Mechanical arm parameter optimization method and device, equipment, storage medium and product

Abstract

The invention discloses a mechanical arm parameter optimization method and device, equipment, a storage medium and a product. The method comprises the steps that a kinematics model and a dynamics model of a target mechanical arm are established; determining optimizable parameters of the target mechanical arm; wherein the optimizable parameters comprise configuration parameters and model selection parameters; and an optimization objective function and optimization constraint conditions of the target mechanical arm are determined according to the optimizable parameters, and optimized mechanical arm parameters are obtained in combination with a target optimization algorithm. According to the mechanical arm parameter optimization method, the configuration parameters and the model selection parameters serve as the optimizable parameters of the target mechanical arm, the optimizable parameters are optimized in combination with the target optimization algorithm, and configuration and model selection parameter optimization of the mechanical arm in the design stage can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of industrial automation technology, and in particular to a robot arm parameter optimization method, device, equipment, storage medium and product. Background Art

[0002] In the field of industrial automation, robotic arms play a vital role, and their performance is directly related to the efficiency and precision of the production line.

[0003] In the prior art, there is no good means to select the configuration parameters and selection parameters of the robot arm during the design stage. The determination of the parameters often relies on the personal experience of engineers and simple kinematic simulations, resulting in the inability to achieve the optimal selection parameters of key components and the configuration parameters of the robot arm. Summary of the invention

[0004] The present invention provides a method, device, equipment, storage medium and product for optimizing parameters of a robot arm, so as to optimize the parameters of the robot arm.

[0005] According to one aspect of the present invention, a method for optimizing robot arm parameters is provided, comprising:

[0006] Establish the kinematic model and dynamic model of the target robotic arm;

[0007] Determine the optimizable parameters of the target robotic arm; wherein the optimizable parameters include configuration parameters and selection parameters;

[0008] The optimization objective function and optimization constraints of the target robotic arm are determined according to the optimizable parameters, and the optimized robotic arm parameters are obtained by combining the target optimization algorithm.

[0009] Further, determining the optimizable parameters of the target robotic arm includes:

[0010] Performing parameter analysis on the target robotic arm to obtain a table of key performance parameters;

[0011] The optimizable parameters are determined according to the key performance parameter table.

[0012] Furthermore, the target optimization algorithm includes a composite optimization algorithm.

[0013] Furthermore, combined with the target optimization algorithm, the optimized robot arm parameters are obtained, including:

[0014] Initializing the optimizable parameters to obtain at least two optimizable parameter groups;

[0015] Determine the best solution and the worst solution in the at least two optimizable parameter groups according to the optimization objective function;

[0016] Determine the centroid of the at least two optimizable parameter groups after removing the worst solution, and determine a mapping point according to the worst solution and the centroid, and if the solution corresponding to the mapping point satisfies the optimization constraint and the optimization objective function value is better than the worst solution, replace the worst solution with the solution corresponding to the mapping point;

[0017] Return to the step of determining the optimal solution and the worst solution in the at least two optimizable parameter groups according to the optimization objective function until the difference between the optimization objective function values ​​of the optimal solution and the worst solution is less than a preset value.

[0018] Furthermore, if the solution corresponding to the mapping point does not satisfy the optimization constraint condition, the mapping point is re-determined.

[0019] According to another aspect of the present invention, there is provided a robot arm parameter optimization device, comprising:

[0020] Model building module, used to build the kinematic model and dynamic model of the target robot arm;

[0021] An optimizable parameter determination module, used to determine the optimizable parameters of the target robotic arm; wherein the optimizable parameters include configuration parameters and selection parameters;

[0022] The parameter optimization module is used to determine the optimization objective function and optimization constraints of the target robotic arm according to the optimizable parameters, and obtain the optimized robotic arm parameters in combination with the target optimization algorithm.

