CN109740270B - Large length-diameter ratio shaft hole assembling system and method based on contact force and moment prediction and analysis - Google Patents
Large length-diameter ratio shaft hole assembling system and method based on contact force and moment prediction and analysis Download PDFInfo
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Abstract
The invention discloses a large length-diameter ratio shaft hole assembly system and method based on contact force and moment prediction and analysis1Triaxial contact force data I2Triaxial moment data I3Actual contact force/moment data I for predicting assembly process5(ii) a Then the stress analysis mechanism analyzes the three-axis pose parameter I1Actual contact force/moment data I5And analyzing the stress condition of the large length-diameter ratio inserting shaft and the assembling hole in the assembling process to obtain an adjusting and analyzing result of the assembling posture. Has the advantages that: by establishing an accurate mathematical model and solving parameters through collected data, the data pairs under different conditions are collected to be used for training a neural network model and establishing a mapping relation of the neural network model, so that the force and the moment are accurately predicted, whether the assembly of the shaft has deviation or not is accurately analyzed through the stress condition of the shaft with large length-diameter ratio in the assembly process after prediction, and analysis support is provided for accurately controlling the assembly action.
Description
Technical Field
The invention relates to the technical field of assembly robot control, in particular to a large length-diameter ratio shaft hole assembly system and method based on contact force and moment prediction and analysis.
Background
In consideration of full-automatic assembly, whether the control of the robot in the assembly process is accurate directly influences the assembly result, the accuracy of parameters of the force and the moment of the robot is a necessary condition for accurate control, the actual force and the moment required by the control of the robot are difficult to accurately calculate due to the disturbance of load gravity, installation error and the like during assembly, the contact force and the moment need to be predicted, the prediction result can be used as an important reference for actual control, the higher the prediction precision is, and the better the assembly effect of the actual control is.
The problem of accurate perception of contact force can be solved by solving the problem of the mapping relation between the terminal pose of the robot and the contact force.
The stress analysis of the assembling process is the premise of accurate control, if the analysis is not right, the feedback of the control is not good, when the assembling angle has deviation, the assembling will be obstructed, the stress analysis at the moment can not feed back the deviation in time, the assembling control difficulty can be increased, and even the assembling can not be completed.
The analytical problem of the assembly process can be summarized as finding the stress condition of the contact point.
Disclosure of Invention
Aiming at the problems existing in the background, the invention provides a large length-diameter ratio shaft hole assembling system and method based on contact force and moment prediction and analysis.
In order to achieve the purpose, the invention adopts the following specific technical scheme:
the large length-diameter ratio shaft hole assembly system based on contact force and moment prediction and analysis comprises an assembly robot, wherein an assembly end of the assembly robot is provided with an attitude sensor and a force sensor, and the attitude sensor is used for detecting a three-axis pose parameter I of the assembly end1The force sensor is used for detecting triaxial contact force data I of the assembly end2Triaxial moment data I3；
The device also comprises a prediction mechanism and a stress analysis mechanism, wherein the prediction mechanism is used for predicting the three-axis pose parameter I1Triaxial contact force data I2Triaxial moment data I3Actual contact force/moment data I for predicting assembly process5The stress analysis mechanism is used for analyzing the stress according to the three-axis pose parameter I1Actual contact force/moment data I5Analyzing the stress condition of the large length-diameter ratio inserting shaft and the assembling hole in the assembling process to obtain an adjusting and analyzing result of the assembling posture;
the data input end group of the prediction mechanism is connected with the output end of the attitude sensor and the output end of the force sensor, the data output end group of the prediction mechanism is connected with the data input end group of the stress analysis mechanism, the data input end group of the stress analysis mechanism is also connected with the output end of the attitude sensor, and the data output end group of the stress analysis mechanism is connected with the data input end group of the assembly robot control mechanism.
The data of force and moment measured by a six-dimensional force sensor arranged at the assembling end of the robot are caused by three parts, namely the system error of the sensor, the gravity action of an assembling load and the external contact force of the assembling load, and if the external contact force of the assembling load, namely the force required by actual control, is obtained, the influence of the system error of the sensor and the gravity action of the assembling load needs to be eliminated.
As shown in the following equation:
it can be seen that the contact force (F) of the assembly processcx、Fcy、Fcz) Is a force sensor measurement (F)x、Fy、Fz) Minus the influence of the weight of the load (G)x、Gy、Gz) And errors (F) introduced by the sensor itselfx0、Fy0、Fz0) Similar problems exist with torque.
For the influence of gravity and error on the assembly end, the actual assembly end is almost consistent in the two processes of the unassembled state and the assembled state, so that the actual contact force/moment data can be obtained by analyzing the influenced condition of the unassembled assembly end and introducing the previously analyzed influenced condition into the assembled state, the interference data are effectively removed, and the control precision of assembly is greatly improved;
the unassembled state refers to a process in which the fitting end has already been attached with the fitting but the fitting has not yet come into contact with the fitting target, and the assembled state refers to a process in which the fitting starts to come into contact with the fitting target until the fitting is completed.
