Compensation method, system, device and medium for robot kinematic parameter error

Abstract

The application discloses a robot kinematics parameter error compensation method, system, device and medium, and the robot kinematics parameter error compensation method is characterized by the following steps: acquiring the error of a robot motion point; establishing a target function based on the error; and compensating the robot kinematics parameter error by using a quantum particle swarm algorithm based on the target function. The application adopts the quantum particle swarm algorithm, which is derived from quantum mechanics and makes particles have quantum behaviors, further increases the randomness of the particles, and is more likely to obtain a global optimum that is more in line with the expected result. Thus, the stability and accuracy of the compensation of the robot kinematics parameter error are improved, and the error of the robot motion point, i.e. the robot pose error, is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robots, and in particular to a robot kinematics parameter error compensation method, system, device and medium. BACKGROUND

[0002] With the development of technology, robots are widely used in various scenarios, and the accuracy requirements of robots are also getting higher and higher. At present, the main factor affecting the accuracy of robots is that the actual size of the connecting rod is different from the design value due to factors such as processing and deformation. This error has a great influence on the accuracy, so how to compensate for the error and correct the parameters through algorithms is the current research focus. With the development of optimization algorithms, the industry has gradually tended to apply intelligent algorithms to improve the accuracy of robots.

[0003] At present, the robot error is generally compensated by the following two methods.

[0004] The first method is to construct a robot model and calculate the theoretical pose of the robot according to the model. The end pose of the robot is collected by a device, and the error is obtained by subtracting the theoretical pose. The least squares method is used to find the minimum error, and the corrected parameters are obtained by repeatedly iterating to the minimum error. The parameters are substituted into the robot DH (connecting rod and joint) model for error compensation. This method is not robust and is easily affected by outliers. Secondly, when there are many characteristic values, the inverse operation will consume a lot of computing power.

[0005] The second method is to collect the data values of the robot end by a laser tracker and other devices, and to calculate the error based on these data. The industrial robot calculates the point position error of the target point in real time according to the trained artificial intelligence model, and realizes the compensation of the accuracy according to the calculated point position error of the target point. This method has the problem of unexplainability of the artificial intelligence model itself. The characteristic values extracted by the deep learning algorithm are usually better than those selected by humans, but this process is often uncontrollable, and this algorithm may select characteristic values in the wrong direction. The robustness of the artificial intelligence algorithm is also insufficient. Even if the input data has a slight disturbance during the training process, the model may give an incorrect result. Artificial intelligence algorithms rely heavily on data, and the process has some unclear parts. Obviously, this method is not suitable for this application scenario. SUMMARY

[0006] The technical problem to be solved by the present application is to overcome the defects of low stability and accuracy of robot error compensation in the prior art, and to provide a robot error compensation method, system, device and medium.

[0007] The present application solves the above technical problems by the following technical solutions:

[0008] The present application provides a robot kinematics parameter error compensation method, which comprises the following steps:

[0009] Obtaining errors of robot motion points;

[0010] Based on the errors, a target function is established;

[0011] Based on the target function, a quantum particle swarm algorithm is used to compensate for the robot kinematics parameter error.

[0012] Preferably, the step of establishing the target function comprises:

[0013] Obtaining the smallest error in the errors;

[0014] According to the smallest error, a target function is established.

[0015] Preferably, the step of using the quantum particle swarm algorithm to compensate for the robot kinematics parameter error comprises:

[0016] According to the target function, the fitness of a particle is determined;

[0017] Based on the fitness of the particle, the robot kinematics parameter error is compensated.

[0018] Preferably, the step of updating the original kinematics parameters based on the fitness of the particle to obtain target kinematics parameters comprises:

[0019] According to the fitness of the particle, the local optimal position of the particle is determined;

[0020] Based on the local optimal position, the global optimal position of the particle is determined;

[0021] According to the global optimal position, the particle position is updated;

[0022] Based on the updated particle position, the robot kinematics parameter error is compensated.

[0023] The present application also provides a robot kinematics parameter error compensation system, which comprises:

[0024] An obtaining module is configured to obtain errors of robot motion points;

[0025] A function establishing module is configured to establish a target function based on the errors;

[0026] A compensation module is configured to use a quantum particle swarm algorithm to compensate for the robot kinematics parameter error based on the target function.

