Parameter optimization design method and device for 2-RPR series robot

Abstract

The application belongs to the technical field of robot structure design, and discloses a parameter optimization design method and equipment for a 2-RPR serial robot, which comprises the following steps: (1) sequentially connecting coordinate points at two joints, lower hinge points of electric cylinders and upper hinge points of electric cylinders corresponding to two limit positions of the 2-RPR serial robot to obtain an obstacle space of the robot; the obstacle space of the robot is an electric cylinder movement area which can be theoretically reached by the end of the robot and is not allowed to be reached in actual use; (2) judging whether the working space of the robot envelops a target working space of the robot, and then judging whether the target working space of the robot and the obstacle space of the robot are coincident, and determining a coefficient k according to the two judgment results; (3) optimizing link parameters and branch chain parameters of the 2-RPR serial robot by using a genetic algorithm. The application obtains robot design parameters with more optimal link lengths and more optimal branch chain arrangements under the premise that the working space meets the demand.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of robot structure design, and more particularly to a parameter optimization design method and device for a 2-RPR serial robot. BACKGROUND

[0002] The size of the robot workspace is mainly determined by the robot's work requirements. For a serial robot, optimizing the robot parameters can improve the spatial accessibility of the robot, make the robot structure more compact, reduce the weight of the robot, and reduce the manufacturing cost, while the optimized workspace utilization is higher and more in line with the actual work requirements.

[0003] In 2024, Umbrella Red Army and others optimized the parameters of the Delta parallel robot. By establishing an envelope penalty function, a kinematic performance evaluation function was obtained by using multivariate nonlinear fitting and linear weighted combination method, and an optimization model was established combining the condition number distribution characteristics and distortion constraint conditions, and genetic algorithm was used for optimization. Compared with before optimization, the reachable workspace volume is reduced by 14.26%. In 2024, Xu Chenyu and others used WOA algorithm to optimize the parameters of a new type of 4RPUR parallel robot, making the workspace boundary smooth and symmetrically distributed. In 2024, Li Zhen and others used particle swarm optimization algorithm to optimize the main structural parameters of the parallel robot leg, and the optimized mechanical leg workspace volume increased by 69.83%. In 2014, Gan Yi and others used genetic algorithm to optimize the D-H parameters of the 6R type robot under the given workspace, and obtained the optimal solution that meets the constraint conditions and minimizes the robot link length. In 2021, Hu Wangning used genetic algorithm to optimize the arm length of the PUMA type arc welding robot, thereby optimizing the robot workspace, and the optimization rate reached 5.5%.

[0004] Scholars have carried out a lot of optimization research on the structural parameters of parallel robots, expanding or reducing the workspace of the robot, and the optimized robot configuration can better serve the application scenarios. With the rapid development of marine equipment, the demand for efficient and high-quality processing of large propellers is increasing, and the 2-RPR serial robot has become an effective solution for efficient and high-quality robot milling of large propellers due to its high rigidity. Since this configuration consists of a six-axis serial body and a double-cylinder branch structure, the link parameters and branch parameters often have redundancy, and this configuration also has a unique obstacle space caused by the movement of the electric cylinder, which limits the robot workspace. SUMMARY

[0005] In view of the above defects or improvement needs of the prior art, the present application provides a parameter optimization design method and device for a 2-RPR serial robot, which aims to solve the problem of redundancy of link parameters and branch chain parameters of the 2-RPR serial robot.

[0006] To achieve the above object, according to one aspect of the present application, a parameter optimization design method for a 2-RPR serial robot is provided, which comprises the following steps:

[0007] (1) sequentially connecting the coordinate points at the two joints of the 2-RPR serial robot, the lower hinge point of the electric cylinder, and the upper hinge points of the electric cylinder corresponding to the two limit positions to obtain the obstacle space of the robot; wherein the obstacle space of the robot is the movement region of the electric cylinder that the robot end can theoretically reach but is not allowed to reach in actual use;

[0008] (2) judging whether the working space of the robot envelops the target working space of the robot, and further judging whether the target working space of the robot coincides with the obstacle space of the robot, and determining the coefficient k according to the two judgment results;

[0009] (3) optimizing the link parameters and branch chain parameters of the 2-RPR serial robot by using a genetic algorithm; wherein the fitness function of the genetic algorithm is:

[0010] f i '=max{f1,f2,…,f n}-f i +rand()i=1,2,…,j

[0011] In the formula, j is the population size, f i =k*(ω1a2+ω2a3+ω3d4+ω4c1+ω5e1), ω1, ω2, ω3, ω4, ω5 are the weights of the five optimization variables a2, a3, d4, c1, e1 corresponding to the population i.