On the basis of obtaining accurate contact force and moment, in order to realize flexible assembly control, an analysis model of the assembly process needs to be established. According to the geometric information of the shaft hole, all contact states of the shaft in the assembling process, which can occur, are analyzed, namely: single-sided contact, single-point contact, and two-point contact. As illustrated in fig. 9 in detail: (a) single-side contact, (b) single-point contact, and (c) two-point contact. The special case that the unilateral contact is the single-point contact can be combined to perform mechanical stress analysis.
Through the design, the stress analysis mechanism is used for analyzing the three-axis pose parameter I1Obtaining the deviation angle between the shaft and the hole, wherein a certain deviation angle can cause the shaft to be greatly resisted when being inserted into the hole, and the resistance is obtained through the triaxial contact force data I2Triaxial moment data I3And calculating to obtain that once the resistance is too large, the insertion action is blocked, the assembly deviation of the shaft hole is caused, and the assembly cannot be completed smoothly, and at the moment, the action of the assembly robot needs to be adjusted to ensure that the shaft is inserted into the hole smoothly.
The forecasting mechanism comprises a static pose and force/moment relation mapping module, a dynamic actual contact force/moment data calculating module, a processing module and a data storage module, wherein the processing module is respectively connected with the static pose and force/moment relation mapping module, the dynamic actual contact force/moment data calculating module and the data storage module.
Through the design, the static pose and force/moment relation mapping module analyzes the stress condition of the assembly end in the unassembled state in advance to establish the mapping relation, and because the assembly part does not contact the assembly target at the moment, the triaxial contact force data I detected by the assembly end at the moment is obtained2aTriaxial moment data I3aThe three-axis pose parameter I is the force data completely influenced by gravity and self error1aContact force with three axes data I2aTriaxial moment data I3aThe relation is established, so that the influence of gravity and self error on the attitude of the assembling end can be known;
the influence can continuously exist in the assembly state, namely the influence on the attitude of the assembly end in the assembly process can be obtained through the static attitude and force/moment relation mapping module, and then the dynamic actual contact force/moment data calculation module is used for calculating the triaxial contact force data I2bTriaxial moment data I3bBy correspondingly deducting the influence, accurate actual contact force/moment data I can be obtained5Thereby providing data support for more accurately controlling the robot action.
Further, the static pose and force/moment relation mapping module is provided with a BP neural network model, the input layer of the BP neural network model is 3 input nodes, and the output layer of the BP neural network model is 6 output nodes.
The BP neural network can well establish the mapping relation between data, once the neural network training is completed, the output result of the output layer can be directly obtained through the input data of the input layer, and the three-axis pose parameter I1The number of the parameter values is 3, so that the input layer of the BP neural network only needs to be set to 3 input nodes, and the triaxial contact force data I2Triaxial moment data I3And 6 parameter values are total, the output layer needs to be set to be 6 output nodes, and each node corresponds to each parameter value one by one.
Further, the stress analysis mechanism includes a judgment processing module, a single-point contact analysis module, a two-point contact analysis module, and a data storage unit, where the judgment processing module is respectively connected to the single-point contact analysis module, the two-point contact analysis module, and the data storage unit in a bidirectional manner.
The judging and processing module is used for judging the number of the contact points, if the contact points are in single-point contact, the subsequent processing is delivered to the single-point contact analysis module for processing, if the contact points are in two-point contact, the two-point contact analysis module is used for judging whether the assembly needs to be readjusted according to the stress analysis result of the single-point contact analysis module/the two-point contact analysis module, so that an assembly analysis result is obtained, and the data storage module is used for storing important data information of all processes, such as the contact point judgment result, the stress analysis result, the assembly analysis result and the like.
Further, the judgment processing module is provided with a contact point judgment unit and an analysis result judgment unit, an output end of the contact point judgment unit is connected with an input end of the single-point contact analysis module and an input end of the two-point contact analysis module, and an input end of the analysis result judgment unit is connected with an output end of the single-point contact analysis module and an output end of the two-point contact analysis module.
The contact point judging unit is used for judging the number of the contact points, and the analysis result judging unit is used for obtaining an assembly analysis result.
A large length-diameter ratio shaft hole assembling method based on contact force and moment prediction and analysis comprises the following steps:
step one, a prediction mechanism passes through a three-axis pose parameter I1Triaxial contact force data I2Triaxial moment data I3Actual contact force/moment data I for predicting assembly process5；
Secondly, the stress analysis mechanism analyzes the pose parameter I according to the three axes1Actual contact force/moment data I5And analyzing the stress condition of the large length-diameter ratio inserting shaft and the assembling hole in the assembling process to obtain an adjusting and analyzing result of the assembling posture.
Through the design, the prediction mechanism can predict the stress condition of the shaft with large length-diameter ratio in real time, namely actual contact force/moment data I5(ii) a The stress analysis mechanism analyzes the stress condition of the shaft and the hole according to the data of the shaft with the large length-diameter ratio to obtain whether the assembly position is available or not and whether the assembly posture is required to be adjusted or not, so that the assembly machine is accurately controlledThe human motion provides data support, improves assembly accuracy and efficiency.