[0027] Preferably, the function establishing module comprises:

[0028] an acquisition unit configured to acquire the smallest error in the errors;

[0029] a function establishment unit configured to establish a target function according to the smallest error.

[0030] Preferably, the compensation module comprises:

[0031] a determination unit configured to determine a particle fitness according to the target function;

[0032] a compensation unit configured to compensate for the robot kinematic parameter error based on the particle fitness.

[0033] Preferably, the compensation unit comprises:

[0034] a first determination subunit configured to determine a local optimal position of the particle according to the particle fitness;

[0035] a second determination subunit configured to determine a global optimal position of the particle based on the local optimal position;

[0036] an updating subunit configured to update the particle position according to the global optimal position;

[0037] a compensation subunit configured to compensate for the robot kinematic parameter error based on the updated particle position.

[0038] The application further provides an electronic device comprising a memory, a processor and a computer program stored in the memory and used for running on the processor, and the processor implements the compensation method for robot kinematic parameter error as described above when running the computer program.

[0039] The application further provides a computer readable storage medium having a computer program stored thereon, and the computer program implements the steps of the compensation method for robot kinematic parameter error as described above when executed by a processor.

[0040] On the basis of common knowledge in the art, the above-mentioned preferred conditions can be combined arbitrarily to obtain preferred examples of the application.

[0041] The positive progress effect of the application is that:

[0042] The present application, by acquiring the error of the robot motion point, establishes a target function, and based on the target function, adopts the quantum particle swarm algorithm to compensate for the robot kinematic parameter error. The present application adopts the quantum particle swarm algorithm, which is derived from quantum mechanics, makes the particles have quantum behavior, further increases the randomness of the particles, and is more likely to obtain a global optimal result that is more in line with the expected result. Thus, the stability and accuracy of the compensation for the robot kinematic parameter error are improved, and the error of the robot motion point, i.e. the robot pose error, is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The flowchart of the robot kinematic parameter error compensation method of the present application embodiment 1.

[0044] Figure 2 The flowchart of step S102 in the present application embodiment 1.

[0045] Figure 3 The flowchart of step S103 in the present application embodiment 1.

[0046] Figure 4 The flowchart of step S1032 in the present application embodiment 1.

[0047] Figure 5 The schematic diagram of the robot pose error in the present application embodiment 1.

[0048] Figure 6 The schematic diagram of the compensated robot pose error in the present application embodiment 1.

[0049] Figure 7 The module schematic diagram of the robot kinematic parameter error compensation system of the present application embodiment 2.

[0050] Figure 8 The module schematic diagram of the function establishing module 2 of the present application embodiment 2.

[0051] Figure 9 The module schematic diagram of the compensation module 3 in the present application embodiment 2.

[0052] Figure 10 The module schematic diagram of the compensation unit 32 in the present application embodiment 2.

[0053] Figure 11 The structural schematic diagram of the electronic device of the present application embodiment 3. DETAILED DESCRIPTION

[0054] The present application will be further described below by way of examples, but the present application is not limited in the scope of the described examples.

[0055] Embodiment 1

[0056] As Figure 1 shown, the embodiment of the application discloses a robot kinematics parameter error compensation method, the compensation method comprises:

[0057] Step S101, acquiring the error of the robot motion point;

[0058] Specifically, according to the test standard, a cube is selected in the robot workspace, the points on the inclined surface of the cube are taken as test points, the laser tracker records the motion trajectory of the robot end, and the error of the robot motion point is calculated;

[0059] The joint speed of the robot is connected to the Cartesian speed of the robot arm end by using the Jacobian:

[0060]

[0061] In the formula, v e is the linear speed of the robot arm end, J gv is the generalized Jacobian matrix of the robot arm, is the joint angular velocity of the robot.

[0062] Integrating the above formula obtains the end position at the end time:

[0063]

[0064] Then the error of the end position at the end time and the expected position, that is, the robot motion point error calculation formula is:

[0065]

[0066] Where, δp e represents the robot motion point error; p ef represents the end position at the end time; p ed represents the expected target position.

[0067] In order to observe the comparison results, the error data obtained is made into a chart, as Figure 3 shown.

[0068] Before acquiring the error of the robot motion point, the following steps can be performed:

[0069] First step: establishing the robot kinematics equation: that is, according to the DH model, the kinematics equation of the robot is established, and the right multiplication of four motion matrices can obtain The transformation matrix of the six connecting rods is obtained according to the following formula:

[0070]

[0071] where Rot(z i-1 , θ i ) denotes rotation about the z i-1 axis by θ i , Trans(z i-1 , d i ) denotes translation by d i-1 about the z i axis, Trans(x i , a i ) denotes translation by a i about the x i axis, and Rot(x i , α i ) denotes rotation about the x i axis by α i .

