A robot arm trajectory planning method, system, device and storage medium

Abstract

The present application provides a robot arm trajectory planning method, system, device and storage medium. The robot arm dynamics equation established based on the Euler-Lagrange equation adopts simple cell mapping, and samples the two points at the beginning and end of the robot arm and several points between the two points in the discrete state space on the continuous trajectory and performs block processing; there are several points corresponding to it in each block state space and they do not overlap with each other, and in each block state space, the simple cell mapping idea is used to map according to the continuous path and the dynamics equation to obtain the trajectory planning control input of the robot arm; the complex calculation problems and calculation errors of the robot arm trajectory planning are effectively solved; at the same time, the present application can ensure the stability of the robot arm trajectory planning, and there will be no problems such as mutation; thereby improving the accuracy, efficiency and reliability of the robot arm trajectory planning, with good actual working effect, and can be adjusted according to the error accuracy requirements in actual working conditions, with high adaptability.

Description

Technical Field

[0001] The present invention belongs to the field of robotics technology, and in particular relates to a robot arm trajectory planning method, system, device and storage medium. Background Art

[0002] The trajectory planning of the robot is a key issue in the robot control system. The trajectory planning between the starting and ending points of the robot can be divided into point-to-point and continuous path trajectory planning methods. For point-to-point trajectory planning problems, only the positions of the starting and ending points are usually required, while for continuous path trajectory planning, not only the positions between the starting and ending points are required, but also the positions of several points between the two points, so as to obtain the trajectory of the continuous path of the robot.

[0003] There are two types of trajectory planning for robotic arms: joint space trajectory planning and Cartesian space trajectory planning. Among them, joint space trajectory planning is relatively simple, with less calculation and can avoid the occurrence of singular problems, but the trajectory of the end of the robotic arm is uncontrollable. The trajectory error of the end of the robotic arm in Cartesian space trajectory planning is small, but the calculation is complex. The planning effect and accuracy of the existing related algorithms are poor. The trajectory planning methods for robotic arms are mainly B-spline curves, spline functions, polynomial functions, interpolation methods, variational methods, combined with genetic algorithms, fuzzy control and other intelligent methods, but there are still challenges in accuracy and computational complexity. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a robot arm trajectory planning method, system, device and storage medium, which can ensure the smooth and continuous movement of the robot arm and have the characteristics of efficient calculation and high precision.

[0005] The present invention is achieved through the following technical solutions:

[0006] A robot arm trajectory planning method, characterized in that it comprises the following steps:

[0007] S1: Establish the dynamic equation of the robot arm according to the Euler-Lagrange equation and obtain the relevant robot arm activity parameters;

[0008] S2: Establish the state equation of the manipulator based on the relevant manipulator activity parameters and determine the limit range of the state and control variables;

[0009] S3: Establish block state spaces for several points between two points on the continuous path of the given trajectory of the robot arm;

[0010] S4: Perform cell mapping search in the block state space according to the cell mapping information selection rule;

[0011] S5: Connect the connection boundaries of the block state space to obtain the trajectory planning of the entire path.

[0012] Furthermore, the two points of the continuous path of the given trajectory of the robot arm include a starting point, an end point and a sampling point, each block state space does not overlap with each other, and the boundaries of adjacent block state spaces are connected to each other and cover the continuous path given by the robot arm.

[0013] Furthermore, the establishment of the block state space is based on the actual working accuracy requirements of the robot arm and the requirements that the block state space should meet, thereby determining the size and discreteness of the block state space.

[0014] Furthermore, in step S4, the segmented continuous path in the block state space is discretized into cells of different levels based on the accuracy requirement of trajectory planning, and the cells passed by the trajectory planning are labeled to obtain labeled cells, and the labeled cells are used as objects of mapping and search screening.

[0015] Furthermore, in step S4, cell mapping is performed on all labeled cells under different levels of control variables, all cell mappings of image cells mapped to labeled cells are screened and relevant mapping information is recorded to establish a cell mapping database;

[0016] In the cell mapping database, we screen again according to the principle of minimizing the difference between the image cell number and the original cell number, and delete the cases where the original cell is far away from the image cell to ensure the continuity of the trajectory;

[0017] The primitive cells with multiple mapping information in the cell mapping database are processed and re-screened according to the principle of minimum distance from mapping point to trajectory, so that each primitive cell has only one mapping information.

[0018] Furthermore, the step S5 of connecting the connection boundaries of the block state space includes the following steps:

[0019] A block state space is set to n=1, the labeled cells at the boundary connection of the nth and n+1th block state spaces are located and renumbered; the labeled cells at the boundary connection of the nth block state space are cell mapped under different levels of control variables, all cell mappings of image cells mapped to the labeled cells of the n+1th block state space are screened and the relevant mapping information is recorded, and the corresponding cell mapping database is updated.