Further, the step one comprises the following steps:
s1.1, a static pose and force/moment relation mapping module establishes a static pose and force/moment relation mapping model of an assembling end of the assembling robot:
s1.1.1, establishing a BP neural network, wherein an input layer of the BP neural network is 3 input nodes, and an output layer of the BP neural network is 6 output nodes;
s1.1.2, training the BP neural network by using the pre-collected N-set matching end pose and measured force/moment data pair J in the unassembled state;
each set of assembly end pose and measurement force/torque data pairs J comprises three-axis pose parameters I1aTriaxial contact force data I2aTriaxial moment data I3aWherein, three-axis pose parameters I1aThree-axis contact force data I as 3 input parameters of BP neural network2aTriaxial moment data I3a6 output parameters as BP neural network;
s1.1.3, verifying the BP neural network by using the other M-group matching end pose and measured force/moment data pair J in the non-assembled state which are acquired in advance;
s1.1.4, the BP neural network after verification is the mapping model of the static pose and the force/moment relation;
the static pose and force/moment relation mapping model is obtained through a BP neural network: three-axis pose parameter I of assembling end of assembling robot1Force/moment data I influenced by gravity4The relationship between;
wherein the gravity-influenced force/moment data I4The three-axis gravity influence force and the three-axis gravity influence moment comprise assembly ends;
the BP neural network can well learn the three-axis pose parameter I1aContact force with three axes data I2aTriaxial moment data I3aThe training and the verification can be finished only by inputting a plurality of groups of data pairs into the neural network according to the data mapping relation between the data pairs, and finally, the rapid calculation is obtainedAnd (3) mapping the static pose and force/moment relation model.
S1.2, in the assembling process, the processing module receives dynamic three-axis pose parameters I of the assembling end of the assembling robot in real time1bTriaxial contact force data I2bTriaxial moment data I3b；
S1.3, the processing module processes the dynamic three-axis pose parameter I of the S1.21bInputting the data into a static pose and force/moment relation mapping module to obtain dynamic gravity-influenced force/moment data I4b；
S1.4, the processing module obtains dynamic gravity-influenced force/moment data I obtained in the S1.34bAnd the triaxial contact force data I of the step two2bTriaxial moment data I3bInputting the data into a dynamic actual contact force/moment data calculation module to obtain actual contact force/moment data I of the assembly end5。
Because the stress influence of the unassembled state comprises two factors of gravity and self error, the mapping relation obtained by the mapping model of the static pose and the force/moment relation is the relationship between the pose and (gravity + error), and the force/moment data I influenced by the gravity is4The data includes the combined influence of gravity and error, and the gravity influence is named as the gravity influence only because the interference of gravity is larger.
Through the design, the three-axis pose parameter I in the unassembled state1aTriaxial contact force data I2aTriaxial moment data I3aA model for mapping static pose and force/moment relationships can be trained and then three-axis pose parameters I can be used during assembly1bInputting the model, dynamic gravity-influenced force/moment data I can be obtained4bThe first to the second4bContact force with three axes data I2bTriaxial moment data I3bComparing and obtaining actual contact force/moment data I5Thereby providing an accurate calculation basis for controlling the control force of the assembling robot.
Furthermore, the interference rejection calculation method in S1.4 is as follows:
wherein, Fx、Fy、FzFor the triaxial contact force data I detected in the second step2b，Fx0、Fy0、Fz0Dynamic gravity-influenced force/moment data I obtained for step three4bThree axes of gravity influence of middle, Tx、Ty、TzFor the triaxial moment data I detected in the second step3b，Tx0、Ty0、Tz0Dynamic gravity-influenced force/moment data I obtained for step three4bMiddle three-axis gravity-induced moment, Fcx、Fcy、Fcz、Tcx、Tcy、TczActual contact force/moment data I for the fitting end5。
Further design, the second step comprises the following steps:
s2.1, the stress analysis mechanism receives actual contact force/moment data I in real time5；
S2.2, the contact point judging unit identifies the number of the contact points of the shaft hole with the large length-diameter ratio, if the number of the contact points is 1, the S2.3 is entered, and if the number of the contact points is 2, the S2.4 is entered;
s2.3, the single-point contact analysis module analyzes the parameters I according to the three-axis pose1Actual contact force/moment data I5Calculating the single-point reaction force N and the single-point resistance f of the shaft hole, and entering S2.5;
s2.4, the two-point contact analysis module analyzes the three-axis pose parameter I1Actual contact force/moment data I5Calculating the reaction force N of two points in the shaft hole1、N2And two-point resistance f1、f2Entering S2.6;
s2.5, the analysis result judging unit analyzes the calculation result of S2.3:
if N is greater than or equal to AN，f≥Af，ANAs a single point reactionThreshold of exertion, AfIf the single-point resistance threshold is obtained, the analysis result is that the assembly angle needs to be adjusted, otherwise, the analysis result is normal assembly;
outputting an analysis result;
s2.6, the analysis result judgment unit analyzes the calculation result of S2.4:
if N is present1≥AN1，N2≥AN2，f1≥Af1，f2≥Af2，AN1、AN2Two points of reaction force threshold, Af1、Af2If the resistance threshold is two points, the analysis result is that the assembly angle needs to be adjusted;
and outputting an analysis result.