[0072]

[0073] where cθ i denotes cosθ i , sθ i denotes sinθ i , cα i denotes cosα i , sα i denotes sinα i , θ i is the joint angle between coordinate axes x i-1 and x i , d i is the link distance, i.e., the directed distance along the z i-1 axis between coordinate axes x i-1 and x i , a i is the link length, i.e., the directed distance along the x i axis between coordinate axes z i-1 and z i , and α i is the joint twist angle, i.e., the angle between coordinate axes z i-1 and z i .

[0074] The transformation matrix of the end joint relative to the base coordinate is obtained by successively multiplying the six link matrices

[0075]

[0076] n x , n y , n z denote the direction vectors of the x axis of the end effector coordinate system in the base coordinate system, o x , o y , oz represents a direction vector of the y-axis of the end effector in the base coordinate system, a x , a y , a z represents a direction vector of the z-axis of the end effector in the base coordinate system, p x , p y , p z represents a position of the end effector.

[0077] The above formula is expanded to obtain a robot kinematics equation:

[0078]

[0079] C5 represents cosθ5, S5 represents sinθ5, c 234 represents cos(θ2+θ3+θ4), d and a are DH parameters, d is a link distance, and a is a link length.

[0080] Second step: build a robot end position acquisition system, that is, operate a laser tracker to calibrate coordinates, so that the coordinates of the robot end and the laser tracker are unified.

[0081] Step S102, based on the error, a target function is established;

[0082] Step S103, based on the target function, a quantum particle swarm algorithm is used to compensate for the kinematic parameter error of the robot.

[0083] In the scheme, the error of the robot motion point is obtained, a target function is established, and based on the target function, a quantum particle swarm algorithm is used to compensate for the kinematic parameter error of the robot. The quantum particle swarm algorithm is used in the application, which is derived from quantum mechanics and makes the particles have quantum behavior, further increases the randomness of the particles, and is more likely to obtain a global optimal result that meets the expectation. Thus, the stability and accuracy of the compensation for the kinematic parameter error of the robot are improved, and the error of the robot motion point, i.e., the robot pose error, is reduced.

[0084] As shown in FIG. Figure 2 In an implementable manner, step S102 includes:

[0085] Step S1021, obtaining the smallest error in the error;

[0086] Step S1022, establishing a target function according to the smallest error.

[0087] In the scheme, the target function is established according to the minimum error principle, and the specific target function is as follows:

[0088]

[0089] wherein δp e represents the error between the desired and actual; k p represents a weighting coefficient determined by the control accuracy of the end of the mechanical arm; f(a) represents a representation form of parameterization of the robot trajectory, and f(a)=δp in the objective function e ;

[0090] f(a) is a parameterized form of the error, assuming that we know the robot trajectory is a quintic polynomial

[0091] q i (t) = a i0 +a i1 t+a i2 t 2 +a i3 t 3 +a i4 t 4 +a i5 t 5

[0092] According to the constraint, the coefficients of the polynomial are:

[0093]

[0094] Then, the derivation and second-order derivation thereof can obtain the joint angle, joint angular velocity, and joint angular acceleration:

[0095]

[0096]

[0097]

[0098] Then, the joint angle, angular velocity, and angular acceleration can be represented by the parameter a: a = [a 15 a 25 … a n5 ] T

[0099] Then, the parameterized form f(a) of the robot trajectory can be obtained.

[0100] As Figure 3 shown in an implementable manner, the step 103 comprises:

[0101] Step S1031, determining the particle fitness according to the objective function;

[0102] Step S1032, compensating for the robot kinematic parameter error based on the particle fitness.

[0103] In the scheme, the error of the motion point of the robot, i.e., the pose error δp e is caused by the inaccurate kinematic parameters of the robot. Assuming that the original kinematic parameters of the robot are a matrix H, after performing fitness calculation with the minimum error as the target, a set of more accurate kinematic parameters of the robot can be obtained.

[0104] In the scheme, the kinematic parameter error of the robot is compensated based on the particle fitness, the stability and accuracy of compensating the kinematic parameter error of the robot are improved, and the error of the motion point of the robot, i.e., the pose error of the robot, is reduced.

