A method for controlling different levels of motion of redundant manipulators

Abstract

The present invention discloses a method for controlling different layers of motion of a redundant manipulator, including: collecting motion data of the redundant manipulator; configuring different layers of motion schemes in discrete time; obtaining the error functions defined for the joint angle layer at the first moment and the second moment respectively, and giving the equation relationship of the error functions at these two adjacent moments; based on the idea of ​​Taylor expansion, Taylor expansion is performed on the error function of the joint angle layer at the first moment at the second moment to obtain the equation relationship of the joint velocity layer at the second moment; and based on the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in the different layers of motion schemes, a discrete time recursive neural network algorithm is obtained for different layers of motion control of the redundant manipulator. The present invention improves the real-time motion of the redundant manipulator, realizes joint fault tolerance and direction maintenance of the end effector, and can be widely used in the field of manipulator control technology.

Description

Technical Field

[0001] The invention relates to the technical field of robot arm control, and in particular to a method for controlling different-layer motions of a redundant robot arm. Background Art

[0002] Redundant robotic arms are widely used in industrial production, medical, military, aerospace and other fields. They can complete tasks with high repetitiveness, high precision requirements and high risks, and improve production efficiency and safety. With the development of artificial intelligence and machine learning technology, the application field of robotic arms is also expanding, becoming an important part of future intelligent manufacturing and service robots.

[0003] The motion control of redundant manipulators usually uses a quadratic programming solver to calculate the optimal joint angles, but this method has some disadvantages, such as high computational complexity, large storage space requirements, and the possibility of local optimal solutions. In addition, when a joint of the manipulator is damaged or the direction of the end effector remains unchanged, the manipulator may not be able to complete the tracking task well during movement. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a different-layer motion control method for a redundant robotic arm to realize different-layer motion control of a redundant robotic arm, improve the real-time motion of the redundant robotic arm, and realize joint fault tolerance and direction maintenance of the end effector.

[0005] An aspect of an embodiment of the present invention provides a method for controlling motion of a redundant manipulator at different levels, including:

[0006] Collecting motion data of the redundant manipulator, wherein the motion data includes rod length, initial joint angle, and expected path;

[0007] Configure different layers of motion schemes in discrete time;

[0008] Obtain the error functions defined for the joint angle layer at the adjacent first moment and the second moment respectively, and give the equation relationship between the error functions of the two adjacent moments; wherein the first moment is the next moment adjacent to the second moment;

[0009] Based on the idea of ​​Taylor expansion, the error function of the joint angle layer at the first moment is Taylor expanded at the second moment to obtain an equation relationship of the joint velocity layer at the second moment;

[0010] Based on the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in different layers of motion schemes, the Euler forward difference formula is applied to derive a discrete-time recursive neural network algorithm for different layers of motion control of redundant manipulators.

[0011] Optionally, in the step of configuring the discrete-time different-layer motion schemes, the expression of the motion scheme is:

[0012]

[0013] Among them, r actual,k+1 is the actual position vector of the end effector; r desired,k+1 is the expected path of the end effector; A k+1 is a row-full-rank matrix; is the joint velocity vector of the robot; k is the update index; τ is the sampling interval; t final is the final time point of the entire calculation time interval; θ k+1 is the joint angle vector of the robot arm; b k+1 is a vector.

[0014] Optionally, obtaining the error functions defined for the joint angle layer at adjacent first and second moments respectively includes:

[0015] The first error function is defined for the joint angle layer at the first moment, and the expression of the first error function is:

[0016] e k+1 =f(θ k+1 ,t k+1 )=r actual,k+1 -r desired,k+1 ,

[0017] At the second moment, the second error function is defined for the joint angle layer. The expression of the second error function is:

[0018] e k =f(θ k ,t k )=r actual,k -r desired,k ,

[0019] Among them, e k+1 =(1-h)e k , h is the step size parameter; r actual,k+1 is the actual position vector of the end effector; r desired,k+1 is the desired path of the end effector; θ k+1 is the joint angle vector of the robot arm; t k+1 represents the first moment; t k Represents the second moment.