[0012] Further, if the working space of the robot envelops the target working space of the robot, and the target working space of the robot does not coincide with the obstacle space of the robot, then the coefficient k is determined as 1; otherwise, the coefficient k is determined as 1.5.

[0013] Further, when judging whether the working space of the robot envelops the target working space of the robot, it is only necessary to judge the three planes Y=0, Z=d1 and Z=d2 in the robot working space.

[0014] Further, for the Y=0 cross section: starting from the point at the right end of the cross section, i.e. the point X max with the largest x coordinate, record the coordinates of the point as (x maxthen find the point with the maximum x-coordinate in the workspace within the range (z0-H / 2, z0+H / 2) and x max -L / 2, record the value of the x-coordinate of the point, at this time the optimal position of the target workspace in the section is determined, the coordinates of the four vertices are A(x0, z0+H / 2), B(x0, z0-H / 2), C(x0+L, z0-H / 2), and D(x0+L, z0+H / 2), since the x-coordinate values of the two points AB are the x-coordinate values of the concave points on the left profile of the workspace, the line segment AB must be contained in the robot workspace, while the two points CD need to be judged, whether the y-coordinate of the point with the x-coordinate of x0+L / 2 in the workspace exists simultaneously smaller than the y-coordinate of the point C and greater than the y-coordinate of the point D, if both points meet the requirements, it means that the line segment CD is also contained in the robot workspace, which means that the rectangle formed by the four points ABCD is contained in the robot workspace.

[0015] Further, according to the shape characteristics of the cross-section vertex coordinates of the machine obstacle space and the vertex coordinates of the target workspace, it is only necessary to judge whether the B point in the target workspace is in the obstacle space at the Y=0 section.

[0016] Further, the transformation matrices of each link of the 2-RPR serial robot are sequentially multiplied to obtain the relative transformation matrix of the end link coordinate system n with respect to the base coordinate system 0, and then the random joint variable is substituted into the obtained relative transformation matrix to obtain the workspace of the robot.

[0017] Further, the coordinate systems of each link of the 2-RPR serial robot are established according to the configuration of the robot, the link parameters are determined according to the coordinate systems of each link, and the transformation matrices of each link are sequentially multiplied to obtain the relative transformation matrix of the end link coordinate system n with respect to the polar coordinate system 0. The random joint variable generated by the random function rand() is substituted into the obtained relative transformation matrix to obtain the coordinate set {P|P(x, y, z)} of the end of the robot, and then the workspace of the robot is obtained.

[0018] Further, the random joint variable is generated by using the random function rand():

[0019]

[0020] In the formula, q is a joint random variable; q max and q min are the upper and lower limits of the joint angle; i=1,…,n, and n is the number of joints of the robot.

[0021] The application further provides a parameter optimization design system of a 2-RPR serial robot.

[0022] The application further provides a computer readable storage medium storing machine executable instructions, which, when invoked and executed by a processor, cause the processor to implement the parameter optimization design method of the 2-RPR serial robot.

[0023] Overall, compared with the prior art, the parameter optimization design method and device of the 2-RPR serial robot provided by the application mainly have the following beneficial effects:

[0024] 1. The application optimizes the kinematic parameters and branch chain parameters of the 2-RPR robot for processing large propellers based on the workspace feasibility judgment result and the genetic algorithm, considers the obstacle workspace factor caused by the electric cylinder configuration, and obtains the robot design parameters with more optimal link lengths and more optimal branch chain arrangements under the premise that the workspace meets the requirements, and the branch chain structure is compact.

