CN118493392B - Automatic industrial robot coordination operation method and system - Google Patents
Automatic industrial robot coordination operation method and system Download PDFInfo
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- CN118493392B CN118493392B CN202410726576.XA CN202410726576A CN118493392B CN 118493392 B CN118493392 B CN 118493392B CN 202410726576 A CN202410726576 A CN 202410726576A CN 118493392 B CN118493392 B CN 118493392B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention discloses a coordinated operation method and a coordinated operation system for an automatic industrial robot, which particularly relate to the technical field of operation methods, and comprise the steps of sampling the working process of the industrial robot in a working space, obtaining the operability measurement and singular value expression of the industrial robot at different sampling points, evaluating the motion performance expression of the industrial robot in the working space, comparing the track and the gesture set by the industrial robot in the working space with the track and the gesture of the actual industrial robot at different sampling points, determining the track precision and the gesture precision of the industrial robot, comprehensively analyzing the motion performance expression, the track precision and the gesture precision of the industrial robot, taking the comprehensive analysis result as an objective function, searching the optimal working path in the working space of the industrial robot by using a particle swarm optimization algorithm.
Description
Technical Field
The invention relates to the field of coordination operation, in particular to a coordination operation method and system of an automatic industrial robot.
Background
In modern manufacturing industry, industrial robots are widely applied to various complex tasks, such as welding, spraying, assembling and carrying, along with the development of industrial automation, requirements on precision, efficiency and safety of the robots are higher and higher, and conventional industrial robot path planning and working space management methods usually only pay attention to stability and precision on a working track, neglect comprehensive performance in the whole working space, neglect optimization spaces of other potential tracks or gestures, cannot ensure optimal performance of the robots in the whole working space, and cannot comprehensively evaluate performance of the robots in the working space to find an optimal path for working of the industrial robots.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide an automatic industrial robot coordination method and system, so as to solve the above-mentioned problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an automatic industrial robot coordination operation method specifically comprises the following steps:
S1: determining a working space of the industrial robot, setting sampling points in the working space, and sampling the working process of the industrial robot in the working space to obtain a Jacobian matrix of the industrial robot at each sampling point;
S2: according to the Jacobian matrix of each sampling point, the operability measurement and singular value expression of the industrial robot at different sampling points are obtained, and the motion performance expression of the industrial robot in a working space is evaluated through the expression of the industrial robot at different sampling points;
S3: comparing the track and the gesture set by the industrial robot in the working space with the track and the gesture of the actual industrial robot at different sampling points to determine the track precision and the gesture precision of the industrial robot;
s4: and comprehensively analyzing the motion performance, the track precision and the gesture precision of the industrial robot, determining the comprehensive evaluation coefficient of the industrial robot, taking the comprehensive evaluation coefficient of the industrial robot as an objective function, and searching the optimal working path in the working space of the industrial robot by using a particle swarm optimization algorithm.
In a preferred embodiment, obtaining operability metrics of the industrial robot at different sampling points comprises:
the operability measurement of the industrial robot is represented by an operability deviation coefficient, and the acquisition logic of the operability deviation coefficient is as follows: determining the working space of the industrial robot, determining the number of sampling points, generating uniform sampling points in the working space of the industrial robot, calculating the Jacobian matrix of each sampling point, and calculating the operability measurement through the Jacobian matrix obtained by the sampling points;
setting an operability measurement threshold value, obtaining sampling points smaller than the operability measurement threshold value in the sampling points, and calculating an operability deviation coefficient through a formula.
In a preferred embodiment, obtaining singular value representations of the industrial robot at different sampling points comprises:
Expressing singular value expression of the industrial robot by an average singular value expression coefficient, wherein the obtaining logic of the average singular value expression coefficient is as follows: obtaining Jacobian matrixes of different sampling points in an industrial robot working space, obtaining singular values of the sampling points according to the Jacobian matrixes of the sampling points, calculating average values of the singular values of the sampling points, obtaining average values of the singular values of the different sampling points in the industrial robot working space, calculating condition numbers of the different sampling points, setting standard values of condition numbers of the sampling points of the industrial robot and standard values of the singular values, and calculating average singular value expression coefficients through a formula.
