CN117387834A - Force sensor calibration method, system, electronic equipment and storage medium - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
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Abstract
The application relates to a force sensor calibration method, a system, electronic equipment and a storage medium, wherein at least three groups of force data measured by the force sensor are acquired under different postures respectively; constructing a moment model of the force sensor according to pose information of the force sensor; and processing a moment matrix formed by each moment model through a least square method, and outputting the true value of the load tool coordinate. Compared with the method that the gravity of the load tool and the gravity of the sensor are required to be manually weighed and the readings of the sensor are required to be obtained under the conditions of two postures (the load tool is not installed and the load tool is installed) respectively, and then the calibration is achieved. The method solves the problem of complex steps of the six-dimensional force sensor calibration method in the related art, and improves the sensor calibration efficiency.
Description
Technical Field
The present disclosure relates to the field of force sensor calibration, and in particular, to a force sensor calibration method, system, computer device, and computer readable storage medium.
Background
Robots typically require handling tools during operation, which are mounted at the ends of force sensors. Under the influence of self gravity, the parameters acquired by the force sensor will be different under different gesture conditions (only static or low-speed motion conditions are considered), however, in order to realize accurate motion control of the robot, the gravity of the same load tool and the readings of the force sensor under different robot gestures should be consistent.
In addition, during operation, force interaction is often generated between the tool end and the environment, and due to the force arm action of the force action point and the force sensor coordinate system, the data collected by the force sensor is not the actual contact acting force between the end tool and the environment.
Thus, calibration of the force sensor for the load tool is required to keep the force sensor readings consistent for different robot poses and to bring the force sensor readings closer to the actual end tool contact force with the environment.
In the related art, the conventional calibration scheme is generally complicated in flow and poor in calibration result precision, and cannot simultaneously meet the requirements of eliminating the influence of the load gravity factor on the reading of the force sensor and reflecting the actual end contact force by the reading of the force sensor.
Disclosure of Invention
The embodiment of the application provides a force sensor calibration method, a device, a system, computer equipment and a computer readable storage medium, which are used for at least solving the problem of complicated calibration scheme flow in the related art.
In a first aspect, an embodiment of the present application provides a method for calibrating a force sensor, which is applied to calibration of a force sensor in a robot, where the force sensor is connected to a flange of the robot and a load tool, respectively, and the method includes:
responding to a data acquisition instruction, and respectively acquiring force data measured by at least three groups of force sensors under different postures, wherein the force data comprise a force value and a moment value, and the force data are acquired under the condition that the load tool has no external force;
responding to a data processing instruction, and constructing a moment model of the force sensor according to pose information of the force sensor, wherein the moment model is used for representing the relative relation among the moment value, the gravity component of the load tool on each axis, the barycentric coordinates of the load tool and the zero drift constant of the force sensor;
Based on moment models of the force sensor under different postures, constructing moment matrixes among the force data and the load tool coordinates;
and responding to a calibration result output instruction, processing the moment matrix through a least square method, and outputting a true value of the load tool coordinate, wherein the load tool coordinate is based on a force sensor coordinate system.
In some of these embodiments, constructing a force sensor moment model from pose information of the force sensor includes:
acquiring stress state information of the force sensor based on pose information of the force sensor;
constructing first association information among the gravity component, the action moment of the gravity component and the load tool coordinate based on the stress state information;
constructing second association information among the zero drift constant, the force data, the gravity component and the action moment of the gravity component based on the zero drift constant and the stress state information;
and constructing a moment model of the force sensor under any gesture based on the first association information and the second association information.
In some of these embodiments, the method further comprises:
Optimizing the torque model by replacing correlation information between torque zero drift parameters in the torque model and the load tool coordinates with a first correlation constant in response to data processing instructions,
based on the optimized moment models under different postures, constructing moment matrixes among the force data and the load tool coordinates of each group;
and processing the optimized moment matrix through a least square method, and outputting the actual value of the load tool coordinates and the actual value of the first association constant.
In some of these embodiments, after outputting the actual value of the load tool coordinates, the method further comprises:
responding to a calibration result output instruction, and acquiring a transformation matrix between the robot base coordinate system and the force sensor;
constructing a gravity value of the load tool, a gravity vector of the load tool under the force sensor coordinate system and third association information between the transformation matrix;
constructing a force model of the force sensor under any gesture based on the third association information and the second association information, and constructing moment arrays between each group of force data and the load tool coordinates based on the force models under different gestures;
According to the true value of the coordinates of the load tool, the moment array is processed through a least square method, and the actual value of the gravity of the load tool and the actual value of the moment component zero drift constant of the force sensor are output;
and obtaining the actual value of the force component zero drift constant of the force sensor according to the actual value of the moment component zero drift constant and the actual value of the first correlation constant.
