CN115890708A - Object gluing method, device and equipment and storage medium - Google Patents
Object gluing method, device and equipment and storage medium Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses a method, a device and equipment for gluing an object and a storage medium, and relates to the field of object gluing. The method comprises the following steps: acquiring actual point cloud of an object to be coated with glue; determining a target rotation matrix of the template object according to the actual point cloud and the template point cloud of the template object; determining a target gluing point position according to a target rotation matrix of the template object and a teaching gluing point position of the template object, and determining a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point position; and sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose. According to the technical scheme of the embodiment of the invention, the target gluing point position is determined according to the target rotation matrix and the teaching gluing point position, so that the target gluing pose is determined, and the efficiency and the accuracy of object gluing are improved.
Description
Technical Field
The invention relates to the technical field of object gluing, in particular to an object gluing method, device, equipment and storage medium.
Background
The gluing belongs to a forming stage in each link of preparation of related products, and production flows such as coating treatment agents, gluing and the like are needed, so that the quality of the glued products is good and bad. Therefore, the gluing is a key process with more labor and time.
The existing object gluing method is usually based on a point cloud matching algorithm, a gluing track is directly determined according to point cloud data of an object, the method is too complex, and the matching precision cannot be guaranteed, so that the object gluing accuracy is low. Thus, improvements are needed.
Disclosure of Invention
The invention provides a method, a device and equipment for gluing an object and a storage medium, which are used for improving the efficiency and the accuracy of gluing the object.
According to an aspect of the present invention, there is provided a method of gluing an object, comprising:
acquiring actual point cloud of an object to be coated with glue;
determining a target rotation matrix of the template object according to the actual point cloud and the template point cloud of the template object;
determining a target gluing point position according to a target rotation matrix of the template object and a teaching gluing point position of the template object, and determining a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point position;
and sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose.
According to another aspect of the present invention, there is provided an object gumming apparatus including:
the actual point cloud acquisition module is used for acquiring actual point cloud of an object to be coated with glue;
the rotation matrix determining module is used for determining a target rotation matrix of the template object according to the actual point cloud and the template point cloud of the template object;
the gluing point location determining module is used for determining a target gluing point location according to a target rotation matrix of the template object and a teaching gluing point location of the template object, and determining a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point location;
and the pose sending module is used for sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method of gluing an object according to any of the embodiments of the invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to perform the method of gluing an object according to any of the embodiments of the present invention when executed.
According to the technical scheme of the embodiment of the invention, the actual point cloud of the object to be coated with glue is obtained; determining a target rotation matrix of the template object according to the actual point cloud and the template point cloud of the template object; determining a target gluing point position according to a target rotation matrix of the template object and a teaching gluing point position of the template object, and determining a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point position; and sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose. According to the technical scheme of the embodiment of the invention, the target gluing point position is determined according to the target rotation matrix and the teaching gluing point position, so that the target gluing pose is determined, and the efficiency and the accuracy of object gluing are improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flow chart of a method for gluing an object according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for gluing an object according to a second embodiment of the present invention;
fig. 3 is a flowchart of an object glue spreading method according to a third embodiment of the present invention;
fig. 4 is a flowchart of a method for gluing an object according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an object gluing device according to a fifth embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device for implementing the method for gluing an object according to the embodiment of the invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," "third," and "object" and the like in the description and claims of the invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In addition, it should be noted that, in the technical solution of the present invention, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the actual point cloud and the template point cloud, etc. all meet the regulations of the relevant laws and regulations, and do not violate the good customs of the public order.
Example one
Fig. 1 is a flowchart of an embodiment of the present invention, which provides a method for gluing an object, where the embodiment is applicable to a case of gluing an object to be glued, and the method may be performed by an object gluing device, where the object gluing device may be implemented in a form of hardware and/or software, and the object gluing device may be configured in an electronic device, for example, an object gluing main control device.
As shown in fig. 1, the method includes:
s101, acquiring actual point cloud of an object to be coated with glue.
In this embodiment, the object to be glued may be an object that needs to be glued currently, for example, a sole of a shoe; the actual point cloud may be a point cloud of the surface of the object to be rubberized, for example a point cloud of the sole.
In a specific embodiment, the point cloud of the object to be glued can be obtained by means of RGB-D (Red Green Blue-Depth, red Green Blue-Depth image) camera shooting.
S102, determining a target rotation matrix of the template object according to the actual point cloud and the template point cloud of the template object.
In this embodiment, the template object may be an object used as a gluing template, such as a sole of a template shoe; the template point cloud may be a point cloud of the surface of the template object, which may be, for example, a point cloud of the template shoe sole. The target rotation matrix may be a matrix obtained by rotating a point on the template object. The dimension of the target rotation matrix is the same as the dimension of the template point cloud, for example, if the template point cloud is a two-dimensional point cloud, the target rotation matrix is a two-dimensional rotation matrix; and if the template point cloud is a three-dimensional point cloud, the target rotation matrix is a three-dimensional matrix.
Specifically, one object can be arbitrarily selected from objects of the same kind as the object to be glued as a template object; acquiring a surface point cloud of a template object as a template point cloud; determining a rotation matrix of the actual point cloud and a transformation matrix of the actual point cloud based on the local features of the actual point cloud; determining a rotation matrix of the template point cloud and a transformation matrix of the template point cloud based on the local features of the template point cloud; and determining a target rotation matrix of the template object according to the distance between the points in the actual point cloud after the transformation of the transformation matrix of the actual point cloud and the corresponding points in the template point cloud after the transformation of the transformation matrix of the template point cloud.
The local features are not limited in the present invention, and may be, for example, a short (Signature of Histogram of Orientation) feature.