Through the design, the assembly angle of the shaft with the large length-diameter ratio and the hole is accurate, and the analyzed shaft hole single-point reaction force N and single-point resistance f (or shaft hole two-point reaction force N)1、N2And two-point resistance f1、f2) The insertion can be completed in a small interval when the continuous assembly is carried out, but once the interval is exceeded, the continuous insertion is only blocked and cannot be completed, and the direction of the insertion action needs to be adjusted again. The method analyzes each assembly action in real time, and three-axis pose parameters I are acquired along with the assembly process1Triaxial contact force data I2Triaxial moment data I3The analysis process is repeated until the assembly is finally completed.
Further describing, in S2.3, the method for calculating the axle hole single-point reaction force N and the single-point resistance force f is as follows:
s2.3.1, determining an analysis plane P-O-Q from the assembled contact points, the analysis plane P-O-Q being an axial section showing a large length to diameter ratio of all contact points;
s2.3.2, decomposing the three-axis pose parameters I1Actual contact force/moment data I5Obtaining a contact force F based on said analysis plane P-O-Qp、FqMoment TrA deflection included angle theta, an axial length l and an axial diameter d of the shaft hole, wherein FqA contact force in the axial direction of the shaft with a large length-diameter ratio, FpIs vertical toContact force in the axial direction, TrIs the moment perpendicular to the analysis plane P-O-Q;
s2.3.3, substituting the data obtained by decomposing S3.2 into the following formula group:
obtaining a single-point reaction force N and a single-point resistance f of the shaft hole;
the design takes a certain shaft section of a shaft as an analysis plane P-O-Q, but the analysis plane P-O-Q needs to comprise all contact points, so that if the shaft hole single-point reaction force N and the single-point resistance f obtained by analysis on the analysis plane P-O-Q exceed a normal stress interval, the shaft posture of the analysis plane P-O-Q needs to be adjusted, and then whether the shaft sections at other angles are normally stressed is analyzed after adjustment, so that the shaft is completely aligned with the hole, and the assembly is completed smoothly.
Approximately, S2.4 calculates the two-point reaction force N of the shaft hole1、N2And two-point resistance f1、f2The method comprises the following steps:
s2.4.1, determining an analysis plane P-O-Q from the assembled contact points, the analysis plane P-O-Q being an axial section showing a large length to diameter ratio of all contact points;
s2.4.2, decomposing the three-axis pose parameters I1Actual contact force/moment data I5Obtaining a contact force F based on said analysis plane P-O-Qp、FqMoment TrDeflection included angle theta of shaft hole, shaft length l, shaft diameter d and non-inserted shaft length h, wherein FqA contact force in the axial direction of the shaft with a large length-diameter ratio, FpContact force in a direction perpendicular to the axis, TrIs the moment perpendicular to the analysis plane P-O-Q;
s2.4.3, substituting the data obtained by decomposing S3.2 into the following formula group:
obtaining the reaction force N of two points of the shaft hole1、N2And two-point resistance f1、f2。
The invention has the beneficial effects that: by establishing an accurate mathematical model and solving parameters through collected data, the data pairs under different conditions are collected to be used for training a neural network model and establishing a mapping relation of the neural network model, so that the force and the moment are accurately predicted, whether the assembly of the shaft has deviation or not is accurately analyzed through the stress condition of the shaft with large length-diameter ratio in the assembly process after prediction, and analysis support is provided for accurately controlling the assembly action.
Drawings
FIG. 1 is a block diagram of a system architecture;
FIG. 2 is a schematic main flow chart of step one;
FIG. 3 is a flow chart of the establishment of a mapping model of the relationship between the static pose and the force/moment;
FIG. 4 is a schematic flow chart of step two;
FIG. 5 is a flowchart of the operation of the prediction step of the embodiment;
FIG. 6 is a diagram illustrating an embodiment of a BP neural network training error;
FIG. 7 is a diagram illustrating an embodiment of a BP neural network validation error;
FIG. 8 is a graph comparing the predicted effects of the examples;
FIG. 9 is a schematic view of a contact point analysis in axial section;
fig. 10 is a mechanical analysis diagram of an axial section.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific embodiments:
a large length-diameter ratio shaft hole assembly system based on contact force and moment prediction and analysis is shown in figure 1 and comprises an assembly robot 1, an assembly end of the assembly robot is provided with an attitude sensor 1a and a force sensor 1b, and the attitude sensor 1a is used for detecting a three-axis pose parameter I of the assembly end1The force sensor 1b is used for detecting the triaxial contact force data I of the assembly end2Triaxial moment data I3；
The device also comprises a prediction mechanism 2 and a stress analysis mechanism 3, wherein the prediction mechanism 2 is used for predicting the three-axis pose parameter I1Triaxial contact force data I2Triaxial moment data I3Actual contact force/moment data I for predicting assembly process5The stress analysis mechanism 3 is used for analyzing the three-axis pose parameters I1Actual contact force/moment data I5Analyzing the stress condition of the large length-diameter ratio inserting shaft and the assembling hole in the assembling process to obtain an adjusting and analyzing result of the assembling posture;
the data input end group of the prediction mechanism 2 is connected with the output end of the attitude sensor 1a and the output end of the force sensor 1b, the data output end group of the prediction mechanism 2 is connected with the data input end group of the stress analysis mechanism 3, the data input end group of the stress analysis mechanism 3 is also connected with the output end of the attitude sensor 1a, and the data output end group of the stress analysis mechanism 2 is connected with the data input end group of the assembly robot control mechanism 4.