In some of these embodiments, the acquiring a transformation matrix between the robot base coordinate system and the force sensor comprises:
acquiring a relative distance between the force sensor and the robot flange, and acquiring a first rotation matrix between a flange coordinate system and a force sensor coordinate system according to the relative distance;
acquiring a second rotation matrix of a robot base coordinate system and a flange terminal coordinate system according to the first rotation matrix and the robot positive kinematic model;
a transformation matrix between the robot base coordinate system and the force sensor is determined based on the first rotation matrix and the second rotation matrix.
In some of these embodiments, the method further comprises:
responding to a gravity compensation instruction, and acquiring force data after gravity compensation according to an actual value of the load tool coordinate, a gravity value of the load tool, a force component zero drift constant and a moment zero constant of the force sensor and gesture information of the robot;
And responding to an actual acting force acquisition instruction, and acquiring the actual contact acting force of the load tool tail end and the environment according to the force data after force compensation, a rotation transformation matrix and a translation transformation matrix between a sensor coordinate system and a tool tail end coordinate system.
In a second aspect, an embodiment of the present application provides a force sensor calibration system, which is characterized by being applied to calibration of a force sensor in a robot, wherein the force sensor is connected to a flange of the robot and a load tool, respectively, and the system comprises: the device comprises a data acquisition module, a data processing module and a calibration result output module;
the data acquisition module is used for: responding to a data acquisition instruction, and respectively acquiring force data measured by at least three groups of force sensors under different postures, wherein the force data comprise a force value and a moment value, and the force data are acquired under the condition that the load tool has no external force;
the data processing module is used for: responding to a data processing instruction, and constructing a moment model of the force sensor according to pose information of the force sensor, wherein the moment model is used for representing the relative relation among the moment value, the gravity component of the load tool on each axis, the barycentric coordinates of the load tool and the zero drift constant of the force sensor;
Based on moment models of the force sensor under different postures, constructing moment matrixes among the force data and the load tool coordinates;
the calibration result output module is used for processing the moment matrix through a least square method and outputting a true value of the load tool coordinate, wherein the load tool coordinate is based on a force sensor coordinate system.
In some of these embodiments, the system further comprises an optimization control module, wherein:
the optimization control module is used for: responding to a gravity compensation instruction, and acquiring force data after gravity compensation according to an actual value of the load tool coordinate, a gravity value of the load tool, a force component zero drift constant and a moment zero constant of the force sensor and gesture information of the robot;
and responding to an actual acting force acquisition instruction, and acquiring the actual contact acting force of the load tool tail end and the environment according to the force data after force compensation, a rotation transformation matrix and a translation transformation matrix between a sensor coordinate system and a tool tail end coordinate system.
In a third aspect, embodiments of the present application provide a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to the first aspect described above when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as described in the first aspect above.
Compared with the related art, the calibration method of the six-dimensional force sensor provided by the embodiment of the application collects force data measured by at least three groups of force sensors under different postures respectively; constructing a moment model of the force sensor according to pose information of the force sensor; further, a moment matrix formed by each moment model is processed through a least square method, and a true value of the load tool coordinate is output. Compared with the method that the gravity of the load tool and the gravity of the sensor are required to be manually weighed in the related art, and the readings of the sensor are required to be obtained under the conditions of two postures (the load tool is not installed and the load tool is installed) respectively, so that the calibration is realized. Therefore, the problem of complex steps of a six-dimensional force sensor calibration method in the related art is solved, and the sensor calibration efficiency is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a schematic view of an application environment of a six-dimensional force sensor calibration method according to an embodiment of the present application;
FIG. 2 is a flow chart of a six-dimensional force sensor calibration method according to an embodiment of the present application;
FIG. 3 is a flow chart of determining a moment model of a force sensor according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a force sensor stress state according to an embodiment of the present application;
FIG. 5 is a block diagram of a force sensor calibration system according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a force sensor calibration system calibration logic according to an embodiment of the present application;
fig. 7 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described and illustrated below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on the embodiments provided herein, are intended to be within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the embodiments described herein can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar terms herein do not denote a limitation of quantity, but rather denote the singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein refers to two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
Fig. 1 is a schematic view of an application environment of a six-dimensional force sensor calibration method according to an embodiment of the present application, as shown in fig. 1, an end load tool 12 such as a clamp, a suction cup, etc. is typically connected to an end of a robot flange 10, and in order to implement motion control of a robot such as impedance control, admittance control, and force-position hybrid control, a force sensor 11 is installed between the load tool 12 and the robot flange 10, and force data of the end tool of the robot in a current posture can be detected in real time through the force sensor 11, so as to provide data support for the motion control of the robot. Further, the end load tool 12 will cause a deviation of the values collected by the force sensor 11 due to the influence of its own gravity, so that the readings of the force sensor 11 cannot fully reflect the condition of the end acting force, and further the accuracy of the robot motion control is affected. Therefore, in order to keep the readings of the force sensor of the same tool load gravity under different robot postures consistent, the data of the force sensor 11 for compensation and correction can be obtained through the calibration of the force sensor, and accurate data support is provided for the motion control of the robot.