[0020] Furthermore, the second cell mapping database is screened again according to the principle of minimizing the difference between the image cell number and the original cell number, and then the original cells with multiple mapping information in the cell mapping database are screened again according to the principle of minimizing the distance from the mapping point to the trajectory, to ensure that each original cell has only one mapping information;

[0021] Set n=n+1, and determine whether n is equal to N, where N is the total number of block state spaces. If not, continue searching the next block state space; if equal, all block state spaces have been processed.

[0022] A robot arm trajectory planning system, comprising:

[0023] A parameter acquisition module is used to establish the dynamic equation of the robot arm according to the Euler-Lagrange equation and obtain relevant robot arm activity parameters;

[0024] A robot state module, used to establish the robot state equation based on the relevant robot activity parameters and determine the limit range of the state and control variables;

[0025] The block state space module is used to establish block state spaces for several points between two points of the continuous path of a given trajectory of the robot arm;

[0026] A cell mapping search module is used to search for cell mappings in the block state space according to the cell mapping information selection rule;

[0027] The trajectory planning module is used to connect the connection boundaries of the block state space to obtain the trajectory planning of the entire path.

[0028] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a robot arm trajectory planning method when executing the computer program.

[0029] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a robot arm trajectory planning method are implemented.

[0030] Compared with the prior art, the present invention has the following beneficial technical effects:

[0031] The present application provides a robot arm trajectory planning method, system, device and storage medium. The present application adopts the idea of ​​simple cell mapping in the robot arm dynamics equation established based on the Euler-Lagrange equation, samples the two points at the beginning and end of the robot arm and several points between the two points, that is, in a given continuous trajectory, in a discrete state space, and performs block processing; there are several points corresponding to it in each block state space and they do not overlap with each other, and in each block state space, the simple cell mapping idea is used to map according to the continuous path and the dynamics equation to obtain the trajectory planning control input of the robot arm; the idea of ​​block state space and the cell mapping information selection rule can greatly improve the calculation efficiency of the cell mapping method in the prior art, and effectively solve the problem of complex calculation of robot arm trajectory planning; the dynamics equation based on the robot arm system can reduce the calculation error and ensure the accuracy and reliability of the robot arm trajectory planning; at the same time, the present application can ensure the stability of the robot arm trajectory planning, and there will be no problems such as mutation; thereby improving the accuracy, efficiency and reliability of the robot arm trajectory planning, with good actual working effect, and can be adjusted according to the error accuracy requirements in actual working conditions, with high adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flow chart of a robot arm trajectory planning method for this application;

[0033] Figure 2 This is a schematic diagram of establishing the block state space for this application;

[0034] Figure 3 This is a schematic diagram of the cell mapping search in the block state space of this application;

[0035] Figure 4 This is a flowchart for searching the cell mapping in the block state space of this application;

[0036] Figure 5 This is a flow chart of the block state space connection boundary processing for this application. DETAILED DESCRIPTION

[0037] The present invention is further described in detail below in conjunction with specific embodiments, which are intended to explain the present invention rather than to limit it.

[0038] 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.

[0039] 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.

[0040] This application provides a robot arm trajectory planning method, such as Figure 1 As shown, the following steps are included:

[0041] S1: Establish the dynamic equation of the robot arm according to the Euler-Lagrange equation and obtain the relevant robot arm activity parameters;

[0042] S2: Establish the state equation of the manipulator based on the relevant manipulator activity parameters and determine the limit range of the state and control variables;

[0043] S3: For several points between two points on the continuous path of the given trajectory of the robot arm, a block state space is established, such as Figure 2 As shown;

[0044] S4: Perform cell mapping search in the block state space according to the cell mapping information selection rule, such as Figure 3 As shown;

[0045] S5: Connect the connection boundaries of the block state space to obtain the trajectory planning of the entire path.

[0046] Preferably, the two points of the continuous path of the given trajectory of the robot arm include a starting point, an end point and a sampling point, each block state space does not overlap with each other, and the boundaries of adjacent block state spaces are connected to each other and cover the continuous path given by the robot arm.

[0047] Preferably, the establishment of the block state space is based on the actual working accuracy requirements of the robot arm and the requirements that the block state space should meet, so as to determine the size and discreteness of the block state space.

[0048] Preferably, in step S4, the segmented continuous path in the block state space is discretized into cells of different levels based on the accuracy requirement of trajectory planning, and the cells passed by the trajectory planning are labeled to obtain labeled cells, and the labeled cells are used as objects of mapping and search screening.

[0049] Further, such as Figure 4As shown, in step S4, cell mapping is performed on all labeled cells under different levels of control variables, all cell mappings of image cells to labeled cells are screened and relevant mapping information is recorded to establish a cell mapping database;

[0050] In the cell mapping database, we screen again according to the principle of minimizing the difference between the image cell number and the original cell number, and delete the cases where the original cell is far away from the image cell to ensure the continuity of the trajectory;

[0051] The primitive cells with multiple mapping information in the cell mapping database are processed and re-screened according to the principle of minimum distance from mapping point to trajectory, so that each primitive cell has only one mapping information.