In a preferred embodiment, evaluating athletic performance of an industrial robot within a workspace, comprises:
And comprehensively analyzing the operability measurement of the industrial robot and the singular value expression of the industrial robot, and generating a motion performance evaluation coefficient by weighting and calculating the operability deviation coefficient and the average singular value expression coefficient.
In a preferred embodiment, determining the trajectory accuracy of the industrial robot comprises:
The track precision of the industrial robot is represented by a track error coefficient, and the track error coefficient acquisition logic is as follows: determining the track position of the industrial robot in the working space according to the running track of the industrial robot, obtaining the track position of the industrial robot at the sampling point, obtaining the actual position of the sampling point,
And calculating the actual position of the industrial robot at the sampling point and the Euclidean distance of the actual position of the sampling point to obtain the error of the industrial robot in the working space, and calculating the track error coefficient of the industrial robot through the root mean square error.
In a preferred embodiment, determining the pose accuracy of the industrial robot comprises:
The attitude precision of the industrial robot is represented by an attitude error coefficient, and the acquisition logic of the attitude error coefficient is as follows: acquiring preset postures of the industrial robot at different sampling points, and representing the preset postures of the industrial robot at different sampling points by using quaternions to acquire conjugated quaternions of the preset postures of the industrial robot at different sampling points, so as to acquire actual posture quaternions of the actual industrial robot at different sampling points;
Multiplying the conjugate quaternion of the preset gesture of the industrial robot at different sampling points with the actual gesture quaternion of the actual industrial robot at different sampling points to obtain the error quaternion of the industrial robot at different sampling points, converting the error quaternion into an axis angle form according to the error quaternion of the industrial robot at different sampling points, and calculating the gesture error coefficient through root mean square error.
In a preferred embodiment, searching for an optimal working path in a working space of an industrial robot comprises:
Comprehensively analyzing the motion performance evaluation coefficient, the track error coefficient and the attitude error coefficient, and establishing a mathematical analysis model through weighted calculation to generate a comprehensive evaluation coefficient;
setting a comprehensive evaluation coefficient threshold, comparing the comprehensive evaluation coefficient with the comprehensive evaluation coefficient threshold, selecting an industrial robot with the comprehensive evaluation coefficient larger than the comprehensive evaluation coefficient threshold to finish the work, and marking the industrial robot with the comprehensive evaluation coefficient larger than the comprehensive evaluation coefficient threshold as the selected robot.
According to the working content of the actual selected robot, an optimal running working track of the selected robot is obtained in the working space of the selected robot by using an optimization algorithm, wherein an optimization target of the selected robot is defined, a comprehensive evaluation coefficient is used as an objective function, constraint conditions of robot movement including joint limit and moment limit are set, the particle swarm optimization algorithm is used for running in the working space of the selected robot, and the optimal working track is searched.
In a preferred embodiment, an automatic industrial robot coordination operation system comprises a data acquisition module, a motion performance evaluation module, a comprehensive evaluation module and a path optimization module, wherein the modules are connected by signals;
The data acquisition module is used for acquiring operability measurement and singular value expression of the industrial robot in different sampling points in the working space and acquiring tracks and postures of the industrial robot in different sampling points in the working space;
the motion performance evaluation module is used for measuring the motion performance of the industrial robot in the working space according to the operability metrics and the singular value performances of different sampling points of the industrial robot in the working space;
The comprehensive evaluation module is used for measuring the comprehensive performance of the industrial robot in the working space according to the motion performance, the track precision and the gesture precision and determining the selected robot working in the working space;
and the path optimization module is used for searching and determining an optimal working path in the working space of the industrial robot by using a particle swarm optimization algorithm according to the comprehensive evaluation coefficient provided by the comprehensive evaluation module.