[0020] Optionally, based on the idea of ​​Taylor expansion, the error function of the joint angle layer at the first moment is Taylor expanded at the second moment to obtain the equation relationship of the joint velocity layer at the second moment. In this step, the expression of Taylor expansion is:

[0021]

[0022] The equation of the joint velocity layer at the second moment is expressed as:

[0023]

[0024] Among them, O(τ 2 ) is the truncation error; J(θ k ) is the Jacobian matrix of the robot, is the time derivative of the desired path of the end effector.

[0025] Optionally, the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in the different layers of motion schemes are applied to obtain a discrete time recursive neural network algorithm for different layers of motion control of the redundant manipulator, including:

[0026] The equation relationship of the joint velocity layer and the equation relationship of the joint velocity layer are combined to obtain a combined result;

[0027] According to the joint results, the discrete time recursive neural network algorithm is designed by applying Euler's forward difference formula;

[0028] The motion control of different layers of redundant manipulators is performed based on a discrete-time recursive neural network algorithm.

[0029] Optionally, the expression of the joint result is:

[0030]

[0031] The expression of the discrete time recurrent neural network algorithm is:

[0032]

[0033] Among them, the expression of Euler forward difference formula is:

[0034]

[0035] Where τ is the sampling interval; J(θ k ) is the Jacobian matrix of the robot; A k is a row-full-rank matrix; is the joint velocity vector of the robot; is the time derivative of the desired path of the end effector; h is the step size parameter; is the pseudo-inverse operator for matrices.

[0036] Another aspect of the embodiment of the present invention further provides a redundant robot arm motion control device at different levels, comprising:

[0037] The first module is used to collect motion data of the redundant manipulator, wherein the motion data includes rod length, initial joint angle, and expected path;

[0038] The second module is used to configure the different layer motion schemes in discrete time;

[0039] The third module is used to obtain the error functions defined for the joint angle layer at the adjacent first moment and the second moment respectively, and give the equation relationship between the error functions of the two adjacent moments; wherein the first moment is the next moment adjacent to the second moment;

[0040] The fourth module is used to perform Taylor expansion on the error function of the joint angle layer at the first moment at the second moment based on the idea of ​​Taylor expansion, so as to obtain an equation relationship of the joint velocity layer at the second moment;

[0041] The fifth module is used to apply the Euler forward difference formula based on the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in different layers of motion schemes, to derive a discrete-time recursive neural network algorithm for different layers of motion control of redundant robotic arms.

[0042] Another aspect of an embodiment of the present invention further provides an electronic device, including a processor and a memory;

[0043] The memory is used to store programs;

[0044] The processor executes the program to implement the method described above.

[0045] Another aspect of the embodiments of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the method described above.

[0046] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0047] The embodiment of the present invention first collects the motion data of the redundant manipulator, and the motion data includes the rod length, the initial joint angle, and the expected path; then configures the different layers of motion schemes in discrete time; then obtains the error functions defined for the joint angle layer at the adjacent first moment and the second moment, and gives the equation relationship of the error functions of the two adjacent moments; wherein the first moment is the next moment adjacent to the second moment; finally, based on the idea of ​​Taylor expansion, the error function of the joint angle layer of the first moment is Taylor expanded at the second moment to obtain the equation relationship of the joint velocity layer at the second moment; and based on the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in the different layers of motion schemes, the Euler forward difference formula is applied to obtain a discrete time recursive neural network algorithm for different layers of motion control of the redundant manipulator. The present invention can realize different layers of motion control of the redundant manipulator, improve the real-time motion of the redundant manipulator, and realize joint fault tolerance and direction maintenance of the end effector. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 is a flowchart of the overall steps of an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of a simplified model of a redundant mechanical arm of the present invention;