[0023] Optionally, the optimizable parameter determination module is further used for:

[0024] Performing parameter analysis on the target robotic arm to obtain a table of key performance parameters;

[0025] The optimizable parameters are determined according to the key performance parameter table.

[0026] Optionally, the target optimization algorithm includes a composite optimization algorithm.

[0027] Optionally, the parameter optimization module is also used to:

[0028] Initializing the optimizable parameters to obtain at least two optimizable parameter groups;

[0029] Determine the best solution and the worst solution in the at least two optimizable parameter groups according to the optimization objective function;

[0030] Determine the centroid of the at least two optimizable parameter groups after removing the worst solution, and determine a mapping point according to the worst solution and the centroid, and if the solution corresponding to the mapping point satisfies the optimization constraint and the optimization objective function value is better than the worst solution, replace the worst solution with the solution corresponding to the mapping point;

[0031] Return to the step of determining the optimal solution and the worst solution in the at least two optimizable parameter groups according to the optimization objective function until the difference between the optimization objective function values ​​of the optimal solution and the worst solution is less than a preset value.

[0032] Optionally, the parameter optimization module is further used to redetermine the mapping point if the solution corresponding to the mapping point does not satisfy the optimization constraint condition.

[0033] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0034] at least one processor; and

[0035] a memory communicatively connected to the at least one processor; wherein,

[0036] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the robot arm parameter optimization method described in any embodiment of the present invention.

[0037] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the robot arm parameter optimization method described in any embodiment of the present invention when executed.

[0038] According to another aspect of the present invention, a computer program product is provided, which includes a computer program / instructions, and when the computer program / instructions are executed by a processor, the steps of the robot arm parameter optimization method described in any embodiment of the present invention are implemented.

[0039] The robot arm parameter optimization method disclosed in the present invention first establishes a kinematic model and a dynamic model of a target robot arm; then determines the optimizable parameters of the target robot arm; wherein the optimizable parameters include configuration parameters and selection parameters; finally, the optimization objective function and optimization constraints of the target robot arm are determined according to the optimizable parameters, and the optimized robot arm parameters are obtained in combination with a target optimization algorithm. The robot arm parameter optimization method disclosed in the present invention optimizes the optimizable parameters by taking the configuration parameters and the selection parameters as the optimizable parameters of the target robot arm and optimizing the optimizable parameters in combination with a target optimization algorithm, thereby realizing the optimization of the configuration and selection parameters of the robot arm in the design stage.

[0040] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 is a flow chart of a method for optimizing robot arm parameters provided according to Embodiment 1 of the present invention;

[0043] Figure 2 is a flow chart of a method for optimizing robot arm parameters provided according to Embodiment 2 of the present invention;

[0044] Figure 3 is a flow chart of a composite optimization algorithm provided according to the second embodiment of the present invention;

[0045] Figure 4 is a structural schematic diagram of a robot arm parameter optimization device provided according to Embodiment 3 of the present invention;

[0046] Figure 5 It is a structural schematic diagram of an electronic device for implementing the robot arm parameter optimization method of embodiment 4 of the present invention. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0048] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0049] Embodiment 1

[0050] Figure 1 This is a flow chart of a method for optimizing the parameters of a robotic arm provided in the first embodiment of the present invention. This embodiment is applicable to the case of optimizing the parameters of a robotic arm. The method can be executed by a robotic arm parameter optimization device. The robotic arm parameter optimization device can be implemented in the form of hardware and / or software. The robotic arm parameter optimization device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0051] S110. Establish a kinematic model and a dynamic model of the target robotic arm.

[0052] The target robot arm is a robot arm that needs to be optimized in parameters during the implementation of the present invention, and the kinematic model and the dynamic model are mathematical models established according to the physical structure and dynamic principles of the target robot arm, respectively.

