CN117840997A - Compensation method and device for mechanical data, equipment and medium - Google Patents
Compensation method and device for mechanical data, equipment and medium Download PDFInfo
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- CN117840997A CN117840997A CN202410068567.6A CN202410068567A CN117840997A CN 117840997 A CN117840997 A CN 117840997A CN 202410068567 A CN202410068567 A CN 202410068567A CN 117840997 A CN117840997 A CN 117840997A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
- B25J9/163—Programme controls characterised by the control loop learning, adaptive, model based, rule based expert control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
- B25J9/1633—Programme controls characterised by the control loop compliant, force, torque control, e.g. combined with position control
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Abstract
The method comprises the steps of obtaining current mechanical data and reference information of mechanical data of a force sensor under the current posture of the tail end of a mechanical arm, inputting the current posture of the tail end of the mechanical arm and the reference information of the mechanical data into a mechanical data prediction model, and obtaining mechanical prediction data of the force sensor under the condition that a front shaft of the mechanical arm keeps stationary; and compensating the current mechanical data of the force sensor by using the mechanical prediction data of the force sensor in the state that the front shaft of the mechanical arm is kept stationary. The method can improve the compensation precision of the mechanical data acquired by the six-dimensional force sensor.
Description
Technical Field
The present invention relates to the field of intelligent control technologies, and in particular, to a method, an apparatus, a device, and a medium for compensating mechanical data.
Background
In the use process of the mechanical arm, as the task scene that the tool at the tail end of the mechanical arm is in contact with the environment is involved in assembly, polishing and the like, a force sensor (mostly using a six-dimensional force sensor) is often required to be installed at the tail end of the mechanical arm to acquire the information of the environment contact force/moment, so that the tail end force control of the mechanical arm is realized, and the stability and the accuracy of the tasks such as assembly, polishing and the like are ensured.
Taking a six-dimensional force sensor as an example, the six-dimensional force sensor is generally arranged at the rear side of the tail end of the mechanical arm and the front side of the tail end tool, so that the tail end contact force/moment data of the mechanical arm are accurately measured, and meanwhile, the tail end tool is prevented from being influenced to complete a set task. However, the installation of the end-of-arm tool affects the accuracy of the measurement data collected by the six-dimensional force sensor, and therefore, compensation of the mechanical data collected by the six-dimensional force sensor is needed.
Disclosure of Invention
According to an aspect of the present disclosure, there is provided a method of compensating mechanical data, including:
acquiring current mechanical data of a force sensor and reference information of the mechanical data under the current gesture of the tail end of the mechanical arm;
inputting the current gesture of the tail end of the mechanical arm and the reference information of the mechanical data into a mechanical data prediction model to obtain mechanical prediction data of the force sensor in a state that a front shaft of the mechanical arm is kept stationary;
and compensating the current mechanical data of the force sensor by using the mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm is kept stationary.
According to another aspect of the present disclosure, there is provided a compensation device for mechanical data, including:
The prediction module is used for acquiring current mechanical data and reference information of the mechanical data of the force sensor under the current gesture of the tail end of the mechanical arm, inputting the current gesture of the tail end of the mechanical arm and the reference information of the mechanical data into the mechanical data prediction model, and acquiring mechanical prediction data of the force sensor under the state that the front shaft is kept stationary;
and the compensation module is used for compensating the current mechanical data of the force sensor by utilizing the mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm is kept stationary.
According to another aspect of the present disclosure, there is provided an electronic device including:
a processor; the method comprises the steps of,
a memory storing a program;
wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the method according to an exemplary embodiment of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method according to an exemplary embodiment of the present disclosure.
According to one or more technical schemes provided by the exemplary embodiments of the present disclosure, current mechanical data and reference information of mechanical data of a force sensor can be obtained under the current posture of the tail end of the mechanical arm, and then the current posture of the tail end of the mechanical arm and the reference information of the mechanical data are input into a mechanical data prediction model, so that mechanical prediction data of the force sensor in a state that a front axle is kept stationary is obtained. Because the mechanical arm posture corresponding to the mechanical arm front-end shaft kept in the static state is not influenced by the mechanical arm front-end shaft, when the mechanical prediction data of the force sensor in the front-end shaft kept in the static state can be used as compensation parameters, the current mechanical data of the force sensor is compensated, the influence of the mechanical arm front-end shaft can be eliminated from the compensated current mechanical data, and therefore the compensation parameter precision of the force sensor is improved, and the control accuracy of the mechanical arm is further improved.
In addition, the mechanical data prediction model can obtain mechanical prediction data in a nonlinear fitting mode, so that nonlinear relation exists between the obtained mechanical prediction data and the current mechanical data, and therefore nonlinear compensation can be carried out on the current mechanical data by utilizing the mechanical prediction data of the force sensor in a state that the front shaft is kept stationary, and the compensation accuracy is further improved.
In addition, the reference information of the mechanical data in the exemplary embodiment of the disclosure can reflect the related data under the current gesture of the tail end of the mechanical arm, and the reference information of the mechanical data can compensate the hysteresis of mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm keeps static, so that the prediction accuracy of the mechanical prediction data is improved.