[0025] 2. Since the robot is used for processing large propeller blades, the values of the parameters are relatively large, at this time, the value of the fitness f is also very large, which will relatively weaken the differences between different individuals and cannot exert the advantages of roulette selection, and then the fitness function is adjusted to exert the advantages of selection. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a flowchart of a parameter optimization design method of a 2-RPR serial robot provided by the application;

[0027] Figure 2 is a schematic diagram of a double-cylinder type high-rigid robot structure in the application;

[0028] Figure 3 is a schematic diagram of a robot obstacle space section in the application;

[0029] Figure 4 is a schematic diagram of a workspace feasibility discrimination selection section in the application;

[0030] Figure 5 is a schematic diagram of a target workspace in the application;

[0031] Figure 6 is a schematic diagram of a target space and an obstacle space in the application;

[0032] Figure 7 is a flow chart of the overlap determination of the target space and the obstacle space in the present application.

[0033] In all the drawings, the same reference signs are used to denote the same elements or structures, wherein: 1 is a one-axis rotating platform, 2 is a two-axis branch electric cylinder, 3 is a two-axis connecting rod, 4 is a three-axis branch electric cylinder, and 5 is a three-axis connecting rod. DETAILED DESCRIPTION

[0034] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0035] Please refer to Figure 1 and Figure 2 , the present application provides a parameter optimization design method of a 2-RPR serial robot, which mainly includes the following steps:

[0036] Step one, sequentially multiply the transformation matrices of each connecting rod of the 2-RPR serial robot to obtain the relative transformation matrix of the end connecting rod coordinate system n relative to the base coordinate system 0, then substitute the random joint variable into the obtained relative transformation matrix to obtain the working space of the robot.

[0037] Among them, the coordinate system of each connecting rod of the robot is established according to the configuration of the 2-RPR serial robot, the connecting rod parameters are determined according to the coordinate system of each connecting rod, the transformation matrices of each connecting rod are sequentially multiplied to obtain the relative transformation matrix of the end connecting rod coordinate system n relative to the polar coordinate system 0, and the random joint variable generated by the random function rand() is substituted into the obtained relative transformation matrix to obtain the coordinate set {P|P(x,y,z)} of the end of the robot, and then the working space of the robot is obtained.

[0038] In one embodiment, a double-cylinder type high-rigid robot configuration for large propeller processing is shown in Figure 2 , which includes a one-axis rotating platform 1, a two-axis branch electric cylinder 2, a two-axis connecting rod 3, a three-axis branch electric cylinder 4, and a three-axis connecting rod 5 of the robot. Unlike traditional six-axis serial robots, both the two-axis and the three-axis of this configuration are driven by RPR type electric cylinder mechanisms, which belong to high-rigid configuration. The two ends of the two-axis branch electric cylinder 2 are respectively hinged to the one-axis platform and the two-axis connecting rod; the two ends of the three-axis branch electric cylinder 4 are respectively hinged to the two-axis connecting rod and the three-axis connecting rod.

[0039] The acquisition of the robot working space includes the following sub-steps:

[0040] Firstly, the coordinate system of each link of the robot is established according to the configuration of the 2-RPR serial robot, and the specific steps are as follows:

[0041] (1) draw the axis of each joint of the robot;

[0042] (2) draw the common perpendicular line a of the adjacent two axes i and i+1 i , find the intersection of the common perpendicular line a i and the axis i, and let the intersection point be the origin O of the coordinate system i i ;

[0043] (3) the z i axis coincides with the joint i axis;

[0044] (4) the x i axis coincides with the common perpendicular line a i , if z i and z i+1 intersect, then the x i is the normal of the plane formed by z i and z i+1 ;

[0045] (5) determine the y i axis according to the right-hand rule;

[0046] (6) when the first joint variable is zero, the coordinate system 0 coincides with the coordinate system 1; for the end coordinate system n, the origin and the direction of x n can be arbitrarily selected.

[0047] Then, the link parameters are determined according to the coordinate system of each link, which are as follows:

[0048] a i-1 = the distance measured along x i-1 from z i-1 to z i ;

[0049] α i-1 = the angle of rotation along x i-1 from z i-1 to z i ;

[0050] d i = the distance measured along z i from x i-1 to x i ;

[0051] θ i = the angle of rotation around z i from x i-1 to x i .