Fig. 2 is a flowchart of a calibration method of a six-dimensional force sensor according to an embodiment of the present application, as shown in fig. 2, the flowchart includes the following steps:
S201, building an application environment for calibrating a force sensor;
specific means of this step include, but are not limited to:
(1) Mounting a force sensor and an end load tool; a specific installation mode can be shown in fig. 1;
(2) Setting working parameters and acquisition frequency of a force sensor; specifically, the method comprises the steps of setting communication parameters, measuring range parameters, sensitivity, sampling frequency and the like; it can be appreciated that specific parameter settings can be flexibly set in combination with specific application scenarios according to experience knowledge in the art;
(3) Starting a force sensor; the method comprises the steps of starting a force sensor, opening a transmission channel between the force sensor and a controller and the like;
(4) Setting the gesture of the robot required for collecting force data; wherein, can set up at least three robot gesture through robot servo controller. It should be noted that in order to avoid the occurrence of a pathological matrix in the subsequent equation calculation, N is more than or equal to 3, and the pointing vectors of the end polishing heads of the robot under these postures are not coplanar as much as possible.
S202, responding to data acquisition instructions, and respectively acquiring force data measured by at least three groups of force sensors under the action of no external force under different postures, wherein the force data comprise force values and moment values;
The execution target of the step is a robot controller or an external computing device, and the data acquisition instruction may be input to the robot controller by a user operating the external device or may be generated by an automated program;
the controller collects corresponding force data under the three groups of poses set in the step S201 respectively; it will be appreciated that to achieve calibration of the force sensor, the force data should be collected without the action of external forces by the load tool;
in the present embodiment, the force sensor is a six-dimensional force sensor capable of measuring forces in three spatial directions and moments (torques) in three rotational directions; it is often used in robotics, mechanical system control, and other applications where accurate measurements of forces and moments applied by objects are required.
S203, responding to the data processing instruction, determining a moment model of the force sensor according to pose information of the force sensor, and constructing moment matrixes among various groups of force data and load tool coordinates based on the moment models of the force sensor under different poses; the moment model is used for representing the relative relation among moment values, gravity components of the load tool on all coordinate axes, barycentric coordinates of the load tool and zero drift constants of the force sensor;
Specifically, fig. 3 is a flowchart for determining a moment model of a force sensor according to an embodiment of the present application, and as shown in fig. 3, the flowchart includes the following steps:
s2031, acquiring stress state information of a force sensor based on pose information of the force sensor, and constructing first association information among a gravity component, an action moment of the gravity component and a load tool coordinate based on the stress state information;
FIG. 4 is a schematic diagram of a force sensor stress state, as shown in FIG. 4, { O } represents a force sensor coordinate system, { T } represents a tool coordinate system, { G } represents a gravity coordinate system relative to a six-dimensional force sensor load, according to an embodiment of the present application;
referring to the stress state shown in fig. 4, and the relationship of the binding force and the moment, a first correlation information equation can be constructed as shown in the following expression (1), wherein the first correlation information equation reflects the relative relationship among the moment value, the gravity components of the load tool on the respective coordinate axes, and the zero drift constant of the force sensor:
（1）
wherein,load gravity +.>For->Is used for controlling the action moment of the (a),for the center of gravity of the load->In force sensor coordinate system->Coordinates of->Respectively->At the position ofAn action component of the direction.
