CN119304931B - Robot stiffness identification method and system based on parallel rope drive loading multi-directional force - Google Patents
Robot stiffness identification method and system based on parallel rope drive loading multi-directional force Download PDFInfo
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Abstract
本发明公开一种基于并联绳驱加载多方向力的机器人刚度辨识方法及系统，属于机器人刚度辨识技术领域，包括：任选多个不同的施加外载荷作为初始载荷组，计算得到初始载荷组的适应性评价指标和初始载荷组所对应观测矩阵的条件数，进而建立面向系统扰动的辨识载荷评价指标；其中，不同外载荷的大小及方向均不同；以面向系统扰动的辨识载荷评价指标最小化为目标，联合机器人承受负载约束力限位为约束条件，构建面向系统扰动的变载荷评价模型，根据模型求解结果选定最优载荷组；基于最优载荷组，利用并联绳驱测量加载系统对机器人进行刚度辨识试验，测量机器人在不同载荷下末端靶球的坐标值，计算得到变形矢量，进而计算得到准确的机器人关节刚度。
The invention discloses a robot stiffness identification method and system based on parallel rope-driven loading of multi-directional forces, belonging to the technical field of robot stiffness identification, comprising: selecting a plurality of different applied external loads as an initial load group, calculating the adaptability evaluation index of the initial load group and the condition number of the observation matrix corresponding to the initial load group, and then establishing an identification load evaluation index for system disturbance; wherein the magnitudes and directions of different external loads are different; taking minimization of the identification load evaluation index for system disturbance as the goal, combining the limit of the load constraint force borne by the robot as a constraint condition, constructing a variable load evaluation model for system disturbance, selecting the optimal load group according to the model solution result; based on the optimal load group, using a parallel rope-driven measurement and loading system to perform a stiffness identification test on the robot, measuring the coordinate values of the target ball at the end of the robot under different loads, calculating the deformation vector, and then calculating the accurate robot joint stiffness.
Description
Technical Field
The invention belongs to the technical field of robot rigidity identification, and particularly relates to a robot rigidity identification method and system based on parallel rope driving loading multi-directional force.
Background
The statements herein merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In high-end manufacturing, particularly in the field of precision machining such as aerospace, robots are becoming increasingly important innovative equipment due to their high degree of flexibility and space position control capability. However, due to the inherent structural characteristics of the serial mechanism, the rigidity of the robot is generally far lower than that of a traditional numerical control machine tool, and particularly when high-precision and heavy-load tasks are executed, the robot is easily deformed under the influence of external force. Therefore, the rigidity becomes an important index for measuring the deformation of the robot against external force, and directly influences the positioning accuracy, the response speed and the adaptability of the system under a complex environment. With the development of industrial robots to higher precision and multitasking, the need for high precision stiffness identification of robots is becoming more urgent.
The current method for identifying the rigidity of the robot is mostly based on static measurement, namely, by applying static load in a specific direction, the displacement response of the end of the robot or the measured object is analyzed, so that the rigidity characteristic of the robot or the measured object is calculated. However, such static measurement methods can only reflect stiffness information in a specific direction, and are difficult to cover complex, multi-directional dynamic stress situations. In practical application, robots are usually subjected to external interference of multiple directions and multiple dimensions, such as dynamic grabbing, carrying and interactive operation, and the application environments require more comprehensive rigidity characteristics, and static rigidity data of a single direction are often insufficient to accurately reflect the overall rigidity characteristics of the robots, so that the application effect of the robots in dynamic and complex scenes is further limited.
In addition, the traditional rigidity identification device or system has a certain limitation in an external load loading mode, load can be applied in a single direction generally, even distribution of force is difficult to ensure, due to limited structural design and directional arrangement of the loading device, multi-direction and multi-angle stress scenes are difficult to simulate, the response of a robot under complex load is difficult to be accurately captured, the loading device has defects in precision and control flexibility, uneven distribution of loading force and limited loading angle are easy to cause, and the precision of rigidity identification is further influenced.