[0105] As Figure 4 shown in an implementable manner, the step S1032 of compensating the kinematic parameter error of the robot based on the particle fitness includes:

[0106] Step S10321, determining the local optimal position of the particle according to the particle fitness;

[0107] updating the local optimal position of the particle according to the particle fitness

[0108]

[0109] wherein, X i (t) is the position vector of each particle J in this place is modified as f represents the fitness of P i (t) and X i (t), and the local optimal position of the particle is determined according to different situations.

[0110] Step S10322, determining the global optimal position of the particle based on the local optimal position;

[0111] The global optimal position of the particle is determined based on the local optimal position as follows:

[0112] P g (t+1) = max{p1(t+1) + p2(t+1) + … + p N (t+1)}

[0113] Step S10323, updating the particle position according to the global optimal position;

[0114] Specifically, the formula for updating the particle position is as follows:

[0115]

[0116] PP ij (t+1) = f ij (t+1) × PP ij (t) + (1-fij (t+1))×P gj (t)

[0117]

[0118] Where M is the particle dimension, N is the total number of particles (this parameter is a set value), and m best This represents the average optimal position of a particle in the particle swarm at that moment.

[0119] In this scheme, the preset number of iterations is N. If the number of iterations has not reached N, the process returns to the step of establishing the objective function based on the minimum error, until the number of iterations reaches N.

[0120] Step S10324: Based on the updated particle positions, compensate for the errors in the robot's kinematic parameters.

[0121] This scheme updates the local and global optimal positions of particles based on the particle fitness, thereby updating the particle positions and compensating for robot kinematic parameter errors. This improves the stability and accuracy of compensating for robot kinematic parameter errors, and thus reduces the error of robot motion points, i.e., robot pose error.

[0122] To better illustrate the effects of the present invention, its effectiveness was verified. For example, the corrected parameters were substituted into the DH model, and the robot was re-run on the test trajectory using a robot kinematics algorithm. The end-effector trajectory was collected by a laser tracker, and the resulting error data was plotted to obtain an appendix. Figure 5 .

[0123] Comparison Appendix Figure 5 , attached Figure 6 It can be seen that the appendix Figure 5 The maximum error was 2.6 mm, but after correction by this invention, the attached... Figure 6 The maximum error was reduced to 0.9 mm, which shows that the present invention has a good effect on reducing robot pose error.

[0124] Example 2

[0125] like Figure 7 As shown, this embodiment discloses a compensation system for robot kinematic parameter errors, the compensation system comprising:

[0126] Module 1 is used to acquire the error of the robot's motion points;

[0127] Specifically, according to the test standard, a cube is selected in the robot's workspace, and points on the inclined surface of the cube are taken as test points. The laser tracker records the motion trajectory of the robot's end effector and calculates the error of the robot's motion points.

[0128] Using Jacobi, the joint velocities of the robot are correlated with the Cartesian velocities of the robot's end effector:

[0129]

[0130] In the formula v e J is the linear velocity at the end effector of the robotic arm. gv For the generalized Jacobian matrix of the robotic arm, This represents the joint angular velocity.

[0131] Integrating the above equation yields the terminal position at the termination time:

[0132]

[0133] Wherein, δp e Characterizing the error of the robot's motion point; p ef p represents the terminal position at the termination time. ed Characterizes the desired target location.

[0134] The error between the end position and the desired position at the termination time, i.e., the error of the robot's motion point, is calculated using the following formula:

[0135]

[0136] To observe and compare the results, the obtained error data were plotted in a chart, such as... Figure 3 As shown.

[0137] Before obtaining the error of the robot's motion points, the following steps can be performed:

[0138] Step 1: Establish the robot's kinematic equations: Based on the DH model, establish the robot's kinematic equations, which can be obtained by right-multiplying by the four motion matrices. The transformation matrix of the six links is obtained from the following formula, and the kinematic equations are as follows:

[0139]

[0140] Among them, Rot(z) i-1 ,θ i ) indicates revolving around z i-1 Axis rotation θ i Trans(z) i-1 ,d i ) indicates revolving around z i-1 Axis translation d i Trans(x) i ,a i ) indicates revolving around x i Axis translation a i Rot(x) i ,α irepresents rotation of x i axis by α i .