S2032, constructing second association information among the zero drift constant, the force data, the gravity component and the acting moment thereof based on the zero drift constant and the stress state information;
in the field, after a load tool is installed on the force sensor, the reading of the force sensor is measured on the premise of no external force, and is actually the sum of zero drift of the force sensor, the gravity of the tool and the gravity of the force sensor; it can be known that although the reading can reflect the whole data, the zero drift value and the gravity value cannot be directly known before the calibration operation;
however, as is known in the art, zero drift refers to the offset between the zero value of the force sensor output and its ideal zero value, which is a fixed constant;
thus, optionally, the zero drift value of the force sensor is set toAnd further constructing second correlation information between the zero drift constant, the force data, and the gravity component and the acting moment thereof, wherein the second correlation information can be expressed by the following expression (2):
wherein,representing force readings on three axes of a set of force sensors,/->Representing torque readings on three axes of the force sensor.
S2033, constructing a moment model of the force sensor under any posture based on the first association information and the second association information;
wherein, by bringing expression (2) into expression (1), a moment model of the force sensor under any posture is constructed, specifically, in an embodiment, the moment model is as shown in expression (3) below:
（3）
wherein the zero drift value of the force sensorAnd the center of gravity of the load +.>Coordinates in force sensor coordinate system, +.>All constants are constant, but the actual value is unknown in this step. In addition, the moment model is a mathematical model, and can represent the relative relation between the moment value read by the force sensor and the force sensor parameter actually required to be calibrated in any pose.
Further, in order to facilitate the calculation of the actual values of the above parameters, the step further includes:
in response to the data processing instruction, optimizing the moment model by replacing correlation information between moment zero drift parameters in the moment model and the load tool coordinates with a first correlation constant; in one embodiment, the moment model is optimized by;
first, the correlation information between the moment zero drift parameter and the load tool coordinate in the moment model is represented by a first correlation constant, and specifically: the following expression (4) shows:
（4）
Further, the expression (4) is brought into the expression (3) and converted into a matrix form as shown in the following expression (5), so that the moment model is optimized:
（5）
it can be understood that the above expression (5) represents a relationship between the readings of the force sensor and the related calibration data in a posture, and further, the expression (5) is extended to multiple groups of force sensor data, so as to obtain the moment matrix, and specifically, one moment matrix is shown in the following expression (6):
（6）
wherein,show the first->The force readings on the three axes of the group force sensor,representing torque readings on three axes of the force sensor.
S204, responding to the calibration result output instruction, processing the moment matrix through a least square method, and outputting a true value of a load tool coordinate, wherein the load tool coordinate is based on a force sensor coordinate system.
Wherein the moment matrix shown in the above expression (6) can be converted into the following form:
（7）
wherein,further go intoSolving the least squares solution of the above expression (7), and +.>The load center of gravity +.>In force sensor coordinate system->Coordinates of (a)Constant->。
The core idea of the least square method is to estimate the parameters by minimizing the sum of squares of residuals between the observed values and the predicted values. In this embodiment, F represents a matrix of forces measured by the force sensor, M represents a column vector of moments measured by the force sensor, and the objective of this step is to find a coefficient matrix a determined by a numerical value by a least square method, so that As close as possible to M.
It can be understood that in this step, on the basis of constructing the moment matrix, the load tool center of gravity is estimated to be the load center of gravity by the least square methodIn force sensor coordinate system->Coordinates of->Constant->。
Through the steps S201 to S204, a moment matrix among all parameters is constructed according to the stress condition of the force sensor and the readings of a plurality of groups of force sensors, and further, the gravity center coordinates of the load tool are efficiently and conveniently obtained through a least square method.
Further, in order to further complete calibration of the force sensor, the weight of the load tool and the zero drift constant of the force sensor need to be obtained, wherein:
(1) The method for obtaining the quality of the load tool comprises the following steps:
step1, responding to a calibration result and outputting an instruction, and acquiring a transformation matrix between a robot base coordinate system and a force sensor;
specifically, step1 further includes the following steps:
step1.1, acquiring a relative distance between the force sensor and the robot flange, and acquiring a first rotation matrix between a flange coordinate system and the force sensor coordinate system according to the relative distanceWherein, can guarantee the relative position between robot flange and the sensor through the locating pin between force transducer and the flange.
Step1.2, obtaining a second rotation matrix of the robot base coordinate system and the flange end coordinate system according to the first rotation matrix and the robot positive kinematic model；
Wherein a robot positive kinematic model is used to describe how the position and pose of the robot end effector (tool or hand) depends on the joint angle. The goal is to calculate the position and pose of the end effector of the robot by known joint angles. Since this step is a conventional technical means in the art, details of the implementation of this step will not be described in detail in this embodiment.