That is, the existing method for identifying the rigidity of the robot has the limitation of a single stress direction, and usually only an external force is applied to the tail end of the robot in a certain direction, and the obtained rigidity model only can reflect the rigidity characteristic of the direction, and although the method is simple to operate, the method has certain defects:
(1) The stiffness of the robot typically has anisotropy, i.e. exhibits different stiffness in different directions, e.g. the stiffness of the robot tip may be much higher in the vertical direction than in the horizontal direction. The stiffness identification in a single direction cannot capture the difference, so that the model cannot accurately reflect the actual situation, and the model does not perform well in a multidirectional stress environment.
(2) In the operation of multiple degrees of freedom and high dynamic actions, stress and deformation in different directions are often coupled with each other, if analysis is performed only according to the stiffness identification result in a single direction, errors may become larger along with the increase of operation complexity, and particularly in the multi-joint operation and the flexible joint system, pose control is unstable easily to reduce identification accuracy.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a robot rigidity identification method and system based on parallel rope drive loading multi-directional force, which establishes a data foundation for follow-up accurate identification by constructing an anti-disturbance identification external load evaluation index and selecting an optimal load group, designs a parallel rope drive measurement loading system, and identifies the rigidity characteristics of the robot in multiple degrees of freedom, multiple angles and multiple directions, realizes accurate multi-directional rigidity measurement and identification, and solves the problem of poor accuracy of the traditional single-direction rigidity identification.
In a first aspect, the invention provides a robot stiffness identification method based on parallel rope drive loading multi-directional force.
A robot stiffness identification method based on parallel rope drive loading multi-directional force comprises the following steps:
optionally selecting a plurality of different applied external loads as an initial load group, and calculating to obtain an adaptability evaluation index of the initial load group and a condition number of an observation matrix corresponding to the initial load group, thereby establishing an identification load evaluation index facing system disturbance, wherein the sizes and directions of the different external loads are different;
the method comprises the steps of constructing a variable load evaluation model facing system disturbance by taking the minimum identification load evaluation index facing the system disturbance as a target and taking the limit of the load bearing constraint force of a combined robot as a constraint condition, and selecting an optimal load group according to a model solving result;
Based on the optimal load group, a parallel rope drive measurement loading system is utilized to carry out a rigidity identification test on the robot, coordinate values of the tail end target ball of the robot under different loads are measured, a deformation vector is calculated, and then the joint rigidity of the robot is calculated.
In a second aspect, the invention provides a robot stiffness identification system based on parallel rope drive loading multi-directional forces.
A robot stiffness identification system based on parallel rope drive loading multi-directional force comprises:
The system comprises an identification load evaluation index construction module, a system disturbance-oriented identification load evaluation index, a load control module and a load control module, wherein the identification load evaluation index construction module is used for selecting a plurality of different applied external loads as an initial load group, calculating to obtain an adaptability evaluation index of the initial load group and a condition number of an observation matrix corresponding to the initial load group, and further establishing the identification load evaluation index oriented to the system disturbance;
The optimal load group selection module is used for constructing a variable load evaluation model facing system disturbance by taking the identification load evaluation index minimization facing system disturbance as a target and taking the limit of the load bearing constraint force of the combined robot as a constraint condition, and selecting an optimal load group according to a model solving result;
The rigidity testing and identifying module is used for carrying out rigidity identification test on the robot by utilizing the parallel rope drive measuring and loading system based on the optimal load group, measuring coordinate values of the tail end target ball of the robot under different loads, calculating to obtain a deformation vector, and further calculating to obtain the joint rigidity of the robot.
In a third aspect, the invention also provides an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of the method of the first aspect.
In a fourth aspect, the present invention also provides a computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method of the first aspect.
The one or more of the above technical solutions have the following beneficial effects:
1. The invention provides a robot stiffness identification method and a system based on parallel rope drive loading multi-directional force, wherein an optimal load group is selected by constructing an anti-disturbance identification external load evaluation index, so that a data foundation is laid for follow-up accurate identification; the invention constructs a rigidity model with more generalization capability by the mode, obviously improves the positioning precision and the operation adaptability of the robot in complex tasks, eliminates the uncertainty caused by coupling effects in different directions, thereby avoiding the accumulation of errors in complex operation processes, providing more accurate data support for a rigidity compensation algorithm of the robot, greatly improving the reliability of the rigidity model and the compensation algorithm and ensuring the operation adaptability of the robot.