The invention has the technical effects and advantages that:
According to the invention, the optimal running track is obtained in the industrial robot working space by using the optimization algorithm, the operability measurement, the condition number and the singular value of the robot are comprehensively considered, and the maximum precision and the maximum efficiency of the industrial robot in executing the task are ensured by combining specific task requirements and constraint conditions.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a flow chart of a coordinated operation method of an automatic industrial robot according to the present invention;
fig. 2 is a schematic structural diagram of an automatic industrial robot coordination operation system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The invention provides a flow diagram of an automatic industrial robot coordination operation method as shown in fig. 1, which specifically comprises the following steps:
S1: determining a working space of the industrial robot, setting sampling points in the working space, and sampling the working process of the industrial robot in the working space to obtain a Jacobian matrix of the industrial robot at each sampling point;
S2: according to the Jacobian matrix of each sampling point, the operability measurement and singular value expression of the industrial robot at different sampling points are obtained, and the motion performance expression of the industrial robot in a working space is evaluated through the expression of the industrial robot at different sampling points;
S3: comparing the track and the gesture set by the industrial robot in the working space with the track and the gesture of the actual industrial robot at different sampling points to determine the track precision and the gesture precision of the industrial robot;
s4: and comprehensively analyzing the motion performance, the track precision and the gesture precision of the industrial robot, determining the comprehensive evaluation coefficient of the industrial robot, taking the comprehensive evaluation coefficient of the industrial robot as an objective function, and searching the optimal working path in the working space of the industrial robot by using a particle swarm optimization algorithm.
The industrial robot controls the position and the posture of the end effector through the combination of the connecting rod and the joint to realize the working task of the end effector, for example, the carrying robot uses a clamp to carry objects, and the stability and the accuracy are ensured through the control path and the posture.
To ensure stability and accuracy of an industrial robot, a proper number of joints needs to be selected, and degrees of freedom of the industrial robot are ensured, so that the motion capability and the operation performance of the industrial robot are affected, and the following are several key reasons for focusing on the joints of the robot:
exercise capacity: the freedom degree of the robot is determined by the joints, so that the movement range and flexibility of the robot are affected, and the robot can reach the required position and posture by the accurate control of the joints, so that various tasks are completed;
Operation precision: the high-precision joint design and control system can remarkably improve the operation precision of the robot, ensure high-quality performance in precision manufacturing and assembly tasks, ensure consistency and stability of the robot in multiple operations due to the mechanical and control precision of the joint, and enable the robot with high repeatability to maintain high-efficiency and consistent output in long-time work;
Fast response: the dynamic performance of the joints determines the response speed of the robot to control instructions, the working efficiency is affected, and the high-performance joints and the driving system can realize rapid and accurate movement, so that the joints can stably run at different speeds.
To ensure that an industrial robot can perform work requirements, and that an end effector of the industrial robot can perform work stably, performance and work efficiency of the robot are comprehensively evaluated, and operability metrics of the industrial robot are considered for reasons including:
The operability metric reflects the motion capabilities of the robot in all directions under the current pose, a high operability metric indicates that the robot can move freely and effectively in all directions, and a low operability metric indicates that the motion is limited;
by analyzing the operability measurement, the motion path of the robot can be selected and optimized, and the robot is ensured to be always in a high operability measurement area when executing the task, so that the operation efficiency is improved;
a configuration with a high operability metric means that the control system's response to the input signal is more stable and accurate in this configuration, and a low operability metric region may cause the control system to be too sensitive or insensitive to the input signal, thereby affecting the accuracy of operation;
by analyzing the operability metrics, the joint configuration and link length can be optimized, ensuring that the robot has optimal operability in the workspace.
The operability measurement of the industrial robot is represented by an operability deviation coefficient, and the acquisition logic of the operability deviation coefficient is as follows: and determining the working space of the industrial robot, determining the number of sampling points, and generating uniform sampling points in the working space of the industrial robot.
It should be noted that, the working space of the industrial robot is determined by specific production requirements and joint limitations and link lengths of the industrial robot, the position of the end effector is obtained through analysis of each joint position, and the boundary of the working space is determined, wherein the working space of the industrial robot comprises the length, the width and the height of three dimensions;
the generation of uniform sampling points in the working space is determined by calculating the density of the sampling points, and the working space is divided into three-dimensional grids with the same size, so that the same number of the sampling points in each three-dimensional grid is ensured according to the number of the three-dimensional grids and the number of the sampling points.