[0141]

[0142] where cθ i represents cosθ i ; sθ i represents sinθ i ; cα i represents cosα i ; sα i represents sinα i ; θ i is the joint angle of the link, the angle between coordinate axis x i-1 and x i axis, d i is the link distance, the directed distance along z i-1 axis between coordinate axis x i and x i-1 axis, a i is the link length, the directed distance along x i-1 axis between coordinate axis z i and z i axis, α i is the link twist angle, the angle between coordinate axis z i-1 and z i axis.

[0143] The transformation matrix of the end joint relative to the base coordinate can be obtained by successively multiplying the six link matrices

[0144]

[0145] n x , n y , n z represent the direction vector of the x axis of the end effector coordinate system in the base coordinate system, o x , o y , o z represent the direction vector of the y axis of the end effector in the base coordinate system, a x , a y , a z represent the direction vector of the z axis of the end effector in the base coordinate system, p x , p y , p z represent the position of the end effector.

[0146] The robot kinematics equation is obtained by expanding the above equation:

[0147]

[0148] C5 represents cos θ5, S5 represents sin θ5, c 234 represents cos (θ2+θ3+θ4), d and a are DH parameters, d is a link distance, and a is a link length.

[0149] Second step: build a robot end position acquisition system, that is, operate a laser tracker to calibrate coordinates, so that the coordinates of the robot end and the laser tracker are unified.

[0150] The function establishing module 2 is configured to establish a target function based on the error.

[0151] The compensation module 3 is configured to compensate for the kinematic parameter error of the robot based on the target function by using a quantum particle swarm algorithm.

[0152] In the scheme, the error of a robot motion point is acquired, a target function is established, and the kinematic parameter error of the robot is compensated for based on the target function by using a quantum particle swarm algorithm. The quantum particle swarm algorithm is used in the scheme, the algorithm is derived from quantum mechanics, the particles have quantum behaviors, the randomness of the particles is further increased, a global optimal result that is more in line with expectations is more easily obtained, the stability and accuracy of the compensation for the kinematic parameter error of the robot are improved, and the error of the robot motion point, that is, the robot pose error, is reduced.

[0153] As shown in FIG. Figure 8 In an implementable manner, the function establishing module 2 includes:

[0154] The acquisition unit 21 is configured to acquire the minimum error in the error.

[0155] The function establishing unit 22 is configured to establish a target function according to the minimum error.

[0156] In the scheme, the target function is established according to the minimum error principle, and the specific target function is as follows:

[0157]

[0158] wherein, δp e represents the error between expectation and actuality; k p represents a weighting coefficient, which is determined by the control accuracy of the robot end; and f(a) represents the parameterized representation form of the robot trajectory, and f(a) = δp e in the target function.

[0159] f(a) is the parameterized form of the error, and assuming that the robot running trajectory is a five-order polynomial

[0160] q i (t) = a i0 +a i1 t+ai2 t 2 +a i3 t 3 +a i4 t 4 +a i5 t 5

[0161] The coefficients of the polynomial are solved according to the constraints:

[0162]

[0163] Then the joint angle, joint angular velocity and joint angular acceleration can be obtained by derivation and second-order derivation:

[0164]

[0165]

[0166]

[0167] The joint angle, angular velocity and angular acceleration can be represented by the parameter a: a = [a 15 a 25 … a n5 ] T

[0168] Then the parameterized form f(a) of the robot running trajectory can be obtained.

[0169] As Figure 9 shown in an implementable manner, the compensation module 3 comprises:

[0170] A determination unit 31 is configured to determine the particle fitness according to the target function.

[0171] A compensation unit 32 is configured to compensate the robot kinematics parameter error based on the particle fitness.

[0172] In the scheme, the error of the motion point of the robot, i.e., the pose error δp e is caused by the inaccurate kinematics parameters of the robot. After fitness calculation with the minimum error as the target, a set of more accurate robot kinematics parameters can be obtained.

[0173] In the scheme, the robot kinematics parameter error is compensated based on the particle fitness, which improves the stability and accuracy of the compensation of the robot kinematics parameter error, and further reduces the error of the motion point of the robot, i.e., the pose error of the robot.

[0174] As Figure 10As shown, in an implementable mode, the compensation unit 32 comprises:

[0175] The first determining sub-unit 321 is configured to determine the local optimal position of the particle according to the particle fitness.

[0176] The local optimal position of the particle is updated according to the particle fitness.