2. The invention provides an identification precision quantitative characterization method based on joint stiffness identification errors, establishes the association relation between the joint stiffness identification errors and the condition number of an observation matrix, and provides an anti-disturbance identification external load evaluation index by combining with an adaptive identification load evaluation index, so as to realize the integrity and accuracy of robot stiffness modeling.
3. The invention designs a parallel rope drive measuring and loading system, which is provided with a parallel rope drive force application loading platform, namely a force application loading experiment table which combines driving devices such as a motor or an air cylinder and mutually matched ropes, wherein a winding roller, an adjusting rope and a universal pulley which can be controlled in multiple directions are arranged, the length of the rope is controlled by the driving devices to change the loading force, and the application and adjustment of multi-degree-of-freedom, multi-angle and multi-direction external force loads are realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of robot stiffness identification based on parallel rope drive loading multi-directional forces in an embodiment of the invention;
FIG. 2 is a schematic diagram of a parallel rope drive measurement loading system according to an embodiment of the present invention;
FIG. 3 is a schematic view of a robot end force structure according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a robot end force loading point in an embodiment of the present invention;
FIG. 5 is a flow chart of a stiffness identification test for a robot using a parallel rope drive test loading system in an embodiment of the present invention.
The laser tracking device comprises a laser tracking instrument 1, a driving device 2, a three-pulley tension sensor 3, a universal pulley 4, a rigid upright post 5, a target ball cross 6, a stress device 7, a rigid base 8, a robot body 9, a robot connecting flange 10, a table body 11, a six-dimensional torque sensor 12, a sensor connecting flange 13, a 14, a rope 15 and a target ball.
Detailed Description
It should be noted that the following detailed description is exemplary only for the purpose of describing particular embodiments and is intended to provide further explanation of the invention and is not intended to limit exemplary embodiments according to the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components, and/or groups thereof.
Example 1
The embodiment provides a robot stiffness identification method based on parallel rope drive loading multi-directional force, which establishes an anti-disturbance identification external load evaluation index to select an optimal load group so as to lay a data foundation for follow-up accurate identification, and utilizes a parallel rope drive experiment measurement loading system to identify the stiffness characteristics of the robot in multiple degrees of freedom, multiple angles and multiple directions, so that accurate multi-directional stiffness measurement is realized, and the adaptability of the robot operation is ensured.
Specifically, considering that the robot system has nonlinear stiffness characteristics under different external loads, the load position is too close to the robot base or too far from the end effector of the robot, the stiffness matrix obtained through identification may not accurately reflect the overall nonlinear stiffness characteristics of the robot, especially when the dependency of the joint stiffness on the end effector position is strong. Therefore, in this embodiment, before external load application and rigidity identification of the robot are performed, related influencing factors and relationships thereof, which cause joint rigidity identification errors, are analyzed according to the structural characteristics of the robot and the identification requirements of the rigidity matrix, and appropriate external load directions and positions are selected on the basis of considering the effects of the influencing factors, so that each degree of freedom of the robot can be stimulated, and the accuracy of rigidity identification is improved.
Firstly, according to the comparison of the robot joint flexibility recognition vector X i and the joint flexibility actual vector X, a corresponding joint flexibility deviation vector is calculatedThe method comprises the following steps:
Considering that the relation between the stiffness and the flexibility of the robot joint is known, the stiffness and the flexibility are generally reciprocal, so that the stiffness deviation and the flexibility deviation have influence relation, and nonlinear influence change generally exists, and the joint stiffness identification error can be calculated according to the obtained joint flexibility deviation vector, and can be expressed as:
Secondly, defining an observation matrix in the process of identifying the rigidity of the robot, wherein the observation matrix represents a linearization relation between moment and dynamic parameters, and the defining process is as follows:
let the loading force applied by the robot tip be m= [ f x,fy,fz,wx,wy,wz]T, the robot tip deform to δ= [ σ x,σy,σz,σαx,σαy,σαz]T ], where [ f x,fy,fz]T is the force applied by the robot tip, [ w x,wy,wz]T is the moment applied by the robot tip, [ σ x,σy,σz]T and [ σα x,σαy,σαz]T are the translational and rotational deformations of the robot tip, respectively. According to hooke's law, there is a linear relationship between the loading force M applied to the robot tip and the deformation δ of the robot tip, which can be expressed as m=kδ, where K is a 6×6 robot tip stiffness matrix;
the robot joint stiffness matrix is Wherein, For the rigidity of each joint, according to a traditional rigidity model defined by a virtual joint method, a quantitative relation exists between a joint rigidity matrix and a terminal rigidity matrix, wherein the quantitative relation can be expressed as K=J -TKθJ-1, wherein J represents a jacobian matrix of a robot;
according to the relation between rigidity and flexibility, a joint flexibility matrix is defined as follows:
The joint flexibility matrix R is separated, so that the relation between the deformation delta of the tail end of the robot and the joint flexibility matrix R is delta=GR, wherein G is a matrix of 6 multiplied by 6, the matrix is defined as an observation matrix in the process of identifying the rigidity of the robot, and the observation matrix is only related to the jacobian matrix of the robot and the loading force born by the tail end of the robot.