Calculating the Jacobian matrix of each sampling point through the kinematic model of the industrial robot and the current joint position, and marking the Jacobian matrix of each sampling point as: j n, wherein n=1, 2, 3, … …, N being a positive integer, N being the number of the sampling point;
it should be noted that the jacobian matrix is a mathematical tool describing the relationship between the motion of the end effector and the articulation of the industrial robot, helps determine the direction and speed of movement of the robot in the workspace, and enables the operability metric of the industrial robot to be obtained by the jacobian matrix.
Calculating an operability metric through the Jacobian matrix obtained by sampling points, and marking the operability metric as: d n, wherein,Wherein J n T is the transpose of the sample point Jacobian matrix;
Setting an operability metric threshold, and marking the operability metric threshold as: d yz, obtaining sample points less than the operability metric threshold and re-labeling operability metrics for sample points less than the operability metric threshold as: d m, wherein m=1, 2,3, … …, M is a positive integer, M is the number of sampling points less than the operability metric threshold;
it should be noted that the operability measurement threshold is set by a worker in the professional field, and is set according to the situation and the work requirement of the actual industrial robot.
Calculating an operability deviation coefficient, wherein the calculation formula is as follows: Wherein CZ pc is the operational deviation coefficient;
as can be seen from the formula, the larger the operability deviation coefficient, the poorer the consistency and stability of the movement of the industrial robot in the working space, and the misoperation may occur at some sampling points, so the stability of the industrial robot may be poorer.
By monitoring the singular values of different sampling points, it is ensured that the industrial robot maintains a high motion capability for reasons including:
the singular value reflects the property of the Jacobian matrix, when the minimum singular value is close to zero, the Jacobian matrix is close to singular, the robot can be positioned at a singular position, and the motion capability of the robot in certain directions can disappear at the singular position, so that the control is difficult and the precision is reduced;
the singular values provide the motion capability information of the robot in different directions, the larger singular value indicates that the motion capability in the direction is stronger, the smaller singular value indicates that the motion capability is weaker, and the motion capability of the robot in the whole working space can be evaluated by monitoring the change of the singular values, so that the region with weaker motion capability is found out;
the singular values directly influence the condition number of the Jacobian matrix, the smaller the condition number is, the more stable the numerical calculation is, the smaller the error amplification effect is, and when the singular values are uniformly distributed and are larger, the smaller the condition number is, and the numerical stability of the robot control system is better.
Expressing singular value expression of the industrial robot by an average singular value expression coefficient, wherein the obtaining logic of the average singular value expression coefficient is as follows: obtaining Jacobian matrixes of different sampling points in an industrial robot working space, obtaining singular values of the sampling points according to the Jacobian matrixes of the sampling points, and marking the singular values of the sampling points as follows: QY i, wherein i=1, 2, 3, … … I, I is a positive integer, I is the smallest dimension of the jacobian matrix, calculates the average value of the singular values of the sampling points, and marks the average value of the singular values of the sampling points as: QY avg, wherein,
It should be noted that for a robotic arm with n joints, the jacobian matrix is typically a 6 x n matrix, since it describes the relationship between six independent movements (3 translations and 3 rotations) and n joint speeds, the number of singular values being n if n is smaller than 6, and the number of singular values being 6 if n is larger than 6.
Acquiring average values of singular values of different sampling points in an industrial robot working space, and marking the average values of the singular values of the different sampling points as:
calculating the condition number of different sampling points, and marking the condition number of different sampling points as: TJ n, wherein, Obtaining the average value of the condition numbers of different sampling points, and averaging the condition numbers of different sampling points;
setting a standard value of the condition number of the sampling points of the industrial robot and a standard value of the singular value, and respectively marking the standard value of the condition number of the sampling points of the industrial robot and the standard value of the singular value as follows: TJ yz and QY yz;
It should be noted that, the standard value of the condition number of the sampling point of the industrial robot and the standard value of the singular value are set by the staff in the professional field, and are determined according to the actual situation and the working requirement of the specific industrial robot.
Calculating an average singular value expression coefficient, wherein the calculation formula is as follows: wherein BX pj is an average singular value representation.