Drawings
Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments, with reference to the following drawings, wherein:
FIG. 1 illustrates a flow diagram of a method of compensating mechanical data in accordance with an exemplary embodiment of the present disclosure;
FIG. 2 illustrates a determined flow diagram of a coordinate system mapping relationship according to an exemplary embodiment of the present disclosure;
FIG. 3 illustrates a six-dimensional force sensor effectiveness detection flow diagram of an exemplary embodiment of the present disclosure;
FIG. 4 shows a functional block diagram of a compensation device for mechanical data according to an exemplary embodiment of the present disclosure;
FIG. 5 shows a schematic block diagram of a chip according to an exemplary embodiment of the present disclosure;
fig. 6 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below. It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
In the use process of the mechanical arm, as the task scene that the tool at the tail end of the mechanical arm is in contact with the environment is involved in assembly, polishing and the like, a force sensor (mostly using a six-dimensional force sensor) is often required to be installed at the tail end of the mechanical arm to acquire the information of the environment contact force/moment, so that the tail end force control of the mechanical arm is realized, and the stability and the accuracy of the tasks such as assembly, polishing and the like are ensured.
Taking a six-dimensional force sensor as an example, the six-dimensional force sensor is generally arranged at the rear side of the tail end of the mechanical arm and the front side of the tail end tool, so that the tail end contact force/moment data of the mechanical arm are accurately measured, and meanwhile, the tail end tool is prevented from being influenced to complete a set task. A six-dimensional force sensor is a sensor capable of measuring forces in three directions and moments in three directions of an object at the same time, and is therefore called a six-dimensional force sensor. Physical quantities that it can measure include: force applied by the six-dimensional force sensor coordinate system in the X-axis, Y-axis and Z-axis directions and moment around the X-axis, Y-axis and Z-axis.
The six-dimensional force sensor is arranged at the tail end of the mechanical arm and mainly used for measuring the three-dimensional force, the three-dimensional moment and other mechanical data under any coordinate system in the three-dimensional space. The mechanical data are used for force feedback control of the robot in the assembly process, so that the robot is helped to sense and respond to external contact force more accurately, and therefore, by additionally arranging a six-dimensional force sensor at the tail end of the mechanical arm, advanced force control methods such as mechanical arm impedance/admittance control, force position hybrid control, dragging teaching and the like can be realized, and the operation precision and flexibility of the robot are improved. However, the installation of the end-of-arm tool affects the accuracy of the measurement data collected by the six-dimensional force sensor, and therefore, compensation of the measurement data collected by the six-dimensional force sensor is highly desirable.
In the process of acquiring compensation data of the six-dimensional force sensor, six axes of the mechanical arm in the related method are all in rotation, and the front axis of the mechanical arm has an excessive influence on the posture of the mechanical arm, so that the mechanical arm is possibly far away from a normal working space, and the compensation parameter precision is influenced. Moreover, the compensation is obtained by adopting a linear fitting method, but due to the phenomenon of multi-axis coupling in the measurement data of the six-dimensional force sensor, the original data and the compensation value are in a nonlinear relation, and the accuracy of the compensation is possibly reduced due to the linear fitting.
In view of the above problems, an exemplary embodiment of the present disclosure provides a method for compensating mechanical data, which may use a current posture of a distal end of a mechanical arm and reference information of mechanical data as input, predict mechanical prediction data of a force sensor in a state where a front axis of the mechanical arm is kept stationary by using a mechanical data prediction model, so as to compensate the current mechanical data of the force sensor by using the mechanical prediction data of the force sensor in the state where the front axis of the mechanical arm is kept stationary, thereby improving the mechanical data compensation precision of the force sensor. The method may be performed by the electronic device or a chip in the electronic device. The electronic device may be an upper computer of the mechanical arm.
Fig. 1 shows a flow diagram of a method of compensating mechanical data according to an exemplary embodiment of the present disclosure. As shown in fig. 1, the method for compensating mechanical data provided by the exemplary embodiment of the present disclosure may include:
step 101: and acquiring current mechanical data of the force sensor and reference information of the mechanical data under the current gesture of the tail end of the mechanical arm.
In practical applications, the reference information of the mechanical data of the exemplary embodiment of the present disclosure may include: the environmental temperature at the current posture of the mechanical arm end may affect the mechanical data measured by the mechanical sensor, and therefore, the environmental temperature at the current posture of the mechanical arm end is obtained as the reference information of the mechanical data.
The reference information of the mechanical data according to the exemplary embodiment of the present disclosure may also include reference information of the mechanical data including: and the rotation parameters of the mechanical arm joint under the current gesture of the tail end of the mechanical arm. It should be understood that the robot arm joint rotation parameters may refer to rotation parameters of all joints of the robot arm.
The robot arm joint rotation parameter may include a robot arm joint rotation speed, or may include a robot arm joint rotation acceleration, for example. Here, the rotation parameters of the mechanical arm joint are as follows: the rotation speed of the mechanical arm joint and the rotation acceleration of the mechanical arm joint have certain hysteresis influence on acting force applied to the tail end of the mechanical arm, so that the mechanical data measured by the mechanical sensor have deviation, and therefore, the rotation parameters of the mechanical arm joint can also be used as reference information of the mechanical data, so that the accuracy of mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm is kept stationary is improved subsequently.
Step 102: and inputting the current gesture of the tail end of the mechanical arm and the reference information of the mechanical data into a mechanical data prediction model to obtain mechanical prediction data of the force sensor under the condition that the front shaft of the mechanical arm is kept stationary.