On the basis of the observation matrix defined above, two matrices r and t are set in relation to the observation matrix G, wherein the matrix r is defined as r=g T G, the matrix t is defined as t=g T δ, and the matrix r is setAndRespectively establishing error relation between the observation matrix and the terminal deformation according to classical measuring and calculating error theory, wherein the relation can be obtained by two matrices r and t related to the observation matrix and the error items of the two matricesAndJoint compliance vector X and joint compliance bias vectorThe establishment can be expressed as:
finally, according to matrix norm correlation theory, combining the above formula (2), arranging the above formula (3), and solving the norm of the joint flexibility vector error to obtain the joint rigidity identification error, wherein the joint rigidity identification error is as follows:
The above formula (4) gives the upper bound of the joint stiffness identification error, and establishes the quantitative relation between the joint stiffness identification error and the condition number cond (r) of the matrix r, and according to the formula, if the condition number cond (r) of the matrix is reduced, the upper bound of the joint stiffness identification error can be reduced, so that the aim of improving the joint stiffness identification precision can be achieved.
Based on the analysis, considering that the adaptability reflects the adaptability and the performance capability of the robot to the rigidity characteristics under different load conditions, the robot rigidity generally shows nonlinear characteristics, the load change can lead to different rigidity responses, and the load selection strategy with high adaptability can help to reveal the rule of the rigidity characteristics along with the load change, so that the identification model can accurately describe the nonlinear behavior of the rigidity, and the load condition with good adaptability is selected, so that the condition that the load is too large to exceed the bearing range of the robot or the rigidity change is too small to be insignificant can be avoided as much as possible, thereby effectively reducing experimental errors and improving the identification precision. Therefore, in this embodiment, the adaptability of the robot to the applied external load is expressed by the reciprocal of the condition number of the normalized jacobian matrix, and the adaptability index is defined as R and expressed as:
the calculation formula of the condition number of the normalized Jacobian matrix is as follows:
Wherein J N is a normalized jacobian matrix, and the value range of k (J N) is k (J N) ∈ [1, + ], and the value range of the adaptability index R is R∈ (0, 1].
Further, when r=1, all singular values corresponding to the normalized jacobian matrix are equal, at this time, the multi-directional force loading parallel rope drive robot joint stiffness identification experiment table is far away from the singular pose, and the movement flexibility of the robot is better, so that the applied external load adaptability is better, and the condition number of the corresponding normalized jacobian matrix is smaller.
According to the analysis result, in the method for identifying the rigidity of the robot according to the present embodiment, an optimal load group is selected first, as shown in fig. 1, and the method includes the following steps:
Step S1, selecting a plurality of different applied external loads as an initial load group, and calculating to obtain an adaptability evaluation index of the initial load group and a condition number of an observation matrix corresponding to the initial load group, so as to establish an identification load evaluation index facing system disturbance, wherein the sizes and directions of the different external loads are different.
Specifically, 6 different applied external loads are selected as initial load groups, a normalized jacobian matrix J Ni (i=1, 2,.. 6) corresponding to each external load in the initial load groups is calculated respectively, wherein the normalization of the jacobian matrix of the robot can be obtained by introducing characteristic lengths and converting all physical quantities into dimensionless forms, further, according to the normalized jacobian matrix, the condition number k i(JN of the normalized jacobian matrix of each external load can be calculated (i=1, 2.. 6), the reciprocal of the condition number of the normalized jacobian matrix is taken as the adaptability of the robot to the applied external load, the adaptability evaluation index of the initial load groups is determined by taking the maximum value of the condition number of all the normalized jacobian matrices of the initial load groups, namely, the adaptability evaluation index of the initial load groups is marked as n 1＝max[ki(JN).