[0053] In this embodiment, the target robot arm can accurately determine the position and posture of the end of the robot arm by means of the coordinated operation of multiple joints. The kinematic model is the core element, which builds a bridge between the joint variables and the position and posture of the end effector. The mathematical expression is:

[0054] P=f(DH(X),θ)

[0055] Where P is the end position of the robot arm; f is the end position function of the robot arm; DH is the parameter information, including the configuration information of the target robot arm, such as the key elements such as the link length, link torsion angle, joint distance and joint rotation angle; X is the list of optimizable parameters of the target robot arm, and θ represents the joint angle of the robot arm.

[0056] Furthermore, the joints of the target robot arm are closely connected by connecting rods to form a complex dynamic system. Based on the Lagrange equation or the Newton-Euler method, the force and torque transmission relationship between the connecting rods can be deeply analyzed. Considering the mass distribution characteristics of the connecting rod, the inertia tensor, and the friction torque at the joint, the dynamic model describing the relationship between the driving torque of the robot arm joint and the joint acceleration, velocity, and displacement is established as follows:

[0057]

[0058] Among them, M(X, θ) is the parameterized manipulator inertia matrix, is the parameterized Coriolis force matrix of the robot arm, G(X, θ) is the gravity matrix, X is the optimizable parameter list of the target robot arm, θ is the joint angle of the robot arm, and H is the driving torque of the robot arm joint.

[0059] S120: Determine optimizable parameters of the target robotic arm.

[0060] Among them, the parameters that can be optimized include configuration parameters and selection parameters. Configuration parameters are mainly DH parameters, including the configuration information of the target robot arm, such as key elements such as connecting rod length, connecting rod torsion angle, joint distance, and joint rotation angle; selection parameters are mainly motor number and reducer number.

[0061] In this embodiment, since there are many parameters involved in the robot arm, optimizable parameters that need to be optimized can be selected from all the parameters and represented by X.

[0062] S130, determining the optimization objective function and optimization constraints of the target robotic arm according to the optimizable parameters, and obtaining optimized robotic arm parameters in combination with the target optimization algorithm.

[0063] In this embodiment, from the perspective of energy consumption, the power F of the robot under the test condition can be used as the objective function. Assuming that the test data set of the test condition is w, the optimization objective function expression is as follows:

[0064]

[0065] Among them, τ(X, i) is the joint torque under the test condition, ν(X, i) is the joint speed under the test condition, X is the optimizable parameter, i represents the joint number of the robot arm, n represents the degree of freedom of the robot arm, and w represents the test data set under the test condition.

[0066] Furthermore, the optimization constraints of the target manipulator include kinematic constraints and dynamic constraints. First, the kinematic constraints of the target manipulator are constructed:

[0067] The Global Conditioning Index (GCI) of a robotic arm is an important quantitative indicator used to evaluate the kinematic performance, operational flexibility and singular configuration avoidance capabilities of the robotic arm. When the robotic arm performs complex trajectory tasks, each joint needs to rotate and extend in precise proportional relationships. The higher the GCI value, the more tacit the joint coordination is, and the preset path can be tracked more smoothly, reducing conditions such as jamming and tremors that affect the accuracy of the operation. A robotic arm with good GCI performance has a more "full" workspace and will not have large areas of unreachable "dead spots"; in addition, in complex scene applications, the robotic arm must avoid surrounding obstacles to complete the task. A robotic arm with a higher GCI value can plan a better obstacle avoidance path. Therefore, the kinematic constraints of the target robotic arm can be achieved by limiting the minimum GCI value. The expression of GCI is as follows:

[0068]

[0069] Where w is the test data set under the test condition, K is the condition number, Where J(θ,X) is the Jacobian matrix of the parameterized robot, X is the list of optimizable parameters, and θ is the joint angle of the robot.

[0070] Therefore, the kinematic constraints of the target manipulator can be described as:

[0071] GCI>GCI(min)

[0072] GCI(min) is the minimum value of GCI, which can be confirmed according to actual working conditions.