[0051] Figure 3 is a schematic diagram of the actual motion trajectory of the redundant manipulator of the present invention;

[0052] Figure 4 is a schematic diagram of the actual trajectory and the expected path of the redundant robot arm end effector of the present invention;

[0053] Figure 5 is a schematic diagram of the joint angle of the present invention;

[0054] Figure 6 is a schematic diagram of the sum of all joint angles of the present invention;

[0055] Figure 7 Schematic diagram of the tracking errors of the redundant manipulator in the X-axis direction and the Y-axis direction calculated by the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] In view of the problems existing in the prior art, an embodiment of the present invention provides a method for controlling motion of different layers of a redundant manipulator, comprising:

[0058] Collecting motion data of the redundant manipulator, wherein the motion data includes rod length, initial joint angle, and expected path;

[0059] Configure different layers of motion schemes in discrete time;

[0060] Obtain the error functions defined for the joint angle layer at the adjacent first moment and the second moment respectively, and give the equation relationship between the error functions of the two adjacent moments; wherein the first moment is the next moment adjacent to the second moment;

[0061] Based on the idea of ​​Taylor expansion, the error function of the joint angle layer at the first moment is Taylor expanded at the second moment to obtain an equation relationship of the joint velocity layer at the second moment;

[0062] Based on the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in different layers of motion schemes, the Euler forward difference formula is applied to derive a discrete-time recursive neural network algorithm for different layers of motion control of redundant manipulators.

[0063] Optionally, in the step of configuring the discrete-time different-layer motion schemes, the expression of the motion scheme is:

[0064]

[0065] Among them, r actual,k+1 is the actual position vector of the end effector; r desired,k+1 is the expected path of the end effector; A k+1 is a row-full-rank matrix; is the joint velocity vector of the robot; k is the update index; τ is the sampling interval; t final is the final time point of the entire calculation time interval; θ k+1 is the joint angle vector of the robot arm; b k+1 is a vector.

[0066] Optionally, obtaining the error functions defined for the joint angle layer at adjacent first and second moments respectively includes:

[0067] The first error function is defined for the joint angle layer at the first moment, and the expression of the first error function is:

[0068] e k+1 =f(θ k+1 ,t k+1 )=r actual,k+1 -r desired,k+1 ,

[0069] At the second moment, the second error function is defined for the joint angle layer. The expression of the second error function is:

[0070] e k =f(θ k ,t k )=r actual,k -r desired,k ,

[0071] Among them, e k+1 =(1-h)e k , h is the step size parameter; r actual,k+1 is the actual position vector of the end effector; r desired,k+1 is the desired path of the end effector; θ k+1 is the joint angle vector of the robot arm; t k+1 represents the first moment; t k Represents the second moment.

[0072] Optionally, based on the idea of ​​Taylor expansion, the error function of the joint angle layer at the first moment is Taylor expanded at the second moment to obtain the equation relationship of the joint velocity layer at the second moment. In this step, the expression of Taylor expansion is:

[0073]

[0074] Among them, O(τ 2 ) is the truncation error; J(θ k ) is the Jacobian matrix of the robot, is the time derivative of the desired path of the end effector;

[0075] The equation of the joint velocity layer at the second moment is expressed as:

[0076]

[0077] Among them, O(τ 2 ) is the truncation error; J(θ k ) is the Jacobian matrix of the robot, is the time derivative of the desired path of the end effector.

[0078] Optionally, the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in the different layers of motion schemes are applied to obtain a discrete time recursive neural network algorithm for different layers of motion control of the redundant manipulator, including:

[0079] The equation relationship of the joint velocity layer and the equation relationship of the joint velocity layer are combined to obtain a combined result;

[0080] According to the joint results, the discrete time recursive neural network algorithm is designed by applying Euler's forward difference formula;

[0081] The motion control of different layers of redundant manipulators is performed based on a discrete-time recursive neural network algorithm.