Then, according to the definition of the observation matrix, the observation matrix G corresponding to the initial load group is calculated and solved, and the condition number of the obtained observation matrix is calculated and is denoted as n 2 =cond (G).
And finally, according to the adaptability evaluation index of the initial load group and the condition number of the observation matrix, establishing an identification load evaluation index n=n 1×n2 facing the system disturbance, wherein the identification load group facing the system disturbance has a smaller n value, and the joint rigidity identification can be carried out by adopting the load group, so that higher precision can be obtained.
S2, constructing a variable load evaluation model facing system disturbance by taking the minimum of identification load evaluation indexes facing system disturbance as a target and taking the limit of load bearing constraint force of the combined robot as a constraint condition, and selecting an optimal load group according to a model solving result.
Specifically, based on the analysis, a variable load evaluation index model oriented to system disturbance is constructed, and the variable load evaluation index model is as follows:
the above formula (7) is a variable load evaluation index model for system disturbance, and uses the constraint force limit of the load borne by the robot as a constraint condition, wherein the upper limit F max is the maximum load borne by the robot, and the lower limit F min is 0.
Based on the variable load evaluation index model, solving by means of the fmincon function in MATLAB to obtain an optimal load group.
And S3, based on the optimal load group, performing a rigidity identification test on the robot by using a parallel rope drive measurement loading system, measuring coordinate values of a target ball at the tail end of the robot under different loads, calculating to obtain a deformation vector, and further calculating to obtain the joint rigidity of the robot.
Specifically, according to the optimal load group obtained by solving, a rigidity identification experiment is carried out on the robot, the robot is set in a group of initial postures, so that each joint has a certain flexibility to cope with external applied load, coordinate values of target balls of the robot under different loads are measured through a laser tester, and further deformation vectors (the vector values can be obtained by comparing terminal measuring point coordinate values under the load with terminal measuring point coordinate values under the unloaded state) are obtained through calculation, and therefore accurate robot joint rigidity can be obtained through data analysis and processing.
Further, on the basis of obtaining the deformation vector, the robot joint stiffness is obtained by calculating the optimal load group as the loading force M applied to the tail end of the robot, the obtained deformation vector as the tail end deformation delta of the robot, the tail end stiffness matrix K of the robot can be obtained by calculating according to the formula delta=K -1 M, the stiffness matrix K θ of the robot joint can be obtained by calculating according to the formula K=J -TKθJ-1, and finally the final robot joint stiffness can be obtained by adopting least square fitting calculation. Preferably, anisotropic comparison analysis may be performed to construct a stiffness model or the like based on the deformation vector obtained by calculation.
As shown in fig. 2 and 3, the present embodiment provides a parallel rope drive measurement loading system, where the system includes a robot body, a parallel rope drive force application loading platform, and the like, where the parallel rope drive force application loading platform includes a rope drive structure, and through reasonable configuration of the parallel rope drive force application loading platform and a corresponding sensing device in the system, accurate force loading, real-time force feedback, and displacement monitoring on the tail end of the robot can be achieved. Specifically, the system comprises a laser tracker 1, a driving device 2, a three-pulley tension sensor 3, a universal pulley 4, a rigid upright post 5, a target ball cross 6, a stress device 7, a rigid base 8, a robot body 9, a robot connecting flange 10, a table body 11, a six-dimensional moment sensor 12, a sensor connecting flange 13, a rope 14 and a target ball 15.