The prediction mechanism 2 comprises a static pose and force/moment relation mapping module 2a, a dynamic actual contact force/moment data calculation module 2b, a processing module 2c and a data storage module 2d, wherein the processing module 2c is respectively connected with the static pose and force/moment relation mapping module 2a, the dynamic actual contact force/moment data calculation module 2b and the data storage module 2 d.
The static pose and force/moment relation mapping module 2a is provided with a BP neural network model, the input layer of the BP neural network model is 3 input nodes, and the output layer of the BP neural network model is 6 output nodes.
The stress analysis mechanism 3 comprises a judgment processing module 3a, a single-point contact analysis module 3b, a two-point contact analysis module 3c and a data storage unit 3d, wherein the judgment processing module 3a is respectively in bidirectional connection with the single-point contact analysis module 3b, the two-point contact analysis module 3c and the data storage unit 3 d.
The judgment processing module 3a is provided with a contact point judgment unit and an analysis result judgment unit, wherein the output end of the contact point judgment unit is connected with the input end of the single-point contact analysis module 3b and the input end of the two-point contact analysis module 3c, and the input end of the analysis result judgment unit is connected with the output end of the single-point contact analysis module 3b and the output end of the two-point contact analysis module 3 c.
The preferred device parameters of this embodiment are as follows:
preferred models of the assembly robot are: antuan MOTOMAN MH12, controller: DX200, load: 12kg, degree of freedom: 6, repeated positioning precision: ± 0.08mm, maximum working radius: 1440mm, power source capacity: 1.5 kVA.
The assembly end of the assembly robot is provided with an attitude sensor 1a, and a controller: DX200 is the control mechanism of the assembly robot;
the force sensor 1b is preferably of the type: the six-dimensional force sensor of ATI-mini45-E has the main technical parameters as follows: measuring range: SI-290-10
Fx,Fy(±N) 290
Fz(±N) 580
Tx,Ty(±Nm) 10
Tz(±Nm) 10
Resolution ratio: SI-290-10
Fx,Fy(N) 1/4
Fz(N) 1/4
Tx,Ty(Nm) 1/188
Tz(Nm) 1/376
And a processor host is additionally arranged, and a prediction mechanism 2 and a stress analysis mechanism 3 are arranged on the processor host.
A large length-diameter ratio shaft hole assembling method based on contact force and moment prediction and analysis comprises the following steps:
step one, a prediction mechanism 2 predicts a three-axis pose parameter I1Triaxial contact force data I2Triaxial moment data I3Actual contact force/moment data I for predicting assembly process5；
Secondly, the stress analysis mechanism 3 analyzes the three-axis pose parameter I1Actual contact force/moment data I5And analyzing the stress condition of the large length-diameter ratio inserting shaft and the assembling hole in the assembling process to obtain an adjusting and analyzing result of the assembling posture.
Wherein, the first step is as shown in fig. 2:
s1.1, a static pose and force/moment relation mapping module 2a establishes a static pose and force/moment relation mapping model of an assembling end of the assembling robot, as shown in FIG. 2:
s1.1.1, establishing a BP neural network, wherein an input layer of the BP neural network is 3 input nodes, and an output layer of the BP neural network is 6 output nodes;
s1.1.2, training the BP neural network by using the pre-collected N-set matching end pose and measured force/moment data pair J in the unassembled state;
each set of assembly end pose and measurement force/torque data pairs J comprises three-axis pose parameters I1aTriaxial contact force data I2aTriaxial moment data I3aWherein, three-axis pose parameters I1aThree-axis contact force data I as 3 input parameters of BP neural network2aTriaxial moment data I3a6 output parameters as BP neural network;
s1.1.3, verifying the BP neural network by using the other M-group matching end pose and measured force/moment data pair J in the non-assembled state which are acquired in advance;
s1.1.4, the BP neural network after verification is the mapping model of the static pose and the force/moment relation;
the static pose and force/moment relation mapping model is obtained through a BP neural network: three-axis pose parameter I of assembling end of assembling robot1Force/moment data I influenced by gravity4The relationship between;
wherein the gravity-influenced force/moment data I4The three-axis gravity influence force and the three-axis gravity influence moment comprise assembly ends;
s1.2, in the assembling process, the processing module 2c receives dynamic three-axis pose parameters I of the assembling end of the assembling robot in real time1bTriaxial contact force data I2bTriaxial moment data I3b；
S1.3, the processing module 2c converts the dynamic three-axis pose parameter I of S1.21bInputting the data into a static pose and force/moment relation mapping module 2a to obtain dynamic gravity-influenced force/moment data I4b；
S1.4, the processing module 2c processes S1.3The dynamic gravity-influenced force/moment data I obtained4bAnd the triaxial contact force data I of the step two2bTriaxial moment data I3bInputting the data into a dynamic actual contact force/moment data calculation module 2b to obtain actual contact force/moment data I of the assembly end5。