[0073] Furthermore, the dynamic constraints of the target manipulator are constructed:

[0074] The dynamic model of the target manipulator is shown in S110. According to the joint speed constraints, joint torque constraints, reducer speed, and reducer torque constraints of the manipulator, it can be confirmed whether the joint speed, joint torque, reducer speed, and reducer torque of the parameterized manipulator are within the constraint range under typical working conditions. The dynamic constraint conditions can be described as:

[0075]

[0076] τ m (X) <T max (X)

[0077]

[0078] τ mg (X) <T max_g (X)

[0079] In the formula, Represents the speed of the mth joint motor, V max (X) represents the maximum speed allowed by the motor currently in use (obtained through the motor number), τ m (X) represents the torque of the mth joint motor, T max (X) indicates the maximum torque allowed by the current motor. Indicates the speed of the mth reducer, V max_g (X) indicates the maximum speed allowed by the currently used reducer (obtained through the reducer number), τ mg (X) represents the torque of the mth reducer, T max_g (X) indicates the maximum torque allowed by the currently used reducer (obtained through the reducer number).

[0080] Furthermore, after determining the optimization objective function and optimization constraints of the target robot arm, the optimized robot arm parameters can be calculated in combination with the target optimization algorithm. Since robot arm parameter optimization is a multivariable, nonlinear complex optimization problem, and the optimizable parameter X of the target robot arm is a non-continuous parameter, it is impossible to use a gradient optimization algorithm to solve it. Preferably, a composite optimization algorithm, a genetic algorithm, or other related algorithms can be used to optimize and solve the parameters of the target robot arm.

[0081] The robot arm parameter optimization method disclosed in the present invention first establishes a kinematic model and a dynamic model of a target robot arm, then determines the optimizable parameters of the target robot arm, wherein the optimizable parameters include configuration parameters and selection parameters, and finally determines the optimization objective function and optimization constraints of the target robot arm according to the optimizable parameters, and obtains the optimized robot arm parameters in combination with a target optimization algorithm. The robot arm parameter optimization method disclosed in the present invention optimizes the optimizable parameters by taking the configuration parameters and the selection parameters as the optimizable parameters of the target robot arm and optimizing the optimizable parameters in combination with a target optimization algorithm, thereby realizing the optimization of the configuration and selection parameters of the robot arm in the design stage.

[0082] Embodiment 2

[0083] Figure 2 Flow chart of a method for optimizing robot arm parameters provided in Embodiment 2 of the present invention. This embodiment is a refinement of the above embodiment. Figure 2 As shown, the method includes:

[0084] S210. Establish a kinematic model and a dynamic model of the target robotic arm.

[0085] The target robot arm is a robot arm that needs to be optimized in parameters during the implementation of the present invention, and the kinematic model and the dynamic model are mathematical models established according to the physical structure and dynamic principles of the target robot arm, respectively.

[0086] In this embodiment, the target robot arm can accurately determine the position and posture of the end of the robot arm by means of the coordinated operation of multiple joints. The kinematic model is the core element, which builds a bridge between the joint variables and the position and posture of the end effector. The mathematical expression is:

[0087] P=f(DH(X),θ)

[0088] Where P is the end position of the robot arm; f is the end position function of the robot arm; DH is the parameter information, including the configuration information of the target robot arm, such as the key elements such as the link length, link torsion angle, joint distance and joint rotation angle; X is the list of optimizable parameters of the target robot arm, and θ represents the joint angle of the robot arm.

[0089] Furthermore, the joints of the target robot arm are closely connected by connecting rods to form a complex dynamic system. Based on the Lagrange equation or the Newton-Euler method, the force and torque transmission relationship between the connecting rods can be deeply analyzed. Considering the mass distribution characteristics of the connecting rod, the inertia tensor, and the friction torque at the joint, the dynamic model describing the relationship between the driving torque of the robot arm joint and the joint acceleration, velocity, and displacement is established as follows:

[0090]

[0091] Among them, M(X, θ) is the parameterized manipulator inertia matrix, is the parameterized Coriolis force matrix of the robot arm, G(X, θ) is the parameterized gravity matrix of the robot arm, X is the optimizable parameter list of the target robot arm, and H is the driving torque of the robot arm joint.