As shown in fig. 2, a robot body 9 to be tested is fixed at the center of a rigid base 8, the rigid base 8 is stably installed on the ground through foundation bolts so as to reduce interference of environmental vibration, the tail end of the robot body 9 is connected with a six-dimensional moment sensor 12 through a robot connecting flange 10, the six-dimensional moment sensor 12 is connected with a force-bearing device 7 through a sensor connecting flange 13, a target ball 15 is arranged on the outer side surface of the force-bearing device 7, a target ball cross 6 is sleeved on the target ball, and the laser tracker 1 is used for recording three-dimensional coordinate information of the target ball and each measuring point on the target ball cross. The six-dimensional moment sensor 12 is in communication with the central controller, and is used for collecting the magnitude and direction of the applied external load in real time and feeding back the magnitude and direction of the applied external load to the central controller, the fed back data can be used for tension and real-time measurement, and the stress device 7 applies external loads with different magnitudes and directions through a rope-driven structure which is distributed in a three-dimensional polyhedral layout and can cover different stress directions.
Further, the parallel rope driving force application loading platform comprises a table body 11, a plurality of rigid upright posts 5 are erected on the table body 11, a driving device 2 is arranged on each rigid upright post, a motor, a cylinder or the like can be adopted by the driving device 2, the rope driving structure comprises a plurality of ropes 14 which can be independently controlled, one ends of the ropes are respectively connected to a plurality of different positions of the force receiving device 7, the other ends of the ropes 14 are respectively wound on corresponding winding drums, and the winding drums are driven by the driving device 2. In this embodiment the force receiving means 7 applies a load via eight independently controllable ropes 14, the end of each rope 14 being connected to a respective drive means 2, capable of applying tension in different directions. The driving device is communicated with the central processing unit and is used for driving the winding roller to rotate forward and backward to control the winding and unwinding of the rope, so that the length of the rope 14 is changed, and the independent rope is controlled to apply external loads with different magnitudes to the stress device.
Preferably, the other end of the rope 14 passes through the three-pulley tension sensor 3 and the universal pulley 4 in sequence and then is wound on the winding drum, and the three-pulley tension sensor is used as a feedback device for collecting tension and feeding back to the central processing unit, so that the accuracy of loading force is ensured.
Further, as shown in fig. 5, the stiffness identification test is performed on the robot by using the parallel rope drive measurement loading system, and the process is as follows:
step 1, calibrating a six-dimensional moment sensor 12, a three-pulley tension sensor 3 and a laser tracker 1 by using a calibration instrument;
Step 2, initializing a robot body 9 to be tested, ensuring that each joint has no displacement or rotation in an initial state, and moving the joints to an initial measurement pose;
Step 3, recording three-dimensional coordinate information of each measuring point on the target ball 15 and the target ball cross 6 by using the laser tracker 1, controlling the driving device 2 to adjust the length of each rope 14, recording the tension of the corresponding rope 14 by using the three-pulley tension sensor 3, and recording the stress of the tail end of the robot body 9 by using the six-dimensional torque sensor 12;
Step 4, gradually adjusting the driving device 2 to control and adjust the length of the rope 14, and simultaneously manually adjusting the universal pulley 4 to adjust the angle of the rope to change the moment direction of the applied load, wherein the rope can apply each external load in the optimal load group obtained by solving the method in the adjusting mode, and the steps 3 and 4 are repeated until all external load force information and position coordinate information are obtained, wherein the obtained information specifically comprises the size and the direction of the stressed load of the tail end of the robot and the coordinate value of each measuring point of the tail end under different loads;
And 5, repeating the step 3 and the step 4 until all the external load groups are measured, and finishing the measurement.
Example two
The embodiment provides a robot rigidity identification system based on parallel rope drives loading multidirectional force, including:
The system comprises an identification load evaluation index construction module, a system disturbance-oriented identification load evaluation index, a load control module and a load control module, wherein the identification load evaluation index construction module is used for selecting a plurality of different applied external loads as an initial load group, calculating to obtain an adaptability evaluation index of the initial load group and a condition number of an observation matrix corresponding to the initial load group, and further establishing the identification load evaluation index oriented to the system disturbance;
The optimal load group selection module is used for constructing a variable load evaluation model facing system disturbance by taking the identification load evaluation index minimization facing system disturbance as a target and taking the limit of the load bearing constraint force of the combined robot as a constraint condition, and selecting an optimal load group according to a model solving result;
The rigidity testing and identifying module is used for carrying out rigidity identification test on the robot by utilizing the parallel rope drive measuring and loading system based on the optimal load group, measuring coordinate values of the tail end target ball of the robot under different loads, calculating to obtain a deformation vector, and further calculating to obtain the joint rigidity of the robot.