Step1.3, determining a transformation matrix between the robot base coordinate system and the force sensor based on the first rotation matrix and the second rotation matrix; constructing a gravity value of a load tool, and third association information between a gravity vector of the load tool under a force sensor coordinate system and a transformation matrix;
specifically, according to the matrix transformation relationship, the transformation matrix can be expressed asThe method comprises the steps of carrying out a first treatment on the surface of the The third association information may be expressed as: />。
Step2, constructing a force model of the force sensor under any posture based on the third association information and the second association information, and constructing moment arrays between each group of force data and the load tool coordinates based on the force models under different postures;
Specifically, in combination with the mathematical expression of the above-described third association information and second association information, the following expression (9) may be output:
（9）
further, it is rewritten as follows:
（10）
wherein,is->The unit matrix takes N different postures of the robots, and the expression (10) is expanded to force data under each posture, so that a force matrix shown in the following expression (11) can be obtained;
（11）
step3, acquiring the actual value of the gravity of the load tool and the actual value of the moment component zero drift constant of the force sensor based on the force matrix and the actual value of the coordinates of the load tool;
specific: the expression is [ (]11 Expressed as expression (12)；
Wherein,indicate->Force readings on three axes of the group force sensor, < +.>Indicate->Rotation matrix transformation of group robot base to force sensor +.>Representing the vector of gravity in the base coordinate system,。
further, the expression (12) is transformed, i.e. simultaneously left-multipliedThe method can obtain: (13)Finally, carrying out pre-estimation solution through a least square method to obtain specific parameters in L;
after obtaining、/>、/>Then, it is further possible to find the gravity true value of the load tool by the following expression (14):
（14）
The zero point values of three force components of the six-dimensional force sensor are obtained through the steps Step1 to Step3And load tool gravity->。
Still further, the method will calculateSubstituting expression (4) can find zero drift constants of three moment components, specifically, by expression (15) as follows:
（15）
in the prior art, the weight of the load tool and the weight of the force sensor are required to be weighed in advance, and then the zero drift constant of the force sensor is obtained by subtracting the gravity of the force sensor and the load tool from the numerical value read by the force sensor; the method not only needs the step of weighing in advance, but also needs to obtain readings when the load tool is installed and the load tool is not installed, namely, the gesture adjustment needs to be carried out twice, so that the problem of complex and tedious steps exists.
By the method provided in the steps S201 to S204, at least three groups of readings are required to be obtained under the attitude of installing the load tool, the gravity values of the load tool and the force sensor are not required to be obtained in advance in a weighing mode, the readings of the force sensor and the kinematics knowledge are combined to construct a relevant moment matrix and a moment matrix, and then the actual gravity value of the load tool, the zero drift constant of the force sensor and the coordinate value of the gravity center of the load tool under the coordinate system of the force sensor are obtained in a least square mode, so that the calibration of the force sensor is completed. Compared with the prior art, the calibration efficiency of the six-dimensional force sensor can be greatly improved.
In addition, it should be noted that, the method provided by the embodiment only needs a series of different general postures of at least 3 robots, the calibration efficiency is high, the motion amplitude is small, the calibration can be performed even if the space is small, and compared with the traditional technology, the limitation is smaller; in addition, in the embodiment, the accuracy of the calculated calibration result is higher as the coupling effect among the parameters is fully considered according to the simultaneous modeling solution of all the parameters.
Furthermore, in the practical application scenario, the influence of the zero point of the force sensor and the load tool can be eliminated according to the calibrated parameters, so as to realize gravity compensation operation, and the gravity compensation operation is realized specifically by the following expressions (16) and (17):
（16）
（17）
wherein,representing the force sensor readings after the gravity compensation calculation. When the robot gravity compensation is needed, the gravity compensation is used for counteracting the joint moment caused by gravity in the motion process of the robot, particularly when an accurate motion task is executed, the motion control performance of the robot under the action of gravity is improved by the gravity compensation, so that the robot can be more suitable for the task requiring high precision and high performance.