The interference rejection calculation method comprises the following steps:
wherein, Fx、Fy、FzFor the triaxial contact force data I detected in the second step2b，Fx0、Fy0、Fz0Dynamic gravity-influenced force/moment data I obtained for step three4bThree axes of gravity influence of middle, Tx、Ty、TzFor the triaxial moment data I detected in the second step3b，Tx0、Ty0、Tz0Dynamic gravity-influenced force/moment data I obtained for step three4bMiddle three-axis gravity-induced moment, Fcx、Fcy、Fcz、Tcx、Tcy、TczActual contact force/moment data I for the fitting end5。
Step two is shown in fig. 4:
s2.1, the stress analysis mechanism 3 receives the actual contact force/moment data I in real time5；
S2.2, the contact point judging unit identifies the number of the contact points of the shaft hole with the large length-diameter ratio, if the number of the contact points is 1, the S2.3 is entered, and if the number of the contact points is 2, the S2.4 is entered;
s2.3, the single-point contact analysis module 2b analyzes the parameters I according to the three-axis pose1Actual contact force/moment data I5Calculating the single-point reaction force N and the single-point resistance f of the shaft hole, and entering S2.5;
s2.4, the two-point contact analysis module 2c analyzes the three-axis pose parameter I1Actual contact force/moment data I5Calculating the reaction force N of two points in the shaft hole1、N2And two-point resistance f1、f2Entering S2.6;
s2.5, the analysis result judging unit analyzes the calculation result of S2.3:
if N is greater than or equal to AN，f≥Af，ANAs a single point reaction force threshold, AfIf the single-point resistance threshold is obtained, the analysis result is that the assembly angle needs to be adjusted, otherwise, the analysis result is normal assembly;
outputting an analysis result;
s2.6, the analysis result judgment unit analyzes the calculation result of S2.4:
if N is present1≥AN1，N2≥AN2，f1≥Af1，f2≥Af2，AN1、AN2Two points of reaction force threshold, Af1、Af2If the resistance threshold is two points, the analysis result is that the assembly angle needs to be adjusted;
and outputting an analysis result.
In this embodiment, the single-point stress situation is shown in part (a) of fig. 10, the cross-sectional plane is the analysis plane P-O-Q, and the method for calculating the single-point reaction force N and the single-point resistance f of the shaft hole in S2.3 is as follows:
s2.3.1, determining an analysis plane P-O-Q from the assembled contact points, the analysis plane P-O-Q being an axial section showing a large length to diameter ratio of all contact points;
s2.3.2, decomposing the three-axis pose parameters I1Actual contact force/moment data I5Obtaining a contact force F based on said analysis plane P-O-Qp、FqMoment TrA deflection included angle theta, an axial length l and an axial diameter d of the shaft hole, wherein FqA contact force in the axial direction of the shaft with a large length-diameter ratio, FpContact force in a direction perpendicular to the axis, TrIs the moment perpendicular to the analysis plane P-O-Q;
s2.3.3, substituting the data obtained by decomposing S3.2 into the following formula group:
obtaining a single-point reaction force N and a single-point resistance f of the shaft hole;
the two-point stress situation in this embodiment is shown in part (b) of FIG. 10, the cross-sectional plane is the analysis plane P-O-Q, S2.4 is used to calculate the two-point reaction force N of the shaft hole1、N2And two-point resistance f1、f2The method comprises the following steps:
s2.4.1, determining an analysis plane P-O-Q from the assembled contact points, the analysis plane P-O-Q being an axial section showing a large length to diameter ratio of all contact points;
s2.4.2, decomposing the three-axis pose parameters I1Actual contact force/moment data I5Obtaining a contact force F based on said analysis plane P-O-Qp、FqMoment TrDeflection included angle theta of shaft hole, shaft length l, shaft diameter d and non-inserted shaft length h, wherein FqA contact force in the axial direction of the shaft with a large length-diameter ratio, FpContact force in a direction perpendicular to the axis, TrIs the moment perpendicular to the analysis plane P-O-Q;
s2.4.3, substituting the data obtained by decomposing S3.2 into the following formula group:
obtaining the reaction force N of two points of the shaft hole1、N2And two-point resistance f1、f2。
Step one of this example was performed using the content shown in fig. 5, where 2000 sets of end pose and measured force/moment data pairs J were collected experimentally, and 1700 sets were used for network training and 300 sets were used for testing.
The experiment uses the relative error rate of the network output and the real data to express the prediction accuracy, the training error is shown in fig. 6, the testing error is shown in fig. 7, and the prediction error of the contact force/moment is 1%.
The embodiment also compares an assembly strategy adopting a random pose adjusting method, and the pose of the tail end of the robot is continuously and randomly adjusted in the inserting process, so that the robot is continuously inserted after reaching a minimum value.