[0082] Optionally, the expression of the joint result is:

[0083]

[0084] The expression of the discrete time recurrent neural network algorithm is:

[0085]

[0086] Among them, the expression of Euler forward difference formula is:

[0087]

[0088] Where τ is the sampling interval; J(θ k ) is the Jacobian matrix of the robot; A k is a row-full-rank matrix; is the joint velocity vector of the robot; is the time derivative of the desired path of the end effector; h is the step size parameter; is the pseudo-inverse operator for matrices.

[0089] Another aspect of the embodiment of the present invention further provides a redundant robot arm motion control device at different levels, comprising:

[0090] The first module is used to collect motion data of the redundant manipulator, wherein the motion data includes rod length, initial joint angle, and expected path;

[0091] The second module is used to configure the different layer motion schemes in discrete time;

[0092] The third module is used to obtain the error functions defined for the joint angle layer at the adjacent first moment and the second moment respectively, and give the equation relationship between the error functions of the two adjacent moments; wherein the first moment is the next moment adjacent to the second moment;

[0093] The fourth module is used to perform Taylor expansion on the error function of the joint angle layer at the first moment at the second moment based on the idea of ​​Taylor expansion, so as to obtain an equation relationship of the joint velocity layer at the second moment;

[0094] The fifth module is used to apply the Euler forward difference formula based on the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in different layers of motion schemes, to derive a discrete-time recursive neural network algorithm for different layers of motion control of redundant robotic arms.

[0095] Another aspect of an embodiment of the present invention further provides an electronic device, including a processor and a memory;

[0096] The memory is used to store programs;

[0097] The processor executes the program to implement the method described above.

[0098] Another aspect of the embodiments of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the method described above.

[0099] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0100] The specific implementation process of the present invention is described in detail below in conjunction with the accompanying drawings:

[0101] Reference Figure 1 , showing a flow chart of a method for controlling motion at different levels of a redundant robotic arm provided by an embodiment of the present invention.

[0102] Wherein, as an example, the different-layer motion control method of the redundant manipulator may include:

[0103] S1. Collect motion data of redundant manipulators, including rod length, initial joint angle, expected path, etc.;

[0104] S2. Design discrete time different layer motion schemes;

[0105] S3. At t k+1 Time and t k Define the error function for the joint angle layer at each moment, and give the equation relationship between the two error functions;

[0106] S4. Based on the idea of ​​Taylor expansion, tk+1 The error function of the joint angle layer at time t k Taylor expansion is performed at all times, and then t k Equational relationship of joint velocity layer at each moment;

[0107] S5. Based on the t k The equation relationship between the joint velocity layers at each moment and the equation relationship between the joint velocity layers in different layers of motion schemes are applied, and Euler forward difference formula is applied. Finally, a discrete-time recursive neural network algorithm is designed and developed for different layers of motion control of redundant robotic arms.

[0108] Among them, at t k+1 =(k+1)τ∈[0,t final ] moment, the discrete time different layer motion scheme in step S2 is as follows:

[0109]

[0110] Where k is the update index, τ is the sampling interval, and t final is the final time point of the entire calculation time interval, θ k+1 is the joint angle vector of the robot arm, is the joint velocity vector of the robot arm, r actual,k+1 is the actual position vector of the end effector, r desired,k+1 is the desired path of the end effector, A k+1 is a full row rank matrix, b k+1 is a vector. Obviously, these two equations are at different levels. The first equation is related to the joint angle level, and the second equation is related to the joint velocity level.

[0111] Wherein, in step S3, at t k+1 Define the error function e for the joint angle layer at all times k+1 =f(θ k+1 ,t k+1 )=r actual,k+1 -r desired,k+1 , at t k Define the error function e for the joint angle layer at all times k =f(θ k ,t k )=r actual,k -r desired,k , and give the equation relationship between these two error functions, namely: e k+1 =(1-h)e k , where h is the step size parameter.