[0092] S220, performing parameter analysis on the target robotic arm to obtain a key performance parameter table.

[0093] In this embodiment, by combing the key performance parameters of the motors and reducers equipped in each joint of the robot arm, a structured table can be formed, and the motors and reducers are labeled to facilitate programming. The motor parameters include rated power, rated torque, peak torque, rated speed, maximum speed, etc.; the reducer parameters mainly consider the reduction ratio, rated output torque, maximum input speed, transmission efficiency, etc.

[0094] S230. Determine optimizable parameters according to the key performance parameter table.

[0095] Among them, the optimizable parameters include configuration parameters and selection parameters.

[0096] In this embodiment, taking into account the complexity of the robotic arm model, the optimizable parameters can be divided into configuration parameters and selection parameters, where the configuration parameters are mainly DH parameters, including the configuration information of the target robotic arm, such as connecting rod length, connecting rod torsion angle, joint distance, joint rotation angle and other key elements; the selection parameters are mainly motor number and reducer number.

[0097] Preferably, the rod length list A, rod offset list D, joint number list MI, and reducer number list GI in the DH parameters can be selected as optimizable parameters. The optimizable parameters are represented by X, then:

[0098] X=[A,D,MI,GI]

[0099] S240, determining the optimization objective function and optimization constraints of the target robotic arm according to the optimizable parameters, and obtaining optimized robotic arm parameters in combination with the target optimization algorithm.

[0100] In this embodiment, the optimization objective function of the target robot arm can be

[0101]

[0102] Among them, τ(X, i) is the joint torque under the test condition, v(X, i) is the joint velocity under the test condition, X is the optimizable parameter, i represents the joint number of the robot arm, n represents the degree of freedom of the robot arm, and w represents the test data set under the test condition.

[0103] The optimization constraints of the target robotic arm may include kinematic constraints and dynamic constraints. After determining the optimization objective function and optimization constraints of the target robotic arm, the optimized robotic arm parameters may be calculated in combination with the target optimization algorithm.

[0104] Among them, the target optimization algorithm includes the composite optimization algorithm. The composite algorithm is an optimization algorithm that is mainly used to solve nonlinear optimization problems, especially for low-dimensional engineering optimization problems. The basic idea is to gradually replace the worst solution in the composite through continuous iteration until the composite shrinks to the specified size and then terminate the algorithm. The composite algorithm is a non-gradient method that does not require the calculation of the derivative of the objective function, nor does it require an explicit analytical expression of the optimization objective or constraint conditions. It generates multiple points (usually more than 10 times the number of design variables), finds the worst point in each iteration, and maps it relative to the center of the remaining points. If the objective function of the mapped point is worse or does not meet the constraints, the mapping distance is reduced until the global optimal solution is found.

[0105] Optionally, in combination with the composite optimization algorithm, the method for obtaining the optimized robot arm parameters may be: initializing the optimizable parameters to obtain at least two optimizable parameter groups; determining the optimal solution and the worst solution in at least two optimizable parameter groups according to the optimization objective function; determining the centroid of at least two optimizable parameter groups after removing the worst solution, and determining the mapping point according to the worst solution and the centroid, if the solution corresponding to the mapping point satisfies the optimization constraints and the optimization objective function value is better than the worst solution, then replacing the worst solution with the solution corresponding to the mapping point; if the solution corresponding to the mapping point does not meet the optimization constraints, then redetermining the mapping point; returning to execute the step of determining the optimal solution and the worst solution in at least two optimizable parameter groups according to the optimization objective function until the difference between the optimization objective function values ​​of the optimal solution and the worst solution is less than a preset value.