The formula shows that the larger the average singular value expression coefficient is, the better the motion performance of the industrial robot is, and the robot system can realize more stable, efficient and accurate coordination operation in a complex environment.
The method comprises the steps of comprehensively analyzing operability measurement of the industrial robot and singular value expression of the industrial robot, measuring the motion performance of the industrial robot in a weighted calculation mode of an operability deviation coefficient and an average singular value expression coefficient, and generating a motion performance evaluation coefficient, wherein a calculation formula of the motion performance coefficient is as follows: YD xn＝-α1CZpc+α2BXpj; wherein YD xn is a motion performance evaluation coefficient, alpha 1、α2 is an operational deviation coefficient and a proportionality coefficient of an average singular value expression coefficient, and alpha 1、α2 is more than 0.
The formula shows that the smaller the operability deviation coefficient is, the larger the average singular value expression coefficient is, the larger the motion performance evaluation coefficient is, and the better the motion performance of the industrial robot is, otherwise, the larger the operability deviation coefficient is, the smaller the average singular value expression coefficient is, the smaller the motion performance evaluation coefficient is, and the worse the motion performance of the industrial robot is.
In the working of an industrial robot, track precision and gesture precision are two key performance indexes for measuring the task of the robot in a working space, and the accuracy of the end effector of the robot on a path and on a gesture are respectively evaluated, wherein the track precision refers to the position accuracy of the end effector of the robot when moving along a specified path, the gesture precision refers to the angle or azimuth accuracy of the end effector of the robot when executing the task, and the reasons for considering the track precision and the gesture precision of the industrial robot include:
In the task requiring high-precision operation, the operation is required to be accurately performed according to a preset path and gesture so as to avoid collision or interference, the track and gesture precision directly influences the consistency of production, and the high-precision track and gesture control can ensure the consistency of the size and shape of each product and reduce the defective rate;
The high track and gesture precision can reduce the requirement of repeated adjustment and correction, improve the production efficiency, and the accurate motion control ensures that the robot can complete tasks faster and more accurately, shortens the production period and improves the production efficiency.
The track precision of the industrial robot is represented by a track error coefficient, and the track error coefficient acquisition logic is as follows: determining the track position of the industrial robot in the working space according to the running track of the industrial robot, obtaining the track position of the industrial robot at the sampling point, and marking the track position of the industrial robot at the sampling point as follows: obtaining the actual position of the sampling point, and marking the actual position of the sampling point as: (x n,yn,zn);
It should be noted that, the track position of the industrial robot in the working space is a continuously changing three-dimensional coordinate, and the actual position of the sampling point is a fixed three-dimensional coordinate.
Obtaining an error of the industrial robot in the working space by calculating the Euclidean distance between the track position of the industrial robot at the sampling point and the actual position of the sampling point, and marking the error of the industrial robot in the working space as:
the track error coefficient of the industrial robot is calculated through root mean square error, and the calculation formula of the track error coefficient is as follows:
As can be seen from the formula, the larger the track error coefficient is, the larger the deviation between the actual path and the preset path of the robot end effector is, the track tracking precision is low, and the fact that the joints and the connecting rods of the mechanical system have manufacturing errors or wear possibly causes inaccurate movement and increases the track error is indicated.
The attitude precision of the industrial robot is represented by an attitude error coefficient, and the acquisition logic of the attitude error coefficient is as follows: acquiring preset postures of the industrial robot at different sampling points, representing the preset postures of the industrial robot at different sampling points by quaternions, and marking the preset postures of the industrial robot at different sampling points as follows: obtaining actual attitude quaternions of the actual industrial robot at different sampling points, and marking the actual attitude quaternions of the actual industrial robot at the different sampling points as follows:
it should be noted that the quaternion provides an efficient and singular-free way to represent and calculate three-dimensional rotations, and in applications requiring a large number of rotation calculations, the quaternion can significantly improve the calculation efficiency.