[0177]

[0178] Wherein, X i (t) the position vector of each particle J is modified as f, which represents the position of the particle in the contrast P i (t) and X i (t) fitness, according to different cases to determine the local optimal position of the particle.

[0179] The second determining sub-unit 322 is configured to determine the global optimal position of the particle based on the local optimal position.

[0180] The global optimal position of the particle is determined based on the local optimal position as follows:

[0181] P g (t+1) = max {p1(t+1) + p2(t+1) + … + p N (t+1)}

[0182] The updating sub-unit 323 is configured to update the particle position according to the global optimal position.

[0183] Specifically, the formula for updating the particle position is as follows:

[0184]

[0185] PP ij (t+1) = f ij (t+1) × PP ij (t) + (1-f ij (t+1)) × P gj (t)

[0186]

[0187] Wherein, M is the dimension of the particle, N is the total number of particles, the parameter is a set value, m best represents the average optimal position of the particle in the particle swarm at this moment.

[0188] In this scheme, N times of cyclic updates are preset, and if the number of cyclic updates does not reach N times, the step of establishing the target function according to the smallest error is returned to until the number of cycles reaches N times.

[0189] The compensation sub-unit 324 is configured to compensate for the kinematic parameter error of the robot based on the updated particle position.

[0190] According to the method, the local and global optimal positions of the particles are updated based on the particle fitness, so that the particle position is updated, the kinematic parameter error of the robot is compensated, the stability and accuracy of the compensation of the kinematic parameter error of the robot are improved, and the robot motion point error, i.e., the robot pose error, is reduced.

[0191] In order to better illustrate the effect of the present application, the effect of the present application is verified, for example, the parameters obtained by correction are substituted into the DH model, the robot is caused to run the test trajectory again through the robot kinematics algorithm, and the end trajectory is collected by the laser tracker, and the error data obtained is made into a graph to obtain FIG. 2. Figure 5 .

[0192] Comparing FIG. 2 with FIG. 3, Figure 5 , it can be seen that the maximum error in FIG. 3 is 2.6 mm, and the maximum error in FIG. 4 is reduced to 0.9 mm after correction by the present application, so that the present application has a good effect on reducing the robot pose error. Figure 6 Figure 5 Figure 6

[0193] Embodiment 3

[0194] Referring to FIG. 4, the present embodiment provides a structural schematic diagram of an electronic device, which comprises a processor 31, a memory 32, and a computer program stored in the memory 32 and capable of running on the processor 31, and the processor 31 implements the compensation method for the kinematic parameter error of the robot in embodiment 1 when executing the program. Figure 11 The electronic device 30 shown is merely an example, and should not bring any limitation to the function and use range of the embodiments of the present application. Figure 11

[0195] As shown in FIG. 4, the electronic device 30 can be in the form of a general computing device, for example, it can be a server device. The components of the electronic device 30 can include but are not limited to the above-mentioned at least one processor 31, the above-mentioned at least one memory 32, and a bus 33 connecting different system components including the memory 32 and the processor 31. Figure 11 The bus 33 includes a data bus, an address bus, and a control bus.

[0196] The memory 32 can include a volatile memory, for example, a random access memory (RAM) 321 and / or a cache memory 322, and can further include a read-only memory (ROM) 323.

[0197] The memory 32 can include a volatile memory, for example, a random access memory (RAM) 321 and / or a cache memory 322, and can further include a read-only memory (ROM) 323.​​​​

[0198] The memory 32 can also include a program / utility 325 having a set (at least one) of program modules 324, including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or a combination thereof, can include implementation of a network environment as in each of these examples or some combination thereof.

[0199] The processor 31 performs a variety of functions, including the functions of the robot kinematic parameter error compensation method in embodiment 1 of the present application, by executing computer program instructions stored in the memory 32.

[0200] The electronic device 30 can also communicate with one or more external devices 34 such as a keyboard or a pointing device, among others. Such communication can occur through Input / Output (I / O) interfaces 35. Still yet, such external devices 34 can also communicate over a network, such as a local area network (LAN), a wide area network (WAN), and / or the Internet, such as the Internet. As can be appreciated, the network connections shown are illustrative and other means of establishing a communications link between the electronic device 30 and a network can be employed. Still yet, the electronic device 30 can be a component of a larger system, which includes a plurality of electronic devices 30. For example, a system can include a plurality of electronic devices 30 connected by a network, such as a LAN, a WAN, or the Internet, among others, which can be configured to allow communication between the electronic devices 30 and various devices external to the system, such as database servers, internet service providers, and the like.