In practical application, because the installation position of the force sensor determines the relation between the coordinate system of the force sensor and the coordinate system of the tail end of the mechanical arm, the compensation precision of the current mechanical data is influenced by the installation precision of the force sensor, if the relation between the coordinate system of the force sensor and the coordinate system of the tail end of the mechanical arm is uncertain, various error couplings can possibly be possibly caused in the error analysis process, specific error sources are difficult to analyze, and the compensation precision of the current mechanical data can be influenced.
For example, the mechanical prediction data of the force sensor in the exemplary embodiment of the present disclosure in the state that the front axle of the mechanical arm is kept stationary may refer to the mechanical prediction data in the case that the front axle of the mechanical arm is kept stationary, but the rear three axles may move. Therefore, the posture of the mechanical arm corresponding to the state that the front axle of the mechanical arm keeps static is not influenced by the front axle of the mechanical arm, so that the mechanical prediction data of the force sensor in the state that the front axle keeps static can be used as compensation parameters to compensate the current mechanical data.
The reference information of the mechanical data in the exemplary embodiment of the disclosure can reflect the related data under the current gesture of the tail end of the mechanical arm, and the reference information of the mechanical data can compensate the hysteresis of mechanical prediction data of the force sensor under the condition that the front axle of the mechanical arm keeps static, so that the prediction accuracy of the mechanical prediction data is improved.
Step 103: and compensating the current mechanical data of the force sensor by using the mechanical prediction data of the force sensor in the state that the front shaft of the mechanical arm is kept stationary.
According to the embodiment of the disclosure, the current mechanical data of the force sensor is compensated by using the mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm is kept stationary, and the mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm is kept stationary can be subtracted on the basis of the current mechanical data of the force sensor, so that the compensated current mechanical data is obtained.
Because the mechanical data prediction model of the exemplary embodiment of the disclosure can obtain mechanical prediction data in a nonlinear fitting manner, so that a nonlinear relation exists between the obtained mechanical prediction data and current mechanical data, the mechanical prediction data of the force sensor in a state that a front shaft is kept stationary can be utilized to carry out nonlinear compensation on the current mechanical data, so that the compensated current mechanical data can be matched with a multiaxial coupling phenomenon of the mechanical sensor, and further the compensation precision is further improved.
As one possible implementation, the training set of the mechanical data prediction model in the training phase of the exemplary embodiment of the present disclosure may include a posture sample in which the end of the mechanical arm is kept stationary at the front axis, a mechanical data sample in which the force sensor is kept stationary at the front axis, and a reference sample of mechanical data.
When training data included in the training set is collected, the rear three axes (the rear three axes are not mutually parallel) of the mechanical shaft can be controlled to rotate under the condition that the mechanical arm keeps static at the front shaft, and a large amount of gesture data of the tail end of the mechanical arm in the rotating process of the rear three axes of the mechanical arm can be collected to be used as a gesture sample of the tail end of the mechanical arm in the static state at the front shaft.
In order to reduce the influence of the installation precision of the force sensor on the training acquisition, the attitude sample of the tail end of the mechanical arm in the static state of the front shaft and the mechanical data sample of the force sensor in the static state of the front shaft can be in the same coordinate system.
For example: the mechanical data sample of the force sensor in the state that the front axle keeps static can be converted into the mechanical arm end coordinate system by utilizing the mapping relation between the mechanical arm end coordinate system and the force sensor coordinate system, so that the mechanical data sample of the force sensor in the state that the front axle keeps static and the posture sample of the mechanical arm end in the state that the front axle keeps static are positioned in the same coordinate system. Also for example: the mapping relation between the coordinate system of the tail end of the mechanical arm and the coordinate system of the force sensor can be utilized to convert the attitude sample of the tail end of the mechanical arm in the static state of the front axle into the coordinate system of the force sensor, so that the mechanical data sample of the force sensor in the static state of the front axle and the attitude sample of the tail end of the mechanical arm in the static state of the front axle are positioned in the same coordinate system.
Because the mechanical arm is not subjected to external force applied by the front axle of the mechanical arm in the three-axle rotation process after the mechanical arm is kept static by the front axle, the mechanical data of the force sensor collected at the moment is used as a mechanical data sample of the force sensor in the static state of the front axle and is used as supervision information, the mechanical prediction data predicted by the trained mechanical data prediction model can be ensured, the influence of the external force generated by the front axle on the mechanical prediction data can be eliminated, and the compensation precision of the mechanical data is improved.
For example, the reference samples of the mechanical data according to the exemplary embodiments of the present disclosure may include an ambient temperature sample in which the distal end of the mechanical arm is kept stationary on the front axle, and may further include a mechanical arm joint rotation parameter sample in which the distal end of the mechanical arm is kept stationary on the front axle, where the mechanical arm joint rotation parameter sample includes a mechanical arm joint rotation speed sample and/or a mechanical arm joint rotation acceleration sample.
The reference samples of the mechanical data can assist in correcting the hysteresis of the mechanical data sample of the force sensor in the state that the front shaft is kept stationary, so that the mechanical prediction data predicted by the mechanical data prediction model can accurately reflect the current posture of the tail end of the mechanical arm, the mechanical prediction data in the state that the front shaft of the mechanical arm is kept stationary accords with the actual state, the possibility that the mechanical prediction data is delayed is reduced, and the compensation precision of the mechanical data is further improved.