Example III
The embodiment provides an electronic device, which comprises a memory, a processor and computer instructions stored on the memory and running on the processor, wherein the computer instructions complete the steps in the robot stiffness identification method based on parallel rope drive loading multi-directional force.
Example IV
The present embodiment also provides a computer readable storage medium for storing computer instructions that, when executed by a processor, perform the steps in the robot stiffness identification method based on parallel rope loading multi-directional forces as described above.
The steps involved in the second to fourth embodiments correspond to the first embodiment of the method, and the detailed description of the second embodiment refers to the relevant description of the first embodiment. The term "computer-readable storage medium" shall be taken to include a single medium or multiple media that includes one or more sets of instructions, and shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the processor and that cause the processor to perform any one of the methodologies of the present invention.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented by general-purpose computer means, alternatively they may be implemented by program code executable by computing means, whereby they may be stored in storage means for execution by computing means, or they may be made into individual integrated circuit modules separately, or a plurality of modules or steps in them may be made into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
While the foregoing is illustrative of the preferred embodiments of the present invention, and while the present invention has been described in connection with the accompanying drawings, it is not intended to limit the scope of the invention, and it will be apparent to those skilled in the art, on the basis of the technical scheme of the invention, various modifications or variations which can be made by the person skilled in the art without the need of creative efforts are still within the protection scope of the invention.
Claims (9)
1. A robot rigidity identification method based on parallel rope drive loading multi-directional force is characterized by comprising the following steps:
optionally selecting a plurality of different applied external loads as an initial load group, and calculating to obtain an adaptability evaluation index of the initial load group and a condition number of an observation matrix corresponding to the initial load group, thereby establishing an identification load evaluation index facing system disturbance, wherein the sizes and directions of the different external loads are different;
the method comprises the steps of constructing a variable load evaluation model facing system disturbance by taking the minimum identification load evaluation index facing the system disturbance as a target and taking the limit of the load bearing constraint force of a combined robot as a constraint condition, and selecting an optimal load group according to a model solving result;
Based on the optimal load group, a parallel rope drive measurement loading system is utilized to carry out a rigidity identification test on the robot, coordinate values of a tail end target ball of the robot under different loads are measured, a deformation vector is calculated, and then the joint rigidity of the robot is calculated;
The parallel rope drive measurement loading system comprises a robot body and a parallel rope drive force application loading platform, wherein the parallel rope drive force application loading platform comprises a rope drive structure;
The robot body to be tested is fixed at the center of a rigid base, the rigid base is stably arranged on the ground through foundation bolts, the tail end of the robot body is connected with a six-dimensional moment sensor through a robot connecting flange, the six-dimensional moment sensor is connected with a stress device through a sensor connecting flange, a target ball is arranged on the outer side surface of the stress device, and a target ball cross is sleeved on the target ball;
The six-dimensional moment sensor is communicated with the central controller and is used for collecting the magnitude and the direction of the applied external load in real time and feeding the magnitude and the direction back to the central controller, the external load in different magnitudes and different directions is applied by the force bearing device through the rope drive structure, and the rope drive structure is distributed in a three-dimensional polyhedral layout and covers different force bearing directions.
2. The method for identifying the rigidity of the robot based on parallel rope driving loading multidirectional force according to claim 1, wherein the system disturbance-oriented identification load evaluation index construction process comprises the following steps:
optionally selecting a plurality of different applied external loads as an initial load group, respectively calculating normalized jacobian matrixes corresponding to each external load in the initial load group, and further solving to obtain the condition number of the normalized jacobian matrixes;
Taking the reciprocal of the condition number of the normalized jacobian matrix as the adaptability of the robot to the applied external load, taking the maximum value of the condition number of all the normalized jacobian matrices of the initial load group, and determining an adaptability evaluation index of the initial load group;
according to definition of the observation matrix, solving to obtain an observation matrix corresponding to the initial load group, and further calculating to obtain the condition number of the observation matrix;
Based on the adaptability evaluation index of the initial load group and the condition number of the observation matrix, an identification load evaluation index oriented to system disturbance is established.