Multiplying the conjugate quaternion of the preset gesture of the industrial robot at different sampling points with the actual gesture quaternion of the actual industrial robot at different sampling points to obtain the error quaternion of the industrial robot at different sampling points, and marking the error quaternion of the industrial robot at different sampling points as: Wherein,
According to the error quaternion of the industrial robot at different sampling points, the error quaternion is converted into an axis angle form, and the error marks of the axis angle form of the different sampling points are as follows: θ n, wherein θ n＝2cos-1 (w);
the attitude error coefficient is calculated through root mean square error, and the calculation formula of the attitude error coefficient is as follows: Wherein ZT wc is an attitude error coefficient.
As can be seen from the formula, the larger the attitude error coefficient is, the larger the difference between the actual attitude of the industrial robot and the preset target attitude is, the larger the attitude error coefficient is, which means that the task cannot reach the expected accuracy, and the problem of product quality can be caused.
Comprehensively analyzing the motion performance evaluation coefficient, the track error coefficient and the attitude error coefficient, establishing a mathematical analysis model through weighted calculation, and generating a comprehensive evaluation coefficient, wherein the calculation formula of the comprehensive evaluation coefficient is as follows: Wherein pg is a comprehensive evaluation coefficient, beta 1、β2、β3 is a proportional coefficient of a motion performance evaluation coefficient, a track error coefficient and an attitude error coefficient, and beta 1、β2、β3 is more than 0.
The formula shows that the larger the motion performance evaluation coefficient is, the smaller the track error coefficient and the attitude error coefficient are, the larger the comprehensive evaluation coefficient is, otherwise, the smaller the motion performance evaluation coefficient is, the larger the track error coefficient and the attitude error coefficient are, the smaller the comprehensive evaluation coefficient is.
Setting a comprehensive evaluation coefficient threshold, comparing the comprehensive evaluation coefficient with the comprehensive evaluation coefficient threshold, selecting an industrial robot with the comprehensive evaluation coefficient larger than the comprehensive evaluation coefficient threshold to finish the work, and marking the industrial robot with the comprehensive evaluation coefficient larger than the comprehensive evaluation coefficient threshold as the selected robot.
According to the working content of the actual selected robot, an optimal running working track of the selected robot is obtained in the working space of the selected robot by using an optimization algorithm, wherein an optimization target of the selected robot is defined, a comprehensive evaluation coefficient is used as an objective function, constraint conditions of robot movement including joint limit and moment limit are set, the particle swarm optimization algorithm is used for running in the working space of the selected robot, and the optimal working track is searched.
It should be noted that the joint limit indicates a movement range of each joint of the selected robot, that is, each joint has an allowable maximum and minimum angle or displacement to prevent damage of a mechanical structure, and the moment limit ensures that a moment applied to each joint of the selected robot is within a bearing range of the selected robot to prevent overload or mechanical damage of a motor, and an optimal working path is composed of a series of sampling points.
According to the invention, the optimal running track is obtained in the industrial robot working space by using the optimization algorithm, the operability measurement, the condition number and the singular value of the robot are comprehensively considered, and the maximum precision and the maximum efficiency of the industrial robot in executing the task are ensured by combining specific task requirements and constraint conditions.