When the mechanical data prediction model is trained, the mechanical data prediction model to be trained can be initialized, a posture sample of the tail end of the mechanical arm in a state that the front shaft is kept stationary, a mechanical arm joint rotation parameter sample of the tail end of the mechanical arm in a state that the front shaft is kept stationary and a reference sample of mechanical data comprise an environmental temperature sample of the tail end of the mechanical arm in a state that the front shaft is kept stationary are input into the mechanical data prediction model to be trained, and mechanical prediction data of the force sensor in a state that the front shaft of the mechanical arm is kept stationary are obtained; and determining a loss value based on the mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm is kept stationary and the mechanical data sample of the force sensor in the state that the front axle is kept stationary. If the loss value is smaller than the preset loss, the training of the mechanical data prediction model to be trained is completed, otherwise, the model parameters of the mechanical data prediction model to be trained are updated by adopting a back propagation algorithm.
The mechanical data prediction model after training can reflect the mapping relation between the tail end gesture of the mechanical arm and the mechanical prediction data of the force sensor. Taking a six-dimensional force sensor as an example, the mechanical prediction data predicted by the mechanical data prediction model can comprise stress of the tail end of the mechanical arm on an X axis, a Y axis and a Z axis of a coordinate system of the six-dimensional force sensor, and can also comprise moment of the tail end of the mechanical arm around the X axis, the Y axis and the Z axis of the coordinate system of the six-dimensional force sensor.
After the mechanical data prediction model training is completed, the gesture sample of the tail end of the mechanical arm in the state that the front shaft of the mechanical arm is kept stationary can be input into the mechanical data prediction model after the training is completed, mechanical prediction data of the six-dimensional force sensor in the state that the front shaft of the mechanical arm is kept stationary is obtained, then mean square error between the mechanical prediction data of the six-dimensional force sensor in the state that the front shaft of the mechanical arm is kept stationary and the mechanical measurement data of the six-dimensional force sensor in the state that the front shaft of the mechanical arm is kept stationary is calculated, if the mean square error is smaller than a preset error, the mechanical measurement data of the six-dimensional force sensor in the state that the front shaft of the mechanical arm is kept stationary is effective, otherwise, the mechanical measurement data of the six-dimensional force sensor in the state that the front shaft of the mechanical arm is kept stationary is ineffective.
In practical applications, the mechanical data prediction model of the exemplary embodiment of the present disclosure may employ a secondary neural network, which may include an input layer, an output layer, and an intermediate layer, where the input layer is fully connected to the intermediate layer, the intermediate layer is fully connected to the output layer, and the layers are not connected, and there is no connection between the cross layers. At this time, the middle layer and the output layer are both calculation layers, and finally the output mechanical prediction data is calculated by using the nodes and the weight matrix of the middle layer. As for the activation function, a Sigmoid function can be adopted to add nonlinear factors, so that the problem which cannot be solved by the linear model is solved.
As one possible implementation, fig. 2 shows a determined flow diagram of a coordinate system mapping relationship of an exemplary embodiment of the present disclosure. As shown in fig. 2, the method of the exemplary embodiment of the present disclosure may further include:
step 201: and when the included angle between the axial direction of the mechanical arm end shaft and the horizontal plane is equal to 0 degree, acquiring the maximum mechanical data of the force sensor coordinate system in the first direction and the end gesture of the mechanical arm corresponding to the maximum mechanical data in the rotating process of the mechanical arm end shaft.
When the included angle between the axial direction of the mechanical arm end shaft and the horizontal plane is equal to 0 DEG, the horizontal plane can be regarded as the plane where the ground is located, and at the moment, the axial direction of the mechanical arm end shaft is flush with the horizontal plane, so that the mechanical data of the force sensor coordinate system in the first direction can be continuously collected through the force sensor in the process of rotating the mechanical arm end shaft.
The mechanical arm terminal shaft can be rotated for at least one circle, and then the mechanical data of the mechanical sensor coordinate system in the first direction in the process of rotating the mechanical arm terminal shaft is detected, so that the maximum mechanical data of the force sensor coordinate system in the first direction is obtained, and the terminal gesture of the mechanical arm at the moment can be the terminal gesture of the mechanical arm corresponding to the maximum mechanical data.
Step 202: and determining the mapping relation between the mechanical arm terminal coordinate system and the force sensor coordinate system based on the included angle between the axial direction of the mechanical arm terminal shaft and the horizontal plane, the maximum mechanical data of the force sensor coordinate system in the first direction and the terminal gesture of the mechanical arm corresponding to the maximum mechanical data.
In practical application, when the mechanical data of the force sensor coordinate system in the first direction is the largest, it is indicated that the gravity direction of the tail end of the mechanical arm coincides with the first direction of the force sensor coordinate system, and at this time, the first direction parameter of the force sensor coordinate system can be determined. Based on the above, the first direction parameter of the mechanical arm end coordinate system can be determined by using the end gesture of the mechanical arm corresponding to the maximum mechanical data, and then the mapping relationship between the mechanical arm end coordinate system and the force sensor coordinate system in the first direction is determined by using the first direction parameter of the force sensor coordinate system and the first direction parameter of the mechanical arm end coordinate system.