[0201] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the foregoing detailed description, such division is merely exemplary and not mandatory. Indeed, according to the embodiments of the present application, the features and functionalities of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functionalities of one unit / module described above can be further divided into a plurality of units / modules.

[0202] Embodiment 4

[0203] The present embodiment provides a computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the robot kinematic parameter error compensation method in embodiment 1.

[0204] More specifically, the computer readable storage medium can include, but is not limited to, portable discs, hard disks, random access memories, read-only memories, erasable programmable read-only memories, optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0205] In possible implementation manners, the present application can also be implemented in the form of a program product, which comprises program codes for causing a terminal device to execute the method for compensating for robot kinematics parameter errors in the embodiment 1 when the program product is run on the terminal device.

[0206] Wherein, the program codes for executing the present application can be written in any combination of one or more programming languages, and can be executed completely on the user device, partially on the user device, as a separate software package, partially on the user device and partially on a remote device, or completely on a remote device.

[0207] Although the specific implementation manners of the present application are described above, those skilled in the art should understand that this is only an example, and the protection scope of the present application is defined by the appended claims. Those skilled in the art can make various changes or modifications to these implementation manners without departing from the principles and essence of the present application, and these changes and modifications all fall within the protection scope of the present application.

Claims

1. A method of compensating for kinematic parameter errors of a robot, characterized in that, The method comprises the following steps: obtaining errors of robot motion points; wherein the robot motion points are points on any one of the cube slopes in the robot workspace according to a test standard; establishing a target function based on the errors; compensating for robot kinematic parameter errors by using a quantum particle swarm algorithm based on the target function; the step of establishing the target function comprises: obtaining the smallest error in the errors; establishing a target function according to the smallest error; the target function is as follows: where δp e represents the error between the desired and actual; k p represents a weighting coefficient, determined by the control accuracy of the end of the robot arm; f(a) represents the representation form of the parameterization of the robot trajectory, in the objective function f(a) = δp e .

2. The method of claim 1, wherein, the step of compensating for robot kinematic parameter errors by using a quantum particle swarm algorithm comprises: determining the fitness of a particle according to the target function; compensating for robot kinematic parameter errors based on the fitness of the particle.

3. The method of claim 2, wherein, the step of compensating for robot kinematic parameter errors based on the fitness of the particle comprises: determining the local optimal position of the particle according to the fitness of the particle; determining the global optimal position of the particle based on the local optimal position; updating the particle position according to the global optimal position; compensating for robot kinematic parameter errors based on the updated particle position.

4. A system for compensation of kinematic parameter errors of a robot, characterized in that The method comprises the following steps: an obtaining module is configured to obtain errors of robot motion points; wherein the robot motion points are points on any one of the cube slopes in the robot workspace according to a test standard; a function establishing module is configured to establish a target function based on the errors; a compensating module is configured to compensate for robot kinematic parameter errors by using a quantum particle swarm algorithm based on the target function; the function establishing module comprises: an obtaining unit is configured to obtain the smallest error in the errors; a function establishing unit is configured to establish a target function according to the smallest error; the target function is as follows: where δp e represents the error between the desired and actual; k p represents a weighting coefficient, determined by the control accuracy of the end of the robot arm; f(a) represents the representation form of the parameterization of the robot trajectory, in the objective function f(a) = δp e .

5. The system for compensation of robot kinematic parameter errors of claim 4, wherein, the compensating module comprises: a determining unit is configured to determine the fitness of a particle according to the target function; a compensating unit is configured to compensate for robot kinematic parameter errors based on the fitness of the particle.

6. The system for compensation of robot kinematic parameter errors of claim 5, wherein, the compensating unit comprises: a first determining subunit is configured to determine the local optimal position of the particle according to the fitness of the particle; a second determining subunit is configured to determine the global optimal position of the particle based on the local optimal position; an updating subunit is configured to update the particle position according to the global optimal position; a compensating subunit is configured to compensate for robot kinematic parameter errors based on the updated particle position.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory for running on the processor, characterized in that, The processor executes the computer program to implement the method for compensating for robot kinematic parameter errors according to any one of claims 1 to 3.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method for compensating for robot kinematic parameter errors according to any one of claims 1 to 3.

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