[0052] Then, the transformation matrix of the end link coordinate system n relative to the base coordinate system 0 can be obtained by sequentially multiplying the transformation matrices of the links. The general expression of the transformation matrix of the link is as follows:

[0053]

[0054] The transformation matrix of the end link coordinate system n relative to the base coordinate system 0 can be obtained by sequentially multiplying the transformation matrices of the links shown in formula (1-1) (as shown in formula 1-2):

[0055]

[0056] Then, the working space of the robot is solved by the Monte Carlo method, specifically:

[0057] A random joint variable is generated using the random function rand():

[0058]

[0059] In the formula, q is a joint random variable; q max and q min are the upper and lower limits of the joint angle, respectively; i = 1, …, n, and n is the number of joints of the robot, wherein the rotation range of the joint angles of the second and third axes depends on the stroke of the electric cylinder and the hinge position of the electric cylinder and the link.

[0060] Then, the generated joint angle (joint random variable) random value is substituted into the relative transformation matrix obtained from formula (1-2) to obtain the coordinate set {P|P(x, y, z)} of the end of the robot, and then all the coordinate points are plotted on a graph to obtain the point cloud graph of the working space of the robot.

[0061] Step two, the coordinate points of the two joints of the 2-RPR serial robot, the lower hinge point of the electric cylinder, and the upper hinge points of the electric cylinder corresponding to the two limit positions are sequentially connected to obtain the obstacle space of the robot; wherein the obstacle space of the robot is the movement area of the electric cylinder that the end of the robot can theoretically reach but is not allowed to reach in actual use.

[0062] Because the second and third axes of the robot are driven by electric cylinders, unlike traditional robots, there is a region of electric cylinder movement, and part of the region is theoretically reachable by the end of the robot, but in actual use, it is not allowed to reach, which is called the obstacle space of the robot. Therefore, the actual size of the obstacle space needs to be solved to ensure that the robot and the workpiece can avoid the obstacle space during processing. The obstacle space caused by the third axis electric cylinder is unreachable by the end of the robot, so only the size of the obstacle space caused by the second axis electric cylinder needs to be considered, and the solving method is as follows:

[0063] The horizontal distance from the lower hinge point of the two-axis electric cylinder to the two-axis joint is denoted as c1, and the vertical distance is denoted as e1; the distance from the upper hinge point of the electric cylinder to the two-axis joint is denoted as c2. With the two-axis perpendicular to the ground as the zero position, the limit angle of the robot two-axis moving in the negative direction is denoted as θ1, and the limit angle of the robot two-axis moving in the positive direction is denoted as θ2.

[0064] With the robot base coordinate as the origin, the coordinates of the two joints are (-a1, d1) obtained by the D-H parameters of the robot, and the coordinates of the lower hinge point of the electric cylinder are (-a1+c1, d1+e1). The position of the lower hinge point does not change, and the position of the upper hinge point changes with the extension and retraction of the sleeve rod. The coordinates of the upper hinge point of the electric cylinder are (-a1-c2*sinθ1, d1+c2*cosθ1) and (-a1+c2*sinθ2, d1+c2*cosθ2) at the two limit positions (the longest and the shortest of the electric cylinder), respectively. According to the geometric method, the above four coordinate points are sequentially connected to obtain the obstacle space of the robot, which consists of a sector area and a triangular area, as shown in Figure 3 .

[0065] Step three, judge whether the working space of the robot envelopes the target working space of the robot, and further judge whether the target working space of the robot and the obstacle space of the robot are coincident, to determine the coefficient k according to the two judgment results.

[0066] If the working space of the robot envelopes the target working space of the robot, and the target working space of the robot and the obstacle space of the robot are not coincident, then the coefficient k is determined as 1; otherwise, the coefficient k is determined as 1.5.

[0067] In this embodiment, the working space feasibility discrimination algorithm can compare the working space of the robot with the working space required by the actual application scenario, and output the result of the feasibility judgment. The working space required by the actual application scenario is referred to as the target working space. The target working space is mostly an irregular geometric body, which is generally abstracted into a regular geometric body, such as a cuboid, a cylinder, a sphere, etc. For example, a large propeller blade can be replaced by a cuboid. The feasibility of the robot working space can be judged by whether the characteristic cross section of the robot working space completely covers the characteristic cross section of the target working space.

[0068] The working space of the robot is an irregular three-dimensional space, and the target space is a regular geometric body. Therefore, the three-dimensional space envelope problem can be solved by changing it into a two-dimensional characteristic plane envelope problem. For a large propeller blade, a cuboid is used as the characteristic geometric body of the blade. For the case that the target working space is a cuboid, only the three planes Y=0, Z=d1 and Z=d2 in the robot working space need to be intercepted for judgment, where d1 is the upper surface of the target working space, and d2 is the lower surface of the target working space. The three cross sections are as shown inFigure 4 as shown.