Considering that the third direction of the force sensor coordinate system coincides with the third direction of the arm end coordinate system when the angle between the axial direction of the arm end shaft and the horizontal plane is equal to 0 °, the mapping relationship between the arm end coordinate system and the force sensor coordinate system in the third direction can be determined based on the angle between the axial direction of the arm end shaft and the horizontal plane.
Meanwhile, three coordinate axes of the same coordinate axis are considered to be perpendicular to each other, so that the mapping relation between the mechanical arm terminal coordinate system and the force sensor coordinate system in the second direction can be determined based on the mapping relation between the mechanical arm terminal coordinate system and the force sensor coordinate system in the first direction and the mapping relation between the mechanical arm terminal coordinate system and the force sensor coordinate system in the third direction.
Taking a six-dimensional force sensor as an example, the determined mapping relationship between the mechanical arm end coordinate system and the force sensor coordinate system in the first direction comprises a mapping relationship between an X axis of the mechanical arm end coordinate system and an X axis of the force sensor coordinate system, the determined mapping relationship between the mechanical arm end coordinate system and the force sensor coordinate system in the second direction comprises a mapping relationship between a Y axis of the mechanical arm end coordinate system and a Y axis of the force sensor coordinate system, and the determined mapping relationship between the mechanical arm end coordinate system and the force sensor coordinate system in the third direction comprises a mapping relationship between a Z axis of the mechanical arm end coordinate system and a Z axis of the force sensor coordinate system.
As can be seen, according to the exemplary embodiments of the present disclosure, when the included angle between the axial direction of the end shaft of the mechanical arm and the horizontal plane is equal to 0 °, the maximum mechanical data of the force sensor coordinate system in the first direction during the rotation process of the end shaft of the mechanical arm is obtained, so that the first direction of the force sensor coordinate system can be accurately defined through the gravity direction, and the error of determining the mapping relation is reduced.
In the related art, since the force sensor cannot be frequently assembled and disassembled in the actual use process, the force sensor cannot be singly subjected to validity detection before use, and mechanical data acquired by the force sensor without validity detection can cause potential safety hazards of equipment. To ensure that the acquired mechanical data is valid, exemplary embodiments of the present disclosure may perform a validity check on the six-dimensional force sensor to determine if its acquired data is valid to ensure that the force sensor's zero drift is less than or equal to the maximum allowable zero drift.
Fig. 3 shows a six-dimensional force sensor effectiveness detection flow diagram of an exemplary embodiment of the present disclosure. As shown in fig. 3, the method of the exemplary embodiment of the present disclosure may further include:
step 301: and when the included angle between the axial direction of the tail end shaft of the mechanical arm and the horizontal plane is larger than 0 degrees and smaller than 90 degrees, acquiring zero drift of the force sensor.
When the included angle between the axial direction of the tail end shaft of the mechanical arm and the horizontal plane is larger than 0 degrees and smaller than 90 degrees, a plurality of pairs of mechanical measurement data of the force sensor in the rotation process of the tail end joint of the mechanical arm can be obtained, and two sampling positions corresponding to each pair of mechanical measurement data are ensured to be symmetrical. The mechanical arm terminal joint can be controlled to rotate for one circle, the rotating track is round, and then mechanical test data are acquired at equal intervals. When the two sampling locations are symmetrical, the two sampling locations are symmetrical about the center of the circle.
When multiple pairs of mechanical test data are acquired, the zero drift of the force sensor can be obtained through a symmetry method, namely, the zero drift of the force sensor is determined based on the multiple pairs of mechanical measurement data, for example: each pair of mechanical test data can be subjected to difference so as to obtain zero drift of the force sensor, and therefore the determination error of the zero drift is reduced.
And when the included angle between the axial direction of the tail end shaft of the mechanical arm and the horizontal plane is larger than 0 degrees and smaller than 90 degrees, the mechanical measurement data acquired by the force sensor can have components on different axes of the coordinate axes of the force sensor, so that the zero drift of the force sensor can be ensured to be determined by mechanical test data of different axes of the coordinate system of the force sensor.
Taking a six-dimensional force sensor as an example, the X-axis zero drift of the six-dimensional force sensor can be determined firstly based on the X-axis zero drift and the zero drift around the X-axis of the six-dimensional force sensor coordinate system corresponding to each pair of mechanical test data, then based on the X-axis zero drift average value of the six-dimensional force sensor coordinate system corresponding to a plurality of pairs of mechanical test data, the X-axis zero drift of the six-dimensional force sensor can be determined, and based on the zero drift average value around the X-axis of the six-dimensional force sensor coordinate system corresponding to a plurality of pairs of mechanical test data. Similarly, the method can also be used for obtaining the Y-axis zero drift and the zero drift around the X-axis of the six-dimensional force sensor, the Z-axis zero drift and the zero drift around the Z-axis of the six-dimensional force sensor,
step 302: and if the zero drift of the force sensor is smaller than or equal to the maximum allowable zero drift, determining that the current mechanical data of the force sensor is reliable.