Example 2
The invention provides a structural schematic diagram of an automatic industrial robot coordination operation system shown in fig. 2, which comprises a data acquisition module, a motion performance evaluation module, a comprehensive evaluation module and a path optimization module, wherein the modules are connected by signals;
The data acquisition module is used for acquiring operability measurement and singular value expression of the industrial robot in different sampling points in the working space and acquiring tracks and postures of the industrial robot in different sampling points in the working space;
the motion performance evaluation module is used for measuring the motion performance of the industrial robot in the working space according to the operability metrics and the singular value performances of different sampling points of the industrial robot in the working space;
The comprehensive evaluation module is used for measuring the comprehensive performance of the industrial robot in the working space according to the motion performance, the track precision and the gesture precision and determining the selected robot working in the working space;
and the path optimization module is used for searching and determining an optimal working path in the working space of the industrial robot by using a particle swarm optimization algorithm according to the comprehensive evaluation coefficient provided by the comprehensive evaluation module.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (2)
1. An automatic industrial robot coordination operation method is characterized by comprising the following steps:
S1: determining a working space of the industrial robot, setting sampling points in the working space, and sampling the working process of the industrial robot in the working space to obtain a Jacobian matrix of the industrial robot at each sampling point;
S2: according to the Jacobian matrix of each sampling point, the operability measurement and singular value expression of the industrial robot at different sampling points are obtained, and the motion performance expression of the industrial robot in a working space is evaluated through the expression of the industrial robot at different sampling points;
S3: comparing the track and the gesture set by the industrial robot in the working space with the track and the gesture of the actual industrial robot at different sampling points to determine the track precision and the gesture precision of the industrial robot;
s4: comprehensively analyzing the motion performance, the track precision and the gesture precision of the industrial robot, determining the comprehensive evaluation coefficient of the industrial robot, taking the comprehensive evaluation coefficient of the industrial robot as an objective function, and searching an optimal working path in the working space of the industrial robot by using a particle swarm optimization algorithm;
obtaining operability metrics of the industrial robot at different sampling points, comprising:
the operability measurement of the industrial robot is represented by an operability deviation coefficient, and the acquisition logic of the operability deviation coefficient is as follows: determining the working space of the industrial robot, determining the number of sampling points, generating uniform sampling points in the working space of the industrial robot, calculating the Jacobian matrix of each sampling point, and calculating the operability measurement through the Jacobian matrix obtained by the sampling points;
Setting an operability metric threshold, and marking the operability metric threshold as: d yz, obtaining sample points less than the operability metric threshold and re-labeling operability metrics for sample points less than the operability metric threshold as: d m, wherein m=1, 2, 3, … …, M is a positive integer, M is the number of the sampling point smaller than the operability metric threshold, and the operability deviation coefficient is calculated by a formula, where the calculation formula is: Wherein CZ pc is the operational deviation coefficient;
obtaining singular value representations of the industrial robot at different sampling points, comprising:
expressing singular value expression of the industrial robot by an average singular value expression coefficient, wherein the obtaining logic of the average singular value expression coefficient is as follows: acquiring Jacobian matrixes of different sampling points in an industrial robot working space, acquiring singular values of the sampling points according to the Jacobian matrixes of the sampling points, and calculating an average value of the singular values of the sampling points;
The average value of the singular values of the sampling points is calculated specifically as follows: obtaining Jacobian matrixes of different sampling points in an industrial robot working space, obtaining singular values of the sampling points according to the Jacobian matrixes of the sampling points, and marking the singular values of the sampling points as follows: QY i, wherein i=1, 2, 3, … … I, I is a positive integer, I is the smallest dimension of the jacobian matrix, calculates the average value of the singular values of the sampling points, and marks the average value of the singular values of the sampling points as: QY avg, wherein,
Acquiring average values of singular values of different sampling points in an industrial robot working space, and marking the average values of the singular values of the different sampling points as:
calculating the condition number of different sampling points, and marking the condition number of different sampling points as: TJ n, wherein, Obtaining the average value of the condition numbers of different sampling points, and averaging the condition numbers of different sampling points;
Setting a standard value of the condition number of the sampling points of the industrial robot and a standard value of the singular value, and respectively marking the standard value of the condition number of the sampling points of the industrial robot and the standard value of the singular value as follows: TJ yz and QY yz; calculating an average singular value expression coefficient by a formula, wherein the calculation formula is as follows: Wherein BX pj is an average singular value representation coefficient;
Evaluating athletic performance of an industrial robot within a workspace, comprising:
The method comprises the steps of comprehensively analyzing operability measurement of the industrial robot and singular value expression of the industrial robot, and generating a motion performance evaluation coefficient by weighting and calculating an operability deviation coefficient and an average singular value expression coefficient, wherein a calculation formula of the motion performance coefficient is as follows: YD xn＝-α1CZpc+α2BXpj; wherein YD xn is a motion performance evaluation coefficient, alpha 1、α2 is an operability deviation coefficient and a proportionality coefficient of an average singular value expression coefficient, and alpha 1、α2 is more than 0;
determining the track accuracy of the industrial robot comprises:
the track precision of the industrial robot is represented by a track error coefficient, and the track error coefficient acquisition logic is as follows: determining the track position of the industrial robot in the working space according to the running track of the industrial robot, obtaining the track position of the industrial robot at the sampling point, and marking the track position of the industrial robot at the sampling point as follows: Obtaining the actual position of the sampling point, and marking the actual position of the sampling point as: (x n,yn,an);
Obtaining an error of the industrial robot in the working space by calculating the actual position of the industrial robot at the sampling point and the Euclidean distance of the actual position of the sampling point, and marking the error of the industrial robot in the working space as: the track error coefficient of the industrial robot is calculated through root mean square error, and the calculation formula of the track error coefficient is as follows:
determining the pose accuracy of the industrial robot, comprising:
The attitude precision of the industrial robot is represented by an attitude error coefficient, and the acquisition logic of the attitude error coefficient is as follows: acquiring preset postures of the industrial robot at different sampling points, representing the preset postures of the industrial robot at different sampling points by quaternions, and marking the preset postures of the industrial robot at different sampling points as follows: obtaining actual attitude quaternions of the actual industrial robot at different sampling points, and marking the actual attitude quaternions of the actual industrial robot at the different sampling points as follows:
Multiplying the conjugate quaternion of the preset gesture of the industrial robot at different sampling points with the actual gesture quaternion of the actual industrial robot at different sampling points to obtain the error quaternion of the industrial robot at different sampling points, and marking the error quaternion of the industrial robot at different sampling points as: Wherein, According to the error quaternion of the industrial robot at different sampling points, the error quaternion is converted into an axis angle form, and the error marks of the axis angle form of the different sampling points are as follows: θ n, wherein θ n＝2cos-1 (w), and calculating an attitude error coefficient by a root mean square error, wherein the calculation formula is: Wherein ZT wc is an attitude error coefficient;
searching for an optimal working path in a working space of an industrial robot, comprising:
comprehensively analyzing the motion performance evaluation coefficient, the track error coefficient and the attitude error coefficient, establishing a mathematical analysis model through weighted calculation, and generating a comprehensive evaluation coefficient, wherein the calculation formula is as follows: Wherein pg is a comprehensive evaluation coefficient, beta 1、β2、β3 is a proportional coefficient of a motion performance evaluation coefficient, a track error coefficient and an attitude error coefficient, and beta 1、β2、β3 is more than 0;
setting a comprehensive evaluation coefficient threshold, comparing the comprehensive evaluation coefficient with the comprehensive evaluation coefficient threshold, selecting an industrial robot with the comprehensive evaluation coefficient larger than the comprehensive evaluation coefficient threshold to finish the work, and marking the industrial robot with the comprehensive evaluation coefficient larger than the comprehensive evaluation coefficient threshold as the selected robot;
According to the working content of the actual selected robot, an optimal running working track of the selected robot is obtained in the working space of the selected robot by using an optimization algorithm, wherein an optimization target of the selected robot is defined, a comprehensive evaluation coefficient is used as an objective function, constraint conditions of robot movement including joint limit and moment limit are set, the particle swarm optimization algorithm is used for running in the working space of the selected robot, and the optimal working track is searched.
2. An automatic industrial robot coordination operation system for realizing the automatic industrial robot coordination operation method according to claim 1, which is characterized by comprising a data acquisition module, a motion performance evaluation module, a comprehensive evaluation module and a path optimization module, wherein the modules are connected by signals;
The data acquisition module is used for acquiring operability measurement and singular value expression of the industrial robot in different sampling points in the working space and acquiring tracks and postures of the industrial robot in different sampling points in the working space;
the motion performance evaluation module is used for measuring the motion performance of the industrial robot in the working space according to the operability metrics and the singular value performances of different sampling points of the industrial robot in the working space;
The comprehensive evaluation module is used for measuring the comprehensive performance of the industrial robot in the working space according to the motion performance, the track precision and the gesture precision and determining the selected robot working in the working space;
and the path optimization module is used for searching and determining an optimal working path in the working space of the industrial robot by using a particle swarm optimization algorithm according to the comprehensive evaluation coefficient provided by the comprehensive evaluation module.
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