[0106] Specifically, combined with the composite shape optimization algorithm, by initializing the feasible solution, multiple vertices can be generated in the feasible solution area to form a composite shape, and the quality of each vertex can be evaluated according to the objective function value, and the composite shape vertices are continuously iterated and updated in the direction of a better objective function value. Using operation strategies such as reflection, expansion, contraction, and compression, the composite shape is gradually approached to the global optimal solution, and finally the combination of robot arm design parameters that meets all constraints and optimizes the objective function is output. Figure 3 A flow chart of a composite optimization algorithm provided by an embodiment of the present invention is shown in the figure, and the specific algorithm steps are as follows:

[0107] Step 1: Randomly initialize the target robot arm to optimize the parameters and build the kinematic model and dynamic model of the target robot arm;

[0108] Step 2: Determine whether the optimizable parameters obtained by random initialization meet the optimization constraints. If so, execute Step 3, otherwise return to Step 1;

[0109] Step 3: Add the optimizable parameter groups that meet the optimization constraints to the initialization list;

[0110] Step 4: Determine whether the optimizable parameter groups in the initialization list have reached the specified number. If so, execute Step 5, otherwise return to Step 1;

[0111] Step 5: Calculate the objective function of each set of optimizable parameters in the initialization list, and find the set of parameters with the smallest and largest objective function values, denoted as Xb and Xw respectively;

[0112] Step 6: Determine whether the difference between the objective function value calculated by Xb and the objective function value calculated by Xw is small enough. If so, complete the iteration and take the optimization parameter as Xb. Otherwise, execute Step 7.

[0113] Step 7: Eliminate the group of parameters Xw with the largest objective function, calculate the centroid of the remaining parameter groups in the initialization list, map Xw relative to the centroid, and calculate a new group of parameters Xcand;

[0114] Xcand=Xc+alpha*(Xc-Xw)

[0115] Where Xc is the centroid of the remaining parameter group calculated after removing Xw from the initialization list, and alpha is an empirical coefficient generally taken as 1.3;

[0116] Step 8: Confirm whether the new parameter group Xcand meets the optimization constraint conditions of the target robot arm. If so, execute Step 9, otherwise execute Step 10;

[0117] Step 9: Confirm whether the objective function value of the current Xcand is better than Xw. If so, execute Step 11, otherwise execute Step 10;

[0118] Step 10: Recalculate Xcand and return to execute Step 8;

[0119] Xcand'=Xc+0.5*alpha*(Xc-Xcand)+beta*0.5*(Xb-Xc)

[0120] Where Xcand' is the recalculated parameter group, Xc is the centroid of the initialization list, alpha is the empirical coefficient, and beta is a random value;

[0121] Step 11: Add Xcand to the parameterized list and replace Xw;

[0122] Step 12: Return to execute Step 5.

[0123] The robot arm parameter optimization method provided in an embodiment of the present invention optimizes the configuration and selection parameters of the robot arm in the design stage by taking the configuration parameters and selection parameters as the optimizable parameters of the target robot arm and taking the composite optimization algorithm as the target optimization algorithm to optimize the optimizable parameters.

[0124] Embodiment 3

[0125] Figure 4 A schematic diagram of the structure of a robot arm parameter optimization device provided in Embodiment 3 of the present invention is shown in FIG. Figure 4 As shown, the device includes: a model building module 310, an optimizable parameter determination module 320 and a parameter optimization module 330.

[0126] The model building module 310 is used to build a kinematic model and a dynamic model of the target robot arm.

[0127] The optimizable parameter determination module 320 is used to determine the optimizable parameters of the target robotic arm.

[0128] Among them, the optimizable parameters include configuration parameters and selection parameters.

[0129] The parameter optimization module 330 is used to determine the optimization objective function and optimization constraints of the target robotic arm according to the optimizable parameters, and obtain the optimized robotic arm parameters in combination with the target optimization algorithm.