[0069] Next, automatically determine whether the above three cross sections meet the envelope requirements, the specific steps are as follows:

[0070] For Y=0 cross section: from the right end of the cross section, that is, the point X with the maximum x coordinate max , record the point coordinates as (x max , z0), then find the point with the maximum x coordinate in the workspace within the range of z coordinates (z0-H / 2, z0+H / 2) and x coordinates less than x max -L / 2, record the value of the x coordinate of the point as x0, at this time the optimal position of the target workspace in the cross section can be determined, the coordinates of the four vertices are A(x0, z0+H / 2), B(x0, z0-H / 2), C(x0+L, z0-H / 2), D(x0+L, z0+H / 2), as shown in Figure 5 . Since the x coordinate values of points A and B are the x coordinate values of the concave points on the left profile of the workspace, the line segment AB must be contained in the robot workspace, while points C and D need to be judged. The judgment method is to find whether there are points with x coordinate x0+L / 2 whose y coordinates are less than the y coordinate of point C and greater than the y coordinate of point D. If there are points that meet the requirements, it means that the line segment CD is also contained in the robot workspace, which means that the rectangle formed by the four points ABCD is contained in the robot workspace. This cross section is judged to meet the requirements, otherwise it does not meet the requirements.

[0071] For Z=d1 and Z=d2 cross sections: the judgment method is the same as the above method steps. If the three cross sections meet the envelope requirements, it means that the target workspace is contained in the robot workspace, that is, the robot workspace meets the actual use requirements.

[0072] Next, consider the influence of the robot obstacle space: the end of the robot obstacle space cannot be reached in actual processing, so the blade to be processed cannot be placed in the robot obstacle space, that is, the target workspace and the robot obstacle space cannot coincide. The coincidence of the two spaces needs to be determined, the specific steps are as follows: from the cross section vertex coordinates of the obtained robot obstacle space and the vertex coordinates of the obtained target workspace, according to the shape characteristics of the two, only need to judge whether the B point in the target workspace is in the obstacle space at Y=0 cross section, that is, only need to judge whether the B point is directly below the straight line formed by GH, as shown in Figure 6 . The target space and obstacle space coincidence discrimination flow chart is shown in Figure 7 .

[0073] The workspace feasibility discrimination has two output results. If the workspace requirement is met, 1 is output, otherwise 0 is output. k is determined by the workspace feasibility discrimination. If the output is 1, k = 1; if the output of the algorithm is 0, k = 1.5.

[0074] In step four, a genetic algorithm is used to optimize the link parameters and branch chain parameters of the 2-RPR series robot. The fitness function of the genetic algorithm is:

[0075] f i '=max{f1,f2,…,f n}-f i +rand()i=1,2,…,n

[0076] In the formula, n is the population size, f i =k*(ω1a2+ω2a3+ω3d4+ω4c1+ω5e1), ω1, ω2, ω3, ω4, ω5 are the weights of the five optimization variables a2, a3, d4, c1, e1 corresponding to the population i.

[0077] In the embodiment, according to the aforementioned robot workspace feasibility discrimination method, the inclusion relationship of robots of different sizes and target workspaces of different sizes can be quickly judged, and whether the target workspace and the obstacle space are coincident can be detected, which provides convenience for the subsequent optimization of each parameter of the robot.

[0078] The genetic algorithm is a search algorithm inspired by the principle of natural evolution. By simulating the population reproduction, DNA replication, crossover, mutation and other phenomena in nature, and through natural selection of survival of the fittest, the population evolves to a better state. By optimizing the robot parameters through the genetic algorithm, under the premise of meeting the workspace requirements, a robot with more compact branch chain arrangement and more optimal link length can be obtained.

[0079] For a 2-RPR robot, there are three kinematic parameters that affect the robot workspace, namely a2, a3 and d4. There are five parameters that affect the robot obstacle space, namely the branch chain parameters c1, c2, e1, θ1 and θ2, wherein θ1 and θ2 are the joint rotation angles as invariants, and c2 does not affect the overall size of the robot and is only used for solving the obstacle space.