[0112] Optionally, in step S4, based on the idea of ​​Taylor expansion, t k+1 The error function e of the joint angle layer at the momentk+1 In t k Carry out Taylor expansion at all times, and we can get

[0113]

[0114] In the above formula, O(τ 2 ) is the truncation error. In addition, Where J(θ k ) is the Jacobian matrix of the robot, is the time derivative of the desired path of the end effector. k+1 =(1-h)e k Substituting into the above formula, we can get t k The equation relationship of the joint velocity layer at each moment is as follows:

[0115]

[0116] Optionally, in step S5, based on the equation relationship of the joint velocity layer in step S4 and t k The equation relationship of the joint velocity layer in step S2 at the moment is obtained by combining

[0117]

[0118] Apply Euler's forward difference formula Finally, a discrete-time recurrent neural network algorithm was designed and developed:

[0119]

[0120] in, is the pseudo-inverse operator for matrices.

[0121] Reference Figure 2 , showing a schematic diagram of a simplified model of a redundant robotic arm of the present invention.

[0122] When conducting simulation experiments, the relevant settings used are as follows: the expected path of the redundant robot end effector is set to a heart shape, and its expressions are as follows:

[0123]

[0124] Among them, ∈1 and ∈2 are parameters related to the initial position of the expected path; the task execution time is set to T = t final -0=30s; sampling interval τ=0.001s; step parameter h=0.1; the initial joint angle of the redundant manipulator is θ0=[π / 6,-π / 4,π / 6,-π / 3] T rad; each rod of the robot arm is 1m long; A k=[0,1,0,0;1,1,1,1];b k =[0;0], that is, A k and b k Remains unchanged at any one time.

[0125] Reference Figure 3 , shows the actual motion trajectory of the redundant manipulator, where the task performed by the end effector is to track a heart-shaped path.

[0126] Reference Figure 4 , shows the actual trajectory and expected path of the redundant manipulator end effector, where the solid line is the actual tracking trajectory of the end effector and the dotted line is the expected path. Figure 3 It can be seen that after a period of time, the actual trajectory and the expected path almost overlap.

[0127] Reference Figure 5 , shows the changes in the joint angles of the redundant robot arm, where the second joint angle remains basically unchanged, that is, the second joint angle is limited during the movement, indicating that the developed discrete-time recursive neural network algorithm has a certain joint fault tolerance ability.

[0128] Reference Figure 6 , which shows the change of the sum of all joint angles of the redundant robot arm. It can be seen that the sum of all joint angles remains basically unchanged, indicating that the developed discrete-time recursive neural network algorithm can realize the directional control of the end effector.

[0129] Reference Figure 7 , shows the tracking errors of the redundant manipulator in the X-axis and Y-axis directions. It can be seen that the maximum steady-state tracking errors in both directions are both 10 -5 On the order of m, the tracking accuracy is generally high.

[0130] In summary, the design method of the present invention is simple and effective, and improves the real-time performance of the redundant robot arm's movement, and achieves joint fault tolerance and direction maintenance of the end effector.

[0131] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0132] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0133] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0134] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0135] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0136] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0137] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0138] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0139] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for controlling motion of different layers of a redundant manipulator, characterized in that: include: Collecting motion data of the redundant manipulator, wherein the motion data includes rod length, initial joint angle, and expected path; Configure different layers of motion schemes in discrete time; Obtain the error functions defined for the joint angle layer at the adjacent first moment and the second moment respectively, and give the equation relationship between the error functions of the two adjacent moments; wherein the first moment is the next moment adjacent to the second moment; Based on the idea of ​​Taylor expansion, the error function of the joint angle layer at the first moment is Taylor expanded at the second moment to obtain an equation relationship of the joint velocity layer at the second moment; Based on the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in different layers of motion schemes, the Euler forward difference formula is applied to derive a discrete-time recursive neural network algorithm for different layers of motion control of redundant manipulators.

