce the next generation of individuals. The optimal preservation method is adopted, namely, a plurality of chromosomes with the largest fitness in the population are selected to directly replace and eliminate the chromosomes with the smallest fitness. Through multiple generations of selection and elimination
2. Crossover operator
Crossover operations produce a new generation of chromosomes with a certain probability by a combination of exchanges between certain two chromosomes. Double-point crossover is used herein, i.e., the exchange of partial gene segments between two chromosomes with a certain probability. As shown in FIG. 4, the two parent chromosomes A and B, the brackets are randomly selected breakpoint positions, and the gene segments A1 (5 7 8 9) and B1 (9 43 2) are exchanged in the brackets, so that A1 and B1 are obtained after gene exchange. If the principle that each node appears only once cannot be complied with, the gene conflict occurs. And for the conflict gene in a1, finding the position of the conflict gene in B1, replacing the conflict gene with the gene in the corresponding position in A, performing multiple operations until no conflict exists, and performing the same operation on the conflict gene in B1 to finally obtain offspring chromosomes a and B, thereby ensuring the diversity of the population.
3. Mutation operator
Mutation operators update populations by randomly changing the value of a gene in a chromosome to create a new chromosome, i.e., extend the range of solutions. For the sequential expression coding mode adopted in the text, the positions of two path nodes in one chromosome can be randomly exchanged to achieve the purpose of mutation, as shown in fig. 5.
Step S45, solving an arc welding robot path. The basic flow of the solution is shown in fig. 6, from the generation of the initial population, the fitness is calculated, if the optimization criterion is met, the optimal individual is solved, if the optimization criterion is not met, the population is iterated by using three genetic operators of selection, intersection and variation, and then the fitness is calculated until the optimization criterion is met.
And finally, step S5, creating a welding program according to the solved arc welding robot path.
In one example of application, the arc welding robot path optimization method is applied to welding of escalator rail assemblies.
The escalator is provided with a circulating operation step, and can realize upward or downward tilting and automatic passenger conveying. The ladder route of the whole escalator is formed by splicing an upper guide rail assembly, a lower guide rail assembly and a truss guide rail, as shown in fig. 6. The upper and lower guide rail assemblies are welding components, and automatic welding production can be realized by using a robot. Because the welding points of the guide rail assembly are many and complex, the method of the embodiment is adopted to carry out path planning and automatic programming, thereby remarkably shortening the manufacturing period and reducing the production cost.
According to the mathematical model of the embodiment, path optimization mathematical models of the upper guide rail assembly and the lower guide rail assembly are respectively established, and then a genetic algorithm is utilized to solve to obtain the shortest welding path, and the optimization result is shown in fig. 7 and 8. Compared with the prior art, the welding takt time of the arc welding robot path optimized by the embodiment is reduced by 18%.
The present invention has been described in detail by way of specific embodiments and examples, but these should not be construed as limiting the invention. Many variations and modifications may be made by one skilled in the art without departing from the principles of the invention, which is also considered to be within the scope of the invention.
Claims (11)
1. An arc welding robot path optimization method, characterized by comprising the following steps:
Step S1, acquiring arc welding target point data, wherein the target point data comprises geometric coordinate data and gesture data;
step S2, simplifying arc welding target points;
S3, establishing a mathematical model by taking the shortest total distance as an optimization target;
S4, solving an arc welding robot path by utilizing a genetic algorithm;
And S5, creating a welding program according to the solved arc welding robot path.
2. The arc welding robot path optimizing method according to claim 1, wherein in the step S1, the geometric coordinate data is extracted from a three-dimensional model of the workpiece and the jig.
3. The arc welding robot path optimizing method according to claim 2, wherein in the step S1, the posture data is obtained by quantization using euler angles and quaternions.
4. The arc welding robot path optimizing method as recited in claim 3, wherein the step S1 of acquiring arc welding target point data is as follows:
Step S11, establishing a three-dimensional model of the workpiece and the clamp;
Step S12, establishing a workpiece coordinate system and a tool coordinate system of the robot;
step S13, determining all welding seams and corresponding postures of welding guns according to process requirements;
s14, extracting the welding start point coordinates and the end point coordinates of each welding line by taking a workpiece coordinate system as a reference;
S15, taking an average value of the coordinates of the starting point and the end point of the welding seam, and recording the average value as the coordinate of the middle point of the welding seam;
S16, calculating a spatial rotation angle of a workpiece coordinate system and a tool coordinate system according to the posture of the welding gun, and recording the spatial rotation angle as a Euler angle of the welding gun of each welding joint;
and S17, converting the Euler angle of the welding gun into a quaternion according to a preset formula.
5. The arc welding robot path optimizing method as recited in claim 4, wherein the preset formula is:
Wherein alpha, beta and gamma are Euler angles of the welding gun, and q 1、q2、q3、q4 is four elements.
6. The arc welding robot path optimizing method according to claim 1, wherein the step S2 of simplifying the arc welding target point is: simplifying the starting point and the end point of each welding line to be the middle point of the welding line, and simplifying the 3D space coordinates of all welding points to be 2D plane coordinates.
7. The arc welding robot path optimizing method according to claim 1, wherein in the step S3, the mathematical model is:
d(θi+1,θi)＝λ[(αi+1-αi)+(βi+1-βi)+(γi+1-γi)]
Wherein the method comprises the steps of D (theta i+1,θi) is Euler angle change of a welding gun between two welding spots, lambda is a conversion coefficient for converting a rotation angle into a linear motion distance, L is an arc welding robot path, and alpha i、βi、γi is Euler angle of the welding gun.
8. The arc welding robot path optimizing method according to claim 1, wherein the step S4 of solving the arc welding robot path by using the genetic algorithm comprises the steps of:
Step S41, carrying out chromosome coding on each feasible path;
Step S42, setting an initial population;
step S43, constructing a fitness function;
step S44, determining genetic operator strategies;
Step S45, solving an arc welding robot path.
9. The arc welding robot path optimizing method according to claim 8, wherein in the step S41, the welding nodes in the arc welding robot path are used as genes of the chromosome, and the feasible solutions are encoded by using the sequential expression.
10. The arc welding robot path optimizing method according to claim 8, wherein in the step S43, an inverse of an objective function of the mathematical model is taken as a fitness function.
11. The arc welding robot path optimizing method as recited in claim 8, wherein in the step S44, the genetic operator strategy iterates the population using three genetic operators of selection, crossover, and mutation.
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