In addition, the end tool coordinate system is the position where the robot actually performs the task, and therefore, the end tool coordinate system is subjected to the gravity compensation calculation to meet the requirements of task execution and control Then, it should also be converted into the end tool coordinate system by a companion matrix; furthermore, the actual acting force of the tail end of the load tool can be obtained by using the calibrated parameters, and the load tool has the advantages ofThe body can be realized by the following expression (18):
（18）
wherein,representing a rotation matrix transformation from the force sensor coordinate system to the tool end coordinate system, < >>Representing a translation transformation from the force sensor coordinate system to the tool end coordinate system,/>Representing an antisymmetric matrix. />Representing the forces and moments in the end tool coordinate system, respectively,/->Representing the force and moment in the force sensor coordinate system, respectively. The result after the treatment is the actual end tool contact force with the environment. The data at this time can be directly applied to the force control algorithm as input to the subsequent force control algorithm.
Through the steps, the influence of the zero point of the sensor and the load gravity on the stress sensing is eliminated in one step, so that the external acting force and moment borne by the end load of the robot are accurately obtained as the input of a subsequent force control algorithm, and the requirements of eliminating the influence of the load gravity factor on the reading of the force sensor and reflecting the actual end contact force by the reading of the force sensor can be simultaneously met.
It should be noted that the steps illustrated in the above-described flow or flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
The embodiment also provides a calibration system of the six-dimensional force sensor, which is used for realizing the above embodiment and the preferred implementation manner, and the description is omitted. As used below, the terms "module," "unit," "sub-unit," and the like may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
FIG. 5 is a block diagram of a force sensor calibration system according to an embodiment of the present application, as shown in FIG. 5, including calibration of force sensors applied to a robot, wherein a data acquisition module force sensor is coupled to a robot flange and a load tool, respectively, the data acquisition module system comprising: the device comprises a data acquisition module 50, a data processing module 51 and a calibration result output module 52;
The data acquisition module 50 is configured to: responding to the data acquisition instructions, and respectively acquiring force data measured by the force sensors of at least three groups of data acquisition modules under different postures, wherein the force data comprises a force value and a moment value, and the force data is acquired under the condition that a load tool has no external force;
the data processing module 51 is configured to: responding to the data processing instruction, and constructing a moment model of the force sensor according to pose information of the force sensor, wherein the moment model is used for representing the relative relation among moment values, gravity components of a load tool on each axis, barycentric coordinates of the load tool and zero drift constants of the force sensor; based on moment models of the force sensor under different postures, constructing moment matrixes among various groups of force data and load tool coordinates;
the calibration result output module 52 is configured to process the moment matrix by a least square method, and output a real value of a load tool coordinate, where the load tool coordinate is based on a force sensor coordinate system.
Through the system, the gravity values of the load tool and the force sensor are not required to be obtained in advance in a weighing mode, the force sensor readings and the kinematics knowledge are combined to construct a relevant moment matrix and a moment matrix, and then the gravity actual value of the load tool, the zero drift constant of the force sensor and the coordinate value of the gravity center of the load tool under the coordinate system of the force sensor can be obtained in a least square mode, so that the calibration of the force sensor is completed, and compared with the traditional technology, the calibration efficiency of the six-dimensional force sensor can be greatly improved.
Further, FIG. 6 is a schematic diagram of a force sensor calibration system calibration logic according to an embodiment of the present application.
In one embodiment, fig. 7 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, as shown in fig. 7, and an electronic device, which may be a server, may be provided, and an internal structure diagram thereof may be shown in fig. 7. The electronic device includes a processor, a network interface, an internal memory, and a non-volatile memory connected by an internal bus, where the non-volatile memory stores an operating system, computer programs, and a database. The processor is used for providing computing and control capability, the network interface is used for communicating with an external terminal through network connection, the internal memory is used for providing environment for the operation of an operating system, and the computer program is executed by the processor and adopts a calibration method of a force sensor, and the database is used for storing data.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the electronic device to which the present application is applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description. The technical features of the above embodiment of the data acquisition module may be arbitrarily combined, and for brevity of description, all possible combinations of the technical features of the above embodiment are not described, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method for calibrating a force sensor, applied to calibration of a force sensor in a robot, wherein the force sensor is connected to a robot flange and a load tool, respectively, the method comprising:
Responding to a data acquisition instruction, and respectively acquiring force data measured by at least three groups of force sensors under different postures, wherein the force data comprise a force value and a moment value, and the force data are acquired under the condition that the load tool has no external force;
responding to a data processing instruction, and constructing a moment model of the force sensor according to pose information of the force sensor, wherein the moment model is used for representing the relative relation among the moment value, the gravity component of the load tool on each axis, the barycentric coordinates of the load tool and the zero drift constant of the force sensor;
based on moment models of the force sensor under different postures, constructing moment matrixes among the force data and the load tool coordinates;
and responding to a calibration result output instruction, processing the moment matrix through a least square method, and outputting a true value of the load tool coordinate, wherein the load tool coordinate is based on a force sensor coordinate system.