[0080] The genetic algorithm is used to optimize the above six parameters, including three kinematic parameters a2, a3 and d4, and three branch chain parameters c1, c2 and e1. The specific steps are as follows:

[0081] First, initialize the genetic algorithm parameters, as shown in Table 1.

[0082] Table 1 Genetic algorithm parameters

[0083]

[0084]

[0085] Then set the fitness function: for the parameter optimization of the robot, there is a premise that the target workspace requirements must be met, and the second optimization goal is to shorten the length of the connecting rod and adjust the electric cylinder arrangement. Therefore, when setting the fitness function, the balance between the two should be fully considered. How to determine whether the workspace under a set of robot parameters meets the requirements of the target workspace needs to use the above workspace judgment method, which will have two output results. If it meets the workspace requirements, it outputs 1, otherwise it outputs 0.

[0086] The fitness function is defined as follows:

[0087] f = k * (ω1a2 + ω2a3 + ω3d4 + ω4c1 + ω5e1) (4-1)

[0088] In the formula, ω1, ω2, ω3, ω4, ω5 are the weights of the five optimization quantities respectively, which can be adjusted to adjust the optimization strength of different parameters. There is also a parameter c2 that can be changed but not optimized. k is determined by the workspace feasibility judgment algorithm. If the output is 1, then k = 1; if the output is 0, then k = 1.5; in this way, the good and bad individuals can be well distinguished, and the fitness of the bad individual will be higher than that of the good individual.

[0089] The roulette selection is the most common selection method in genetic algorithm, its basic idea is: the probability of each individual being selected is proportional to its fitness value, that is, the genes of the individual with higher fitness are more likely to be inherited to the next generation, which conforms to the law of natural selection. The specific operation steps are as follows:

[0090] (1) Calculate the proportion of the fitness of each individual in the total fitness of the population, that is, the probability of being inherited to the next generation.

[0091] (2) Calculate the cumulative probability of each individual.

[0092] (3) Generate a random number, see which part of the cumulative probability the random number falls into, and then select.

[0093] The fitness values of different individuals should be significantly different, otherwise there is little difference between random selection. However, when optimizing the five parameters of the robot, since the robot is used to process large propeller blades, the values of each parameter are relatively large, and at this time the value of the fitness f will also be large, which will cause the difference between different individuals to be relatively weakened, and the advantage of roulette selection cannot be played. Therefore, the fitness function needs to be transformed as follows:

[0094] fi fmax{f1,f2,…,fn} - f1 n fmax{f1,f2,…,fn} - f2 i fmax{f1,f2,…,fn} - fn + rand() i=1,2,…,n (4-2)

[0095] In the formula, n is the population number. The above formula transforms the fitness values of all individuals, and selects the maximum fitness value from all individuals and subtracts the fitness of each individual. As can be seen from formula (4-1), the original fitness of a good individual is small, and after subtraction, a larger fitness value will be obtained, and the problem that the difference is not obvious due to the high magnitude of the fitness value is eliminated. After the subtraction, a small random amount is added, and the purpose is to retain the worst individual and give it a chance to inherit.

[0096] Each individual carries six genes, and the crossover and mutation operations are also applied to the six genes. By properly adjusting the probabilities of crossover and mutation and the degree of gene change during mutation, the genetic algorithm can converge faster.

[0097] The application further provides a parameter optimization design system of a 2-RPR serial robot, the system comprising a memory and a processor, the memory storing a computer program, and the processor executing the computer program to execute the parameter optimization design method of the 2-RPR serial robot.

[0098] The application further provides a computer readable storage medium, the computer readable storage medium storing machine executable instructions, the machine executable instructions causing the processor to implement the parameter optimization design method of the 2-RPR serial robot when the machine executable instructions are called and executed by the processor.

[0099] Those skilled in the art can easily understand that the above description is only a preferred embodiment of the application, and is not used to limit the application, and any modification, equivalent replacement and improvement made within the spirit and principle of the application should be included in the protection scope of the application.