[0052] Preferably, Figure 5 As shown, the step S5 of connecting the connection boundaries of the block state space includes the following steps:

[0053] A block state space is set to n=1, the labeled cells at the boundary connection of the nth and n+1th block state spaces are located, and they are renumbered; the labeled cells at the boundary connection of the nth block state space are cell-mapped under different levels of control variables, all cell mappings of image cells mapped to the labeled cells of the n+1th block state space are screened, the relevant mapping information is recorded, and the corresponding cell mapping database is updated;

[0054] Furthermore, the second cell mapping database is screened again according to the principle of minimizing the difference between the image cell number and the original cell number, and then the original cells with multiple mapping information in the cell mapping database are screened again according to the principle of minimizing the distance from the mapping point to the trajectory, to ensure that each original cell has only one mapping information;

[0055] Set n=n+1, and determine whether n is equal to N, where N is the total number of block state spaces. If not, continue searching the next block state space; if equal, all block state spaces have been processed.

[0056] The present application provides a robot arm trajectory planning system, based on a robot arm trajectory planning method, comprising:

[0057] A parameter acquisition module is used to establish the dynamic equation of the robot arm according to the Euler-Lagrange equation and obtain relevant robot arm activity parameters;

[0058] A robot state module, used to establish the robot state equation based on the relevant robot activity parameters and determine the limit range of the state and control variables;

[0059] The block state space module is used to establish block state spaces for several points between two points of the continuous path of a given trajectory of the robot arm;

[0060] A cell mapping search module is used to search for cell mappings in the block state space according to the cell mapping information selection rule;

[0061] The trajectory planning module is used to connect the connection boundaries of the block state space to obtain the trajectory planning of the entire path.

[0062] In another embodiment of the present invention, a computer device is provided, the computer device including a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a robot arm trajectory planning method.

[0063] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of a robot trajectory planning method in the above embodiment.

[0064] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A robot arm trajectory planning method, characterized in that: The following steps are involved: S1: Establish the dynamic equation of the robot arm according to the Euler-Lagrange equation and obtain the relevant robot arm activity parameters; S2: Establish the state equation of the manipulator based on the relevant manipulator activity parameters and determine the limit range of the state and control variables; S3: Establish block state spaces for several points between two points on the continuous path of the given trajectory of the robot arm; S4: Perform cell mapping search in the block state space according to the cell mapping information selection rule; the specific method is as follows: The positioning segmented continuous path in the block state space is discretized into cells of different levels based on the accuracy requirement of trajectory planning, and the cells passed by the trajectory planning are labeled to obtain labeled cells, which are used as objects of mapping and search screening; Among them, cell mapping is performed on all labeled cells under different levels of control variables, all cell mappings from image cells to labeled cells are screened and relevant mapping information is recorded to establish a cell mapping database; In the cell mapping database, we screen again according to the principle of minimizing the difference between the image cell number and the original cell number, and delete the cases where the original cell is far away from the image cell to ensure the continuity of the trajectory; Process the primitive cells with multiple mapping information in the cell mapping database, and re-screen them according to the principle of minimum distance from mapping point to trajectory, so that each primitive cell has only one mapping information; S5: Connect the connection boundaries of the block state space to obtain the trajectory planning of the entire path.

2. A robot arm trajectory planning method according to claim 1, characterized in that: The two points of the continuous path of the given trajectory of the robot arm include a starting point, an end point and a sampling point, each block state space does not overlap with each other, and the boundaries of adjacent block state spaces are connected to each other and cover the continuous path given by the robot arm.

3. A robot arm trajectory planning method according to claim 1, characterized in that: The establishment of the block state space is based on the actual working accuracy requirements of the robot arm and the requirements that the block state space should meet, thereby determining the size and discreteness of the block state space.

4. A robot arm trajectory planning method according to claim 1, characterized in that: The step S5 of connecting the connection boundaries of the block state space includes the following steps: A block state space is set to n=1, the labeled cells at the boundary connection of the nth and n+1th block state spaces are located and renumbered; the labeled cells at the boundary connection of the nth block state space are cell-mapped under different levels of control variables, all cell mappings of image cells mapped to the labeled cells of the n+1th block state space are screened and the relevant mapping information is recorded, and the corresponding cell mapping database is updated.

5. A robot arm trajectory planning system, characterized in that: A robot arm trajectory planning method based on any one of claims 1 to 4, comprising: A parameter acquisition module is used to establish the dynamic equation of the robot arm according to the Euler-Lagrange equation and obtain relevant robot arm activity parameters; A robot state module, used to establish the robot state equation based on the relevant robot activity parameters and determine the limit range of the state and control variables; The block state space module is used to establish block state spaces for several points between two points of the continuous path of a given trajectory of the robot arm; A cell mapping search module is used to search for cell mappings in the block state space according to the cell mapping information selection rule; The trajectory planning module is used to connect the connection boundaries of the block state space to obtain the trajectory planning of the entire path.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the robot arm trajectory planning method as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a robot arm trajectory planning method as described in any one of claims 1 to 4 are implemented.

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