2. The method of claim 1, wherein constructing a force sensor moment model from pose information of the force sensor comprises:
acquiring stress state information of the force sensor based on pose information of the force sensor;
Constructing first association information among the gravity component, the action moment of the gravity component and the load tool coordinate based on the stress state information;
constructing second association information among the zero drift constant, the force data, the gravity component and the action moment of the gravity component based on the zero drift constant and the stress state information;
and constructing a moment model of the force sensor under any gesture based on the first association information and the second association information.
3. The method according to claim 2, wherein the method further comprises:
optimizing the torque model by replacing correlation information between torque zero drift parameters in the torque model and the load tool coordinates with a first correlation constant in response to data processing instructions,
based on the optimized moment models under different postures, constructing moment matrixes among the force data and the load tool coordinates of each group;
and processing the optimized moment matrix through a least square method, and outputting the actual value of the load tool coordinates and the actual value of the first association constant.
4. A method according to any one of claims 2 to 3, wherein after outputting the actual value of the load tool coordinates, the method further comprises:
responding to a calibration result output instruction, and acquiring a transformation matrix between the robot base coordinate system and the force sensor;
constructing a gravity value of the load tool, a gravity vector of the load tool under the force sensor coordinate system and third association information between the transformation matrix;
constructing a force model of the force sensor under any gesture based on the third association information and the second association information, and constructing moment arrays between each group of force data and the load tool coordinates based on the force models under different gestures;
according to the true value of the coordinates of the load tool, the moment array is processed through a least square method, and the actual value of the gravity of the load tool and the actual value of the moment component zero drift constant of the force sensor are output;
and obtaining the actual value of the force component zero drift constant of the force sensor according to the actual value of the moment component zero drift constant and the actual value of the first correlation constant.
5. The method of claim 4, wherein the obtaining a transformation matrix between the robot base coordinate system and the force sensor comprises:
Acquiring a relative distance between the force sensor and the robot flange, and acquiring a first rotation matrix between a flange coordinate system and a force sensor coordinate system according to the relative distance;
acquiring a second rotation matrix of a robot base coordinate system and a flange terminal coordinate system according to the first rotation matrix and the robot positive kinematic model;
a transformation matrix between the robot base coordinate system and the force sensor is determined based on the first rotation matrix and the second rotation matrix.
6. The method according to claim 1, wherein the method further comprises:
responding to a gravity compensation instruction, and acquiring force data after gravity compensation according to an actual value of the load tool coordinate, a gravity value of the load tool, a force component zero drift constant and a moment zero constant of the force sensor and gesture information of the robot;
and responding to an actual acting force acquisition instruction, and acquiring the actual contact acting force of the load tool tail end and the environment according to the force data after force compensation, a rotation transformation matrix and a translation transformation matrix between a sensor coordinate system and a tool tail end coordinate system.
7. A force sensor calibration system for calibration of a force sensor in a robot, wherein the force sensor is connected to a robot flange and a load tool, respectively, the system comprising: the device comprises a data acquisition module, a data processing module and a calibration result output module;
the data acquisition module is used for: responding to a data acquisition instruction, and respectively acquiring force data measured by at least three groups of force sensors under different postures, wherein the force data comprise a force value and a moment value, and the force data are acquired under the condition that the load tool has no external force;
the data processing module is used for: responding to a data processing instruction, and constructing a moment model of the force sensor according to pose information of the force sensor, wherein the moment model is used for representing the relative relation among the moment value, the gravity component of the load tool on each axis, the barycentric coordinates of the load tool and the zero drift constant of the force sensor;
based on moment models of the force sensor under different postures, constructing moment matrixes among the force data and the load tool coordinates;
the calibration result output module is used for processing the moment matrix through a least square method and outputting a true value of the load tool coordinate, wherein the load tool coordinate is based on a force sensor coordinate system.