[0130] Optionally, the optimizable parameter determination module 320 is further used for:

[0131] Perform parameter analysis on the target robotic arm to obtain a key performance parameter table; determine optimizable parameters based on the key performance parameter table.

[0132] Optionally, the target optimization algorithm includes a composite optimization algorithm.

[0133] Optionally, the parameter optimization module 330 is further used for:

[0134] Initialize the optimizable parameters to obtain at least two optimizable parameter groups; determine the best solution and the worst solution in the at least two optimizable parameter groups according to the optimization objective function; determine the centroid of the at least two optimizable parameter groups after removing the worst solution, and determine the mapping point according to the worst solution and the centroid, if the solution corresponding to the mapping point meets the optimization constraint and the optimization objective function value is better than the worst solution, replace the worst solution with the solution corresponding to the mapping point; return to execute the step of determining the best solution and the worst solution in the at least two optimizable parameter groups according to the optimization objective function, until the difference between the optimization objective function values ​​of the best solution and the worst solution is less than a preset value.

[0135] Optionally, the parameter optimization module 330 is further configured to redetermine the mapping point if the solution corresponding to the mapping point does not satisfy the optimization constraint condition.

[0136] The robot arm parameter optimization device provided in the embodiment of the present invention can execute the robot arm parameter optimization method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0137] Embodiment 4

[0138] Figure 5A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0139] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0140] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0141] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a robotic arm parameter optimization method.

[0142] In some embodiments, the robot arm parameter optimization method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the robot arm parameter optimization described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the robot arm parameter optimization method in any other appropriate manner (e.g., by means of firmware).

[0143] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0144] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0145] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0146] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0147] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0148] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

Claims

1. A method for optimizing robot arm parameters, characterized in that: include: Establish the kinematic model and dynamic model of the target robotic arm; Determine the optimizable parameters of the target robotic arm; wherein the optimizable parameters include configuration parameters and selection parameters; The optimization objective function and optimization constraints of the target robotic arm are determined according to the optimizable parameters, and the optimized robotic arm parameters are obtained by combining the target optimization algorithm.

2. The method according to claim 1, characterized in that Determining optimizable parameters of the target robotic arm, including: Performing parameter analysis on the target robotic arm to obtain a table of key performance parameters; The optimizable parameters are determined according to the key performance parameter table.

3. The method according to claim 1, characterized in that: The target optimization algorithm includes a complex optimization algorithm.

4. The method according to claim 3, characterized in that Combined with the target optimization algorithm, the optimized robot arm parameters are obtained, including: Initializing the optimizable parameters to obtain at least two optimizable parameter groups; Determine the best solution and the worst solution in the at least two optimizable parameter groups according to the optimization objective function; Determine the centroid of the at least two optimizable parameter groups after removing the worst solution, and determine a mapping point according to the worst solution and the centroid, and if the solution corresponding to the mapping point satisfies the optimization constraint and the optimization objective function value is better than the worst solution, replace the worst solution with the solution corresponding to the mapping point; Return to the step of determining the optimal solution and the worst solution in the at least two optimizable parameter groups according to the optimization objective function until the difference between the optimization objective function values ​​of the optimal solution and the worst solution is less than a preset value.

5. The method according to claim 4, characterized in that If the solution corresponding to the mapping point does not satisfy the optimization constraint condition, the mapping point is re-determined.

6. A robot arm parameter optimization device, characterized in that: include: Model building module, used to build the kinematic model and dynamic model of the target robot arm; An optimizable parameter determination module, used to determine the optimizable parameters of the target robotic arm; wherein the optimizable parameters include configuration parameters and selection parameters; The parameter optimization module is used to determine the optimization objective function and optimization constraints of the target robotic arm according to the optimizable parameters, and obtain the optimized robotic arm parameters in combination with the target optimization algorithm.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the robot arm parameter optimization method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the robot arm parameter optimization method according to any one of claims 1 to 5 when executed.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the robot arm parameter optimization method according to any one of claims 1 to 5 are implemented.

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