In the experimental process, a coordinate surface letter P-O-Q is replaced by an X-O-Z letter, the obtained comparison effect is shown in figure 8, a red line in the figure represents the change of the contact force/moment in the assembling process predicted by the method, and a blue line represents the change of the contact force/moment in the assembling process of the comparison method, so that the method can obviously show that the parameters of the assembling process are more accurate after the accurate prediction is carried out, the data change fluctuation is smaller, and the aim of predicting the data optimized assembling is better achieved.
The adjustment strategy of the present invention can reduce Fx and Fy in time as the force/torque becomes large. When the values of Fx and Fy are changed, the insertion shaft is subjected to the frictional force of the hole wall, and the values of Fz, Tx, and Ty are also changed. The value of Tz remains constant throughout. As can be seen from the experimental results, the intervals of force adjustment gradually increase. This shows that the pose of the insert shaft gradually approaches the optimum after each pose adjustment.
Through experimental result analysis, the assembly strategy of the project can be obtained, and compliance control of 5N/0.5N · m is realized for the shaft hole assembly with large length-diameter ratio (the length-diameter ratio is more than 10).
Claims (1)
1. The method for assembling the shaft hole with the large length-diameter ratio based on contact force and moment prediction and analysis comprises an assembling robot (1), wherein an assembling end of the assembling robot is provided with a posture sensor (1a) and a force sensor (1b), and the posture sensor (1a) is used for detecting a three-axis pose parameter I of the assembling end1The force sensor (1b) is used for detecting triaxial contact force data I of the assembly end2Triaxial moment data I3；
The device also comprises a prediction mechanism (2) and a stress analysis mechanism (3), wherein the prediction mechanism (2) is used for predicting the three-axis pose parameter I1Triaxial contact force data I2Triaxial moment data I3Actual contact force/moment data I for predicting assembly process5The stress analysis mechanism (3) is used for analyzing the three-axis pose parameters I1Actual contact force/moment data I5Analyzing the stress condition of the large length-diameter ratio inserting shaft and the assembling hole in the assembling process to obtain an adjusting and analyzing result of the assembling posture;
the data input end group of the prediction mechanism (2) is connected with the output end of the attitude sensor (1a) and the output end of the force sensor (1b), the data output end group of the prediction mechanism (2) is connected with the data input end group of the stress analysis mechanism (3), the data input end group of the stress analysis mechanism (3) is also connected with the output end of the attitude sensor (1a), and the data output end group of the stress analysis mechanism (3) is connected with the data input end group of the assembly robot control mechanism (4);
the prediction mechanism (2) comprises a static pose and force/moment relation mapping module (2a), a dynamic actual contact force/moment data calculation module (2b), a processing module (2c) and a data storage module (2d), wherein the processing module (2c) is respectively connected with the static pose and force/moment relation mapping module (2a), the dynamic actual contact force/moment data calculation module (2b) and the data storage module (2 d);
the static pose and force/moment relation mapping module (2a) is provided with a BP neural network model, the input layer of the BP neural network model is provided with 3 input nodes, and the output layer of the BP neural network model is provided with 6 output nodes;
the stress analysis mechanism (3) comprises a judgment processing module (3a), a single-point contact analysis module (3b), a two-point contact analysis module (3c) and a data storage unit (3d), wherein the judgment processing module (3a) is respectively in bidirectional connection with the single-point contact analysis module (3b), the two-point contact analysis module (3c) and the data storage unit (3 d);
the judgment processing module (3a) is provided with a contact point judgment unit and an analysis result judgment unit, the output end of the contact point judgment unit is connected with the input end of the single-point contact analysis module (3b) and the input end of the two-point contact analysis module (3c), and the input end of the analysis result judgment unit is connected with the output end of the single-point contact analysis module (3b) and the output end of the two-point contact analysis module (3 c);
it is characterized by comprising:
step (ii) ofFirstly, a predicting mechanism (2) passes through a three-axis pose parameter I1Triaxial contact force data I2Triaxial moment data I3Actual contact force/moment data I for predicting assembly process5；
Secondly, the stress analysis mechanism (3) analyzes the three-axis pose parameter I1Actual contact force/moment data I5Analyzing the stress condition of the large length-diameter ratio inserting shaft and the assembling hole in the assembling process to obtain an adjusting and analyzing result of the assembling posture;
the first step comprises the following steps:
s1.1, a static pose and force/moment relation mapping module (2a) establishes a static pose and force/moment relation mapping model of an assembling end of the assembling robot:
s1.1.1, establishing a BP neural network, wherein an input layer of the BP neural network is 3 input nodes, and an output layer of the BP neural network is 6 output nodes;
s1.1.2, training the BP neural network by using the pre-collected N-set matching end pose and measured force/moment data pair J in the unassembled state;