3. The method for identifying the rigidity of the robot based on the parallel rope driving loading multidirectional force according to claim 1, wherein the parallel rope driving force application loading platform comprises a table body, a plurality of rigid upright posts are erected on the table body, and a driving device is arranged on each rigid upright post;
the driving device is communicated with the central processing unit and is used for driving the winding roller to rotate forward and backward to control the winding and unwinding of the rope and the change of the length, so that the independent rope is controlled to apply external loads with different magnitudes to the stress device.
4. The method for identifying the rigidity of the robot based on parallel rope driving loading multidirectional force according to claim 3, wherein the other end of the rope sequentially passes through a three-pulley tension sensor and a universal pulley and then is wound on a winding drum, and the three-pulley tension sensor is used as a feedback device for collecting tension and feeding back the tension to a central processing unit.
5. The method for identifying the rigidity of the robot based on parallel rope driving loading multi-directional force according to claim 2, further comprising a laser tracker for recording three-dimensional coordinate information of the target ball and each measuring point on the target ball cross.
6. The method for identifying the rigidity of the robot based on the parallel rope-driven loading multi-directional force according to claim 1, wherein the method for identifying the rigidity of the robot by using the parallel rope-driven measuring and loading system comprises the following steps:
step 1, calibrating a six-dimensional moment sensor, a three-pulley tension sensor and a laser tracker by using a calibration instrument;
step 2, initializing a robot to be tested, ensuring that each joint has no displacement or rotation in an initial state, and moving the robot to an initial measurement pose;
Step 3, recording three-dimensional coordinate information of the target ball and each measuring point on the target ball cross by using a laser tracker, controlling a driving device to adjust the length of each rope, recording the tension of the corresponding rope by using a three-pulley tension sensor, and recording the stress of the tail end of the robot by using a six-dimensional torque sensor;
Step 4, gradually adjusting the driving device to control and adjust the length of each rope, adjusting the angle of the universal pulley to adjust the angle of the rope, changing the moment direction of applied load, enabling the rope to apply each external load in the optimal load group, and acquiring external load force information and position coordinate information;
And 5, repeating the step 3 and the step 4 until all the external load groups are measured, and finishing the measurement.
7. A robot rigidity identification system based on parallel rope drives loading multidirectional force is characterized by comprising:
The system comprises an identification load evaluation index construction module, a system disturbance-oriented identification load evaluation index, a load control module and a load control module, wherein the identification load evaluation index construction module is used for selecting a plurality of different applied external loads as an initial load group, calculating to obtain an adaptability evaluation index of the initial load group and a condition number of an observation matrix corresponding to the initial load group, and further establishing the identification load evaluation index oriented to the system disturbance;
The optimal load group selection module is used for constructing a variable load evaluation model facing system disturbance by taking the identification load evaluation index minimization facing system disturbance as a target and taking the limit of the load bearing constraint force of the combined robot as a constraint condition, and selecting an optimal load group according to a model solving result;
The rigidity testing and identifying module is used for carrying out a rigidity identifying test on the robot by utilizing a parallel rope drive measuring and loading system based on the optimal load group, measuring coordinate values of a target ball at the tail end of the robot under different loads, calculating to obtain a deformation vector, and further calculating to obtain the joint rigidity of the robot;
The parallel rope drive measurement loading system comprises a robot body and a parallel rope drive force application loading platform, wherein the parallel rope drive force application loading platform comprises a rope drive structure;
The robot body to be tested is fixed at the center of a rigid base, the rigid base is stably arranged on the ground through foundation bolts, the tail end of the robot body is connected with a six-dimensional moment sensor through a robot connecting flange, the six-dimensional moment sensor is connected with a stress device through a sensor connecting flange, a target ball is arranged on the outer side surface of the stress device, and a target ball cross is sleeved on the target ball;
The six-dimensional moment sensor is communicated with the central controller and is used for collecting the magnitude and the direction of the applied external load in real time and feeding the magnitude and the direction back to the central controller, the external load in different magnitudes and different directions is applied by the force bearing device through the rope drive structure, and the rope drive structure is distributed in a three-dimensional polyhedral layout and covers different force bearing directions.
8. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of a method for identifying the stiffness of a robot based on parallel rope loading multidirectional forces according to any one of claims 1-6.
9. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of a method of robot stiffness identification based on parallel rope loading multi-directional forces as claimed in any one of claims 1-6.
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