Taking a six-dimensional force sensor as an example, an X-axis zero drift threshold, a Y-axis zero drift threshold, a Z-axis zero drift threshold and a Z-axis zero drift threshold can be set, then whether the X-axis zero drift of a coordinate system of the six-dimensional force sensor is smaller than or equal to the X-axis zero drift threshold is judged, if so, the X-axis stress data of the coordinate system of the six-dimensional force sensor is effective, otherwise, the X-axis stress data of the coordinate system of the six-dimensional force sensor is ineffective. Similarly, whether the X-axis stress data is effective or not can be referred to, whether the Y-axis stress data and the Z-axis stress data are effective or not can be judged, and whether the X-axis moment data are effective or not, whether the Y-axis moment data are effective or not and whether the Z-axis moment data are effective or not can be referred to.
Therefore, through the validity detection, the reliability of the collected current mechanical data can be ensured, and the current mechanical data can be ensured to be within the allowed threshold range.
According to one or more technical schemes provided by the exemplary embodiments of the present disclosure, current mechanical data and reference information of mechanical data of a force sensor can be obtained under the current posture of the tail end of the mechanical arm, and then the current posture of the tail end of the mechanical arm and the reference information of the mechanical data are input into a mechanical data prediction model, so that mechanical prediction data of the force sensor in a state that a front axle is kept stationary is obtained. Because the mechanical arm posture corresponding to the mechanical arm front-end shaft kept in the static state is not influenced by the mechanical arm front-end shaft, when the mechanical prediction data of the force sensor in the front-end shaft kept in the static state can be used as compensation parameters, the current mechanical data of the force sensor is compensated, the influence of the mechanical arm front-end shaft can be eliminated from the compensated current mechanical data, and therefore the compensation parameter precision of the force sensor is improved, and the control accuracy of the mechanical arm is further improved.
In addition, the mechanical data prediction model can obtain mechanical prediction data in a nonlinear fitting mode, so that nonlinear relation exists between the obtained mechanical prediction data and the current mechanical data, and therefore nonlinear compensation can be carried out on the current mechanical data by utilizing the mechanical prediction data of the force sensor in a state that the front shaft is kept stationary, and the compensation accuracy is further improved.
In addition, the reference information of the mechanical data in the exemplary embodiment of the disclosure can reflect the related data under the current gesture of the tail end of the mechanical arm, and the reference information of the mechanical data can compensate the hysteresis of mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm keeps static, so that the prediction accuracy of the mechanical prediction data is improved.
In the case of dividing each functional module by adopting a corresponding function, exemplary embodiments of the present disclosure provide a compensation device for mechanical data, which may be an electronic device or a chip applied to the electronic device. Fig. 4 shows a functional block diagram of a compensation device for mechanical data according to an exemplary embodiment of the present disclosure. As shown in fig. 4, the mechanical data compensation device 400 includes:
the prediction module 401 is configured to obtain current mechanical data and reference information of mechanical data of the force sensor under a current gesture of a distal end of the mechanical arm, input the current gesture of the distal end of the mechanical arm and the reference information of the mechanical data into a mechanical data prediction model, and obtain mechanical prediction data of the force sensor under a state that a front axle is kept stationary;
and the compensation module 402 is used for compensating the current mechanical data of the force sensor by using the mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm is kept stationary.
In one possible implementation, the reference information of the mechanical data includes: the environmental temperature of the tail end of the mechanical arm under the current gesture; and/or the number of the groups of groups,
the reference information of the mechanical data comprises: and the mechanical arm joint rotation parameters comprise mechanical arm joint rotation speed and/or mechanical arm joint rotation acceleration under the current gesture of the tail end of the mechanical arm.
In a possible implementation manner, the apparatus further includes an obtaining module 403, configured to convert the current pose of the end of the mechanical arm and the current mechanical data of the force sensor into the same coordinate system by using a mapping relationship between the coordinate system of the end of the mechanical arm and the coordinate system of the force sensor.
In one possible implementation manner, the training set of the mechanical data prediction model in the training stage includes a posture sample of the tail end of the mechanical arm in a static state of the front axle, a mechanical data sample of the force sensor in a static state of the front axle and a reference sample of mechanical data.
In one possible implementation, the reference sample of mechanical data includes an ambient temperature sample of the mechanical arm tip held stationary at a front axis; and/or the number of the groups of groups,
The reference samples of the mechanical data comprise mechanical arm joint rotation parameter samples of the tail end of the mechanical arm in a state that a front shaft is kept stationary, and the mechanical arm joint rotation parameter samples comprise mechanical arm joint rotation speed samples and/or mechanical arm joint rotation acceleration samples.
In one possible implementation, the attitude sample of the tail end of the mechanical arm in the stationary state of the front axle and the mechanical data sample of the force sensor in the stationary state of the front axle are in the same coordinate system.
In a possible implementation manner, the obtaining module 403 is further configured to obtain, when an included angle between an axial direction of a mechanical arm end shaft and a horizontal plane is equal to 0 °, maximum mechanical data of a force sensor coordinate system in a first direction and an end pose of the mechanical arm corresponding to the maximum mechanical data during rotation of the mechanical arm end shaft; and determining a mapping relation between the mechanical arm terminal coordinate system and the force sensor coordinate system based on an included angle between the axial direction of the mechanical arm terminal shaft and the horizontal plane, the maximum mechanical data of the force sensor coordinate system in the first direction and the terminal gesture of the mechanical arm corresponding to the maximum mechanical data.