2. The method for controlling the motion of a redundant manipulator at different levels according to claim 1, characterized in that: In the step of configuring the discrete-time different-layer motion schemes, the expression of the motion scheme is: Among them, r actual,k+1 is the actual position vector of the end effector; r desired,k+1 is the expected path of the end effector; A k+1 is a row-full-rank matrix; is the joint velocity vector of the robot; k is the update index; τ is the sampling interval; t final is the final time point of the entire calculation time interval; θ k+1 is the joint angle vector of the robot arm; b k+1 is a vector.

3. The method for controlling the motion of a redundant manipulator at different levels according to claim 2, characterized in that: The step of obtaining the error functions defined for the joint angle layer at adjacent first and second moments respectively comprises: At the first moment, the first error function is defined for the joint angle layer. The expression of the first error function is: e k+1 =f(θ k+1 ,t k+1 )=r actual,k+1 -r desired,k+1 , At the second moment, the second error function is defined for the joint angle layer. The expression of the second error function is: e k =f(θ k ,t k )=r actual,k -r desired,k , Among them, e k+1 =(1-h)e k , h is the step size parameter; r actual,k+1 is the actual position vector of the end effector; r desired,k+1 is the desired path of the end effector; θ k+1 is the joint angle vector of the robot arm; t k+1 represents the first moment; t k Represents the second moment.

4. The method for controlling the motion of a redundant manipulator at different levels according to claim 3, characterized in that: Based on the idea of ​​Taylor expansion, the error function of the joint angle layer at the first moment is Taylor expanded at the second moment to obtain the equation relationship of the joint velocity layer at the second moment. In this step, the expression of Taylor expansion is: The equation of the joint velocity layer at the second moment is expressed as: Among them, O(τ 2 ) is the truncation error; J(θ k ) is the Jacobian matrix of the robot, is the time derivative of the desired path of the end effector.

5. The method for controlling the motion of a redundant manipulator at different levels according to claim 4, characterized in that: Based on the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in the different layers of motion schemes, the Euler forward difference formula is applied to obtain a discrete time recursive neural network algorithm for different layers of motion control of the redundant manipulator, including: The equation relationship of the joint velocity layer at the second moment is combined with the equation relationship of the joint velocity layer in the different layers of motion schemes to obtain a combined result; According to the joint results, the discrete time recursive neural network algorithm is designed by applying Euler's forward difference formula; The motion control of different layers of redundant manipulators is performed based on a discrete-time recursive neural network algorithm.

6. The method for controlling different layers of motion of a redundant manipulator according to claim 5, characterized in that: The expression of the combined result is: The expression of the discrete time recurrent neural network algorithm is: Among them, the expression of Euler forward difference formula is: Where τ is the sampling interval; J(θ k ) is the Jacobian matrix of the robot; A k is a row-full-rank matrix; is the joint velocity vector of the robot; is the time derivative of the desired path of the end effector; h is the step size parameter; is the pseudo-inverse operator for matrices.

7. A redundant robot arm motion control device at different levels, characterized in that: include: The first module is used to collect motion data of the redundant manipulator, wherein the motion data includes rod length, initial joint angle, and expected path; The second module is used to configure the different layer motion schemes in discrete time; The third module is used to obtain the error functions defined for the joint angle layer at the adjacent first moment and the second moment respectively, and give the equation relationship between the error functions of the two adjacent moments; wherein the first moment is the next moment adjacent to the second moment; The fourth module is used to perform Taylor expansion on the error function of the joint angle layer at the first moment at the second moment based on the idea of ​​Taylor expansion, so as to obtain an equation relationship of the joint velocity layer at the second moment; The fifth module is used to apply the Euler forward difference formula based on the equation relationship of the joint velocity layer at the second moment and the equation relationship of the joint velocity layer in different layers of motion schemes, to derive a discrete-time recursive neural network algorithm for different layers of motion control of redundant robotic arms.

8. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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