Claims

1. A parameter optimization design method of a 2-RPR series robot, characterized in that, The method comprises the following steps: (1) sequentially connecting the coordinate points of the two joints of the 2-RPR serial robot, the lower hinge point of the electric cylinder, and the upper hinge points of the electric cylinder corresponding to the two limit positions to obtain the obstacle space of the robot; wherein the obstacle space of the robot is the movement region of the electric cylinder that the robot end can theoretically reach but is not allowed to reach in actual use; (2) judging whether the working space of the robot envelopes the target working space of the robot, and further judging whether the target working space of the robot coincides with the obstacle space of the robot, and determining the coefficient k according to the two judgment results; (3) optimizing the link parameters and branch chain parameters of the 2-RPR serial robot by using a genetic algorithm; wherein the fitness function of the genetic algorithm is: f i ' = max {f1, f2, …, fj} n ' = min {f1, f2, …, fj} i + rand() i = 1, 2, …, j where j is the population size, f i = k * (ω1a2+ ω2a3+ ω3d4+ ω4c1+ ω5e1), ω1, ω2, ω3, ω4, ω5 are the weights of the five optimization variables a2, a3, d4, c1, e1 corresponding to the population i; When judging whether the working space of the robot envelopes the target working space of the robot, it can be judged by intercepting the three planes Y=0, Z=d1 and Z=d2 in the working space of the robot; For Y=0 section: from the point at the right end of the section, i.e. the point X with the largest x coordinate max , record the point coordinate as (x max , z0), then find the point with the largest x coordinate among all points in the workspace with z coordinate in the range (z0-H / 2, z0+H / 2) and x coordinate less than x max -L / 2, record the value of x coordinate of this point as x0, at this time the optimal position of the target workspace in this section is determined, the coordinates of the four vertices are A(x0, z0+H / 2), B(x0, z0-H / 2), C(x0+L, z0-H / 2), D(x0+L, z0+H / 2), since the x coordinate values of points A and B are the x coordinate values of the concave points on the left contour of the workspace, therefore the line segment AB must be contained in the robot workspace, while for points C and D, it is necessary to determine whether there exist points in the workspace with x coordinate x0+L / 2 whose z coordinate is less than the z coordinate of point C and greater than the z coordinate of point D, if such points exist, it means that the line segment CD must also be contained in the robot workspace, which means that the rectangle formed by the four points ABCD is contained in the robot workspace.

2. The parameter optimization design method of the 2-RPR series robot according to claim 1, wherein: If the working space of the robot envelopes the target working space of the robot, and the target working space of the robot does not coincide with the obstacle space of the robot, then the coefficient k is determined as 1; otherwise, the coefficient k is determined as 1.

5.

3. The parameter optimization design method of the 2-RPR series robot according to claim 1, wherein: According to the shape characteristics of the cross-section vertex coordinates of the robot obstacle space and the vertex coordinates of the target working space, it is only necessary to judge whether the B point in the target working space is in the obstacle space at the Y=0 cross-section.

4. The parameter optimization design method of the 2-RPR series robot according to any one of claims 1-3, characterized in that: The transformation matrices of the links of the 2-RPR serial robot are sequentially multiplied to obtain the relative transformation matrix of the end link coordinate system n with respect to the base coordinate system 0, and then the random joint variables are substituted into the obtained relative transformation matrix to obtain the working space of the robot.

5. The parameter optimization design method of the 2-RPR series robot according to claim 4, wherein: According to the configuration of the 2-RPR serial robot, the coordinate systems of the links of the robot are established, the link parameters are determined according to the coordinate systems of the links, the transformation matrices of the links are sequentially multiplied to obtain the relative transformation matrix of the end link coordinate system n with respect to the polar coordinate system 0, and the random joint variables generated by the random function rand() are substituted into the obtained relative transformation matrix to obtain the coordinate set {P|P(x,y,z)} of the robot end, and then the working space of the robot is obtained.

6. The parameter optimization design method of the 2-RPR series robot according to claim 1, wherein: The random joint variables are generated by using the random function rand(): where q is the joint random variable; q max and q min are the upper and lower bounds of the joint angle, respectively; i = 1, …, n, and n is the number of joints of the robot.

7. A parameter optimization design system of a 2-RPR series robot, characterized in that: The system comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to execute the parameter optimization design method of the 2-RPR serial robot according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: The computer readable storage medium stores machine executable instructions, and when the machine executable instructions are called and executed by the processor, the machine executable instructions cause the processor to implement the parameter optimization design method of the 2-RPR serial robot according to any one of claims 1-6.

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