In a possible implementation manner, the obtaining module 403 is configured to determine, based on maximum mechanical data of the force sensor coordinate system in the first direction and a terminal pose of the mechanical arm corresponding to the maximum mechanical data, a mapping relationship between the mechanical arm terminal coordinate system and the force sensor coordinate system in the first direction; determining a mechanical data mapping relation between the mechanical arm terminal coordinate system and the force sensor coordinate system in a third direction based on an included angle between the axial direction of the mechanical arm terminal shaft and the horizontal plane; and determining the mapping relation of the mechanical arm terminal coordinate system and the force sensor coordinate system in the second direction based on the mapping relation of the mechanical arm terminal coordinate system and the force sensor coordinate system in the first direction and the mapping relation of the mechanical arm terminal coordinate system and the force sensor coordinate system in the third direction.
In a possible implementation manner, the zero drift of the force sensor is smaller than or equal to the maximum allowed zero drift, and the device further includes an obtaining module 403, configured to obtain the zero drift of the force sensor when the included angle between the axial direction of the end shaft of the mechanical arm and the horizontal plane is greater than 0 ° and smaller than 90 °, and determine that the current mechanical data of the force sensor is reliable if the zero drift of the force sensor is smaller than or equal to the maximum allowed zero drift.
In a possible implementation manner, the obtaining module 403 is configured to obtain a plurality of pairs of mechanical measurement data of the force sensor during the rotation of the end joint of the force sensor when an included angle between an axial direction of the end shaft of the mechanical arm and a horizontal plane is greater than 0 ° and less than 90 °, and determine zero drift of the force sensor based on the plurality of pairs of mechanical measurement data, where two sampling positions corresponding to each pair of mechanical measurement data are symmetrical.
Fig. 5 shows a schematic block diagram of a chip according to an exemplary embodiment of the present disclosure. As shown in fig. 5, the chip 500 includes one or more (including two) processors 501 and a communication interface 502. The communication interface 502 may support a server to perform the data transceiving steps in the image processing method described above, and the processor 501 may support the server to perform the data processing steps in the image processing method described above.
Optionally, as shown in fig. 5, the chip 500 further includes a memory 503, where the memory 503 may include a read-only memory and a random access memory, and provides operating instructions and data to the processor. A portion of the memory may also include non-volatile random access memory (non-volatile random access memory, NVRAM).
In some embodiments, as shown in fig. 5, the processor 501 performs the corresponding operation by invoking a memory-stored operating instruction (which may be stored in an operating system). The processor 501 controls the processing operations of any one of the terminal devices, and may also be referred to as a central processing unit (central processing unit, CPU). Memory 503 may include read only memory and random access memory and provides instructions and data to processor 501. A portion of the memory 503 may also include NVRAM. Such as a memory, a communication interface, and a memory coupled together by a bus system that may include a power bus, a control bus, a status signal bus, etc., in addition to a data bus. But for clarity of illustration, the various buses are labeled as bus system 504 in fig. 5.
The method disclosed by the embodiment of the disclosure can be applied to a processor or implemented by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general purpose processor, a digital signal processor (digital signal processing, DSP), an ASIC, an off-the-shelf programmable gate array (field-programmable gate array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps and logic blocks of the disclosure in the embodiments of the disclosure may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present disclosure may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The exemplary embodiments of the present disclosure also provide an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor for causing the electronic device to perform a method according to embodiments of the present disclosure when executed by the at least one processor.
The present disclosure also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is for causing the computer to perform a method according to an embodiment of the present disclosure.
The present disclosure also provides a computer program product comprising a computer program, wherein the computer program, when executed by a processor of a computer, is for causing the computer to perform a method according to embodiments of the disclosure.
Referring to fig. 6, a block diagram of an electronic device 600 that may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the electronic device 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 may be any type of device capable of inputting information to the electronic device 600, and the input unit 606 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. The output unit 607 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 608 may include, but is not limited to, magnetic disks, optical disks. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices through a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.
As shown in fig. 6, the computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above. For example, in some embodiments, the methods of the exemplary embodiments of the present disclosure may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 600 via the ROM 602 and/or the communication unit 609. In some embodiments, the computing unit 601 may be configured to perform the methods of the exemplary embodiments of the present disclosure by any other suitable means (e.g., by means of firmware).
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described by the embodiments of the present disclosure are performed in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, a terminal, a user equipment, or other programmable apparatus. The computer program or instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program or instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available media. The usable medium may be a magnetic medium, e.g., floppy disk, hard disk, tape; optical media, such as digital video discs (digital video disc, DVD); but also semiconductor media such as solid state disks (solid state drive, SSD).
Although the present disclosure has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations thereof can be made without departing from the spirit and scope of the disclosure. Accordingly, the specification and drawings are merely exemplary illustrations of the present disclosure as defined in the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the disclosure. It will be apparent to those skilled in the art that various modifications and variations can be made to the present disclosure without departing from the spirit or scope of the disclosure. Thus, the present disclosure is intended to include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (13)
1. A method of compensating mechanical data, comprising:
acquiring current mechanical data of a force sensor and reference information of the mechanical data under the current gesture of the tail end of the mechanical arm;
inputting the current gesture of the tail end of the mechanical arm and the reference information of the mechanical data into a mechanical data prediction model to obtain mechanical prediction data of the force sensor in a state that a front shaft of the mechanical arm is kept stationary;
And compensating the current mechanical data of the force sensor by using the mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm is kept stationary.