each set of assembly end pose and measurement force/torque data pairs J comprises three-axis pose parameters I1aTriaxial contact force data I2aTriaxial moment data I3aWherein, three-axis pose parameters I1aThree-axis contact force data I as 3 input parameters of BP neural network2aTriaxial moment data I3a6 output parameters as BP neural network;
s1.1.3, verifying the BP neural network by using the other M-group matching end pose and measured force/moment data pair J in the non-assembled state which are acquired in advance;
s1.1.4, the BP neural network after verification is the mapping model of the static pose and the force/moment relation;
the static pose and force/moment relation mapping model is obtained through a BP neural network: three-axis pose parameter I of assembling end of assembling robot1Force/moment data I influenced by gravity4The relationship between;
wherein the gravity-influenced force/moment data I4Comprises a suitThe three-axis gravity influence force and the three-axis gravity influence moment of the matching end;
s1.2, in the assembling process, the processing module (2c) receives dynamic three-axis pose parameters I of the assembling end of the assembling robot in real time1bTriaxial contact force data I2bTriaxial moment data I3b；
S1.3, the processing module (2c) converts the dynamic three-axis pose parameter I of S1.21bInputting the data into a static pose and force/moment relation mapping module (2a) to obtain dynamic gravity-influenced force/moment data I4b；
S1.4, the processing module (2c) obtains the dynamic gravity-influenced force/moment data I obtained in the S1.34bAnd the triaxial contact force data I of the step two2bTriaxial moment data I3bInputting the data into a dynamic actual contact force/moment data calculation module (2b) to obtain actual contact force/moment data I of the assembling end5；
The interference elimination calculation mode in S1.4 is as follows:
wherein, Fx、Fy、FzFor the triaxial contact force data I detected in step S1.22b，Fx0、Fy0、Fz0For the dynamic gravity-influenced force/moment data I obtained in step S1.34bThree axes of gravity influence of middle, Tx、Ty、TzFor the triaxial moment data I detected in step S1.23b，Tx0、Ty0、Tz0For the dynamic gravity-influenced force/moment data I obtained in step S1.34bMiddle three-axis gravity-induced moment, Fcx、Fcy、Fcz、Tcx、Tcy、TczThe actual contact force/moment number of the fitting endAccording to I5；
The second step comprises the following steps:
s2.1, the stress analysis mechanism (3) receives actual contact force/moment data I in real time5；
S2.2, the contact point judging unit identifies the number of the contact points of the shaft hole with the large length-diameter ratio, if the number of the contact points is 1, the S2.3 is entered, and if the number of the contact points is 2, the S2.4 is entered;
s2.3, the single-point contact analysis module (3b) analyzes the parameters I according to the three-axis pose1Actual contact force/moment data I5Calculating the single-point reaction force N and the single-point resistance f of the shaft hole, and entering S2.5;
s2.4, the two-point contact analysis module (3c) analyzes the three-axis pose parameter I1Actual contact force/moment data I5Calculating the reaction force N of two points in the shaft hole1、N2And two-point resistance f1、f2Entering S2.6;
s2.5, the analysis result judging unit analyzes the calculation result of S2.3:
if N is greater than or equal to AN，f≥Af，ANAs a single point reaction force threshold, AfIf the single-point resistance threshold is obtained, the analysis result is that the assembly angle needs to be adjusted, otherwise, the analysis result is normal assembly;
outputting an analysis result;
s2.6, the analysis result judgment unit analyzes the calculation result of S2.4:
if N is present1≥AN1，N2≥AN2，f1≥Af1，f2≥Af2，AN1、AN2Two points of reaction force threshold, Af1、Af2If the resistance threshold is two points, the analysis result is that the assembly angle needs to be adjusted;
outputting an analysis result;
s2.3, the method for calculating the single-point reaction force N and the single-point resistance f of the shaft hole comprises the following steps:
s2.3.1, determining an analysis plane P-O-Q from the assembled contact points, the analysis plane P-O-Q being an axial section showing a large length to diameter ratio of all contact points;
s2.3.2, decompositionThe three-axis pose parameter I1Actual contact force/moment data I5Obtaining a contact force F based on said analysis plane P-O-Qp、FqMoment TrA deflection included angle theta, an axial length l and an axial diameter d of the shaft hole, wherein FqA contact force in the axial direction of the shaft with a large length-diameter ratio, FpContact force in a direction perpendicular to the axis, TrIs the moment perpendicular to the analysis plane P-O-Q;
s2.3.3, substituting the data obtained by decomposing S3.2 into the following formula group:
obtaining a single-point reaction force N and a single-point resistance f of the shaft hole;
s2.4 calculating two-point reaction force N of shaft hole1、N2And two-point resistance f1、f2The method comprises the following steps:
s2.4.1, determining an analysis plane P-O-Q from the assembled contact points, the analysis plane P-O-Q being an axial section showing a large length to diameter ratio of all contact points;
s2.4.2, decomposing the three-axis pose parameters I1Actual contact force/moment data I5Obtaining a contact force F based on said analysis plane P-O-Qp、FqMoment TrDeflection included angle theta of shaft hole, shaft length l, shaft diameter d and non-inserted shaft length h, wherein FqA contact force in the axial direction of the shaft with a large length-diameter ratio, FpContact force in a direction perpendicular to the axis, TrIs the moment perpendicular to the analysis plane P-O-Q;
s2.4.3, substituting the data obtained by decomposing S3.2 into the following formula group:
obtaining the reaction force N of two points of the shaft hole1、N2And two-point resistance f1、f2。
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