8. The system of claim 7, further comprising an optimization control module, wherein:
the optimization control module is used for: responding to a gravity compensation instruction, and acquiring force data after gravity compensation according to an actual value of the load tool coordinate, a gravity value of the load tool, a force component zero drift constant and a moment zero constant of the force sensor and gesture information of the robot;
and responding to an actual acting force acquisition instruction, and acquiring the actual contact acting force of the load tool tail end and the environment according to the force data after force compensation, a rotation transformation matrix and a translation transformation matrix between a sensor coordinate system and a tool tail end coordinate system.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 6 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1 to 6.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08257975A (en) * | 1995-03-23 | 1996-10-08 | Agency Of Ind Science & Technol | Force-control robot compensating force detection |
| CN105643641A (en) * | 2014-11-11 | 2016-06-08 | 沈阳新松机器人自动化股份有限公司 | Force sensor calibration device and method and force control robot |
| CN108716962A (en) * | 2018-05-10 | 2018-10-30 | 珞石（山东）智能科技有限公司 | Robot end's force snesor zero bias scaling method synchronous with load parameter |
| CN109822574A (en) * | 2019-03-20 | 2019-05-31 | 华中科技大学 | A kind of method of industrial robot end six-dimension force sensor calibration |
| US20200070369A1 (en) * | 2018-08-31 | 2020-03-05 | Samsung Electronics Co., Ltd. | Electronic device and method for calculating at least one parameter for measuring external force |
| CN111189577A (en) * | 2020-01-16 | 2020-05-22 | 腾讯科技（深圳）有限公司 | Sensor calibration and data measurement method, device, equipment and storage medium |
| CN112710424A (en) * | 2020-12-08 | 2021-04-27 | 上海交通大学 | Method for calibrating six-dimensional force sensor at tail end of robot |
| JP7127897B1 (en) * | 2021-04-26 | 2022-08-30 | 株式会社トライフォース・マネジメント | How to calibrate the force sensor |
| CN116481695A (en) * | 2023-04-24 | 2023-07-25 | 广州艾目易科技有限公司 | Method, device, equipment and medium for determining actual acting force of surgical robot |
-
2023
- 2023-12-04 CN CN202311641460.8A patent/CN117387834B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08257975A (en) * | 1995-03-23 | 1996-10-08 | Agency Of Ind Science & Technol | Force-control robot compensating force detection |
| CN105643641A (en) * | 2014-11-11 | 2016-06-08 | 沈阳新松机器人自动化股份有限公司 | Force sensor calibration device and method and force control robot |
| CN108716962A (en) * | 2018-05-10 | 2018-10-30 | 珞石（山东）智能科技有限公司 | Robot end's force snesor zero bias scaling method synchronous with load parameter |
| US20200070369A1 (en) * | 2018-08-31 | 2020-03-05 | Samsung Electronics Co., Ltd. | Electronic device and method for calculating at least one parameter for measuring external force |
| CN109822574A (en) * | 2019-03-20 | 2019-05-31 | 华中科技大学 | A kind of method of industrial robot end six-dimension force sensor calibration |
| CN111189577A (en) * | 2020-01-16 | 2020-05-22 | 腾讯科技（深圳）有限公司 | Sensor calibration and data measurement method, device, equipment and storage medium |
| WO2021143294A1 (en) * | 2020-01-16 | 2021-07-22 | 腾讯科技（深圳）有限公司 | Sensor calibration method and apparatus, data measurement method and apparatus, device, and storage medium |
| CN112710424A (en) * | 2020-12-08 | 2021-04-27 | 上海交通大学 | Method for calibrating six-dimensional force sensor at tail end of robot |
| JP7127897B1 (en) * | 2021-04-26 | 2022-08-30 | 株式会社トライフォース・マネジメント | How to calibrate the force sensor |
| CN116481695A (en) * | 2023-04-24 | 2023-07-25 | 广州艾目易科技有限公司 | Method, device, equipment and medium for determining actual acting force of surgical robot |
Non-Patent Citations (3)
| Title |
|---|
| XUTING YANG: "Force Perception of Industrial Robot Based on Multi-parameter Coupled Model", 2019 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO), 1 December 2019 (2019-12-01), pages 1676 - 1681, XP033691709, DOI: 10.1109/ROBIO49542.2019.8961653 * |
| 刘岩;武俊峰;夏科睿;马常友;: "机械臂腕力传感器负载端重力补偿算法与仿真", 哈尔滨理工大学学报, no. 02, 10 April 2018 (2018-04-10) * |
| 魏秀权;吴林;高洪明;李海超;: "遥控焊接工具装配力控制的重力补偿算法", 焊接学报, no. 04, 25 April 2009 (2009-04-25) * |
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