2. The method of claim 1, wherein the reference information of the mechanical data comprises: the environmental temperature of the tail end of the mechanical arm under the current gesture; and/or the number of the groups of groups,
the reference information of the mechanical data comprises: and the mechanical arm joint rotation parameters comprise mechanical arm joint rotation speed and/or mechanical arm joint rotation acceleration under the current gesture of the tail end of the mechanical arm.
3. The method according to claim 1, wherein the method further comprises:
and converting the current gesture of the tail end of the mechanical arm and the current mechanical data of the force sensor into the same coordinate system by utilizing the mapping relation between the coordinate system of the tail end of the mechanical arm and the coordinate system of the force sensor.
4. A method according to claim 3, characterized in that the method further comprises:
when the included angle between the axial direction of the mechanical arm end shaft and the horizontal plane is equal to 0 DEG, acquiring the maximum mechanical data of a force sensor coordinate system in the first direction and the end gesture of the mechanical arm corresponding to the maximum mechanical data in the rotating process of the mechanical arm end shaft;
And determining a mapping relation between the mechanical arm terminal coordinate system and the force sensor coordinate system based on an included angle between the axial direction of the mechanical arm terminal shaft and the horizontal plane, the maximum mechanical data of the force sensor coordinate system in the first direction and the terminal gesture of the mechanical arm corresponding to the maximum mechanical data.
5. The method of claim 4, wherein determining the mapping between the robot arm end coordinate system and the force sensor coordinate system based on the angle between the axial direction of the robot arm end shaft and the horizontal plane, the maximum mechanical data of the force sensor coordinate system in the first direction, and the end pose of the robot arm corresponding to the maximum mechanical data, comprises:
determining a mapping relation between a mechanical arm terminal coordinate system and a force sensor coordinate system in a first direction based on the maximum mechanical data of the force sensor coordinate system in the first direction and the terminal gesture of the mechanical arm corresponding to the maximum mechanical data;
determining a mechanical data mapping relation between the mechanical arm terminal coordinate system and the force sensor coordinate system in a third direction based on an included angle between the axial direction of the mechanical arm terminal shaft and the horizontal plane;
And determining the mapping relation of the mechanical arm terminal coordinate system and the force sensor coordinate system in the second direction based on the mapping relation of the mechanical arm terminal coordinate system and the force sensor coordinate system in the first direction and the mapping relation of the mechanical arm terminal coordinate system and the force sensor coordinate system in the third direction.
6. The method of claim 1, wherein the training set of the mechanical data prediction model during the training phase comprises a pose sample of the mechanical arm tip in a stationary state of the front axle, a mechanical data sample of the force sensor in a stationary state of the front axle, and a reference sample of the mechanical data.
7. The method of claim 6, wherein the reference sample of mechanical data comprises an ambient temperature sample of the robot arm tip held stationary at a front axis; and/or the number of the groups of groups,
the reference samples of the mechanical data comprise mechanical arm joint rotation parameter samples of the tail end of the mechanical arm in a state that a front shaft is kept stationary, and the mechanical arm joint rotation parameter samples comprise mechanical arm joint rotation speed samples and/or mechanical arm joint rotation acceleration samples.
8. The method of claim 6, wherein the attitude sample of the robot arm tip in the stationary state of the front axis and the mechanical data sample of the force sensor in the stationary state of the front axis are in the same coordinate system.
9. The method of any of claims 1-8, wherein the force sensor has a zero drift less than or equal to a maximum allowable zero drift, the method further comprising:
acquiring zero drift of a force sensor when the included angle between the axial direction of the tail end shaft of the mechanical arm and the horizontal plane is more than 0 degrees and less than 90 degrees;
and if the zero drift of the force sensor is smaller than or equal to the maximum allowable zero drift, determining that the current mechanical data of the force sensor is reliable.
10. The method of claim 9, wherein the acquiring the zero drift of the force sensor when the angle between the axial direction of the end shaft of the mechanical arm and the horizontal plane is greater than 0 ° and less than 90 ° comprises:
when the included angle between the axial direction of the tail end shaft of the mechanical arm and the horizontal plane is more than 0 degrees and less than 90 degrees, acquiring a plurality of pairs of mechanical measurement data of the force sensor in the process of rotating the tail end joint of the force sensor;
and determining zero drift of the force sensor based on a plurality of pairs of mechanical measurement data, wherein two sampling positions corresponding to each pair of mechanical measurement data are symmetrical.
11. A device for compensating mechanical data, comprising:
The prediction module is used for acquiring current mechanical data and reference information of the mechanical data of the force sensor under the current gesture of the tail end of the mechanical arm, inputting the current gesture of the tail end of the mechanical arm and the reference information of the mechanical data into the mechanical data prediction model, and acquiring mechanical prediction data of the force sensor under the state that the front shaft is kept stationary;
and the compensation module is used for compensating the current mechanical data of the force sensor by utilizing the mechanical prediction data of the force sensor in the state that the front axle of the mechanical arm is kept stationary.
12. An electronic device, comprising:
a processor; the method comprises the steps of,
a memory storing a program;
wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 10.
13. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-10.
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