S103, determining a target gluing point position according to the target rotation matrix of the template object and the teaching gluing point position of the template object, and determining a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point position.
In this embodiment, the teaching glue dot position may be a recorded artificially determined glue dot position; the target gluing point position can be a point position for gluing an object to be glued; the target gluing pose can be a pose in which the robot arm is used for gluing an object to be glued.
Specifically, a technician can drag a mechanical arm, glue the template object based on the gluing process, and take the gluing point position of the object in the gluing process as a teaching gluing point position; converting the teaching gluing point location through the target rotation matrix to obtain a target gluing point location; and determining the target gluing pose of the mechanical arm on the object to be glued according to the coordinates of the target gluing point position in the working coordinate system of the mechanical arm. By adopting the technical scheme, the teaching gluing point position is determined by the technical personnel according to the gluing process, the accurate teaching gluing point position can be obtained at one time, the gluing process can be attached, the complexity of the traditional teaching is greatly simplified, and the gluing efficiency and precision of the object are further improved.
And S104, sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose.
Specifically, the target gluing pose is sent to the mechanical arm in a communication mode; correspondingly, after the mechanical arm receives the template gluing pose, the object to be glued can be glued based on the target gluing pose. It should be noted that, the communication method may adopt at least one of the prior art, and the present invention is not limited to this.
The method comprises the steps of obtaining actual point cloud of an object to be coated with glue; determining a target rotation matrix of the template object according to the actual point cloud and the template point cloud of the template object; determining a target gluing point position according to a target rotation matrix of the template object and a teaching gluing point position of the template object, and determining a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point position; and sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose. By adopting the technical scheme, the target gluing point position is determined according to the target rotation matrix and the teaching gluing point position, and then the target gluing pose is determined, so that the efficiency and the accuracy of object gluing are improved.
Example two
Fig. 2 is a flowchart of an object gluing method according to a second embodiment of the present invention, and this embodiment optimizes and improves the determination operation of the target rotation matrix of the template object based on the above embodiments.
Further, the target rotation matrix of the template object is determined according to the actual point cloud and the template point cloud of the template object, and the target rotation matrix is refined into a first Fast Point Feature Histogram (FPFH) feature of the actual point cloud and a second FPFH feature of the template point cloud; selecting at least one first point set from the actual point cloud, and selecting at least one second point set from the template point cloud; determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the first FPFH characteristics and at least one group of first point sets, and determining a second rotation matrix and a second transformation matrix of the template point cloud according to the second FPFH characteristics and at least one group of second point sets; determining a new actual point cloud according to the first transformation matrix and the actual point cloud, and determining a new template point cloud according to the second transformation matrix and the template point cloud; and comparing the distance between the new actual point cloud and the new template point cloud, and determining a target rotation matrix of the template object according to the comparison result so as to complete the determination operation of the target rotation matrix of the template object.
In the embodiments of the present invention, reference may be made to the description of the foregoing embodiments, which are not described in detail.
As shown in fig. 2, the method includes:
s201, acquiring actual point cloud of an object to be coated with glue.
S202, respectively determining first FPFH (Fast Point Feature Histograms) features of the actual Point cloud and second FPFH features of the template Point cloud.
Wherein, the first FPFH characteristic can be the FPFH characteristic of the middle point of the actual point cloud; the second FPFH feature may be an FPFH feature of a midpoint of the template point cloud.
In the embodiment, a first FPFH (field-programmable gate flash) feature corresponding to each point in the actual point cloud is determined; a second FPFH feature corresponding to each point in the template point cloud is determined.
Taking a point in the actual point cloud as an example, the first FPFH feature for determining the point is described as follows: determining the number of points in the point neighborhood in the actual point cloud, and determining the distance between each point in the point neighborhood and the point; determining SPFH (Simplified Point Feature Histogram) features of each Point in the Point neighborhood; determining the ratio of the SPFH characteristics of each point in the neighborhood to the distance between each corresponding point and the point; determining the weighted average result of each ratio, namely accumulating all ratios and dividing the sum by the number of the points in the neighborhood; and determining an addition result of the weighted average result and the simplified point feature histogram SPFH feature of the point, and taking the addition result as the first FPFH feature of the point.
For example, a first FPFH characteristic of a point in the actual point cloud may be determined according to the following formula:
wherein S is q Representing q points in the actual point cloud S; s i Representing i points in the actual point cloud S; FPFH (S) q ) Represents a point S q The FPFH characteristic of (1); SPFH (S) q ) Represents point S q The SPFH characteristic of (a); SPFH (S) i ) Represents a point S i The simplified point feature histogram SPFH feature of (1); k represents a point S q The number of points in the neighborhood of (2); omega i Represents point S q And point S i The distance between them.
Correspondingly, taking a point in the template point cloud as an example, the process for determining the second FPFH feature of the point is similar to the process for determining the first FPFH feature of the midpoint of the actual point cloud, and is not repeated here.
S203, selecting at least one first point set from the actual point cloud, and selecting at least one second point set from the template point cloud.
In this embodiment, the first point set refers to a set of points selected from the actual point cloud, and may include at least one point. The second point set refers to a set of points selected from the template point cloud, and may include at least one point.
Optionally, randomly sampling a first number of points from the actual point cloud to serve as a first point set, and randomly sampling at least one group of first point sets; correspondingly, a first number of points are randomly sampled from the template point cloud as a second set of points, and at least one second set of points is randomly sampled. For example, N groups of first point sets are selected from the actual point cloud, each group of first point sets comprising M points; selecting N groups of first point sets from the template point cloud, wherein each group of first point sets comprises M points; wherein N and M are both natural numbers greater than or equal to 1. The first number is less than or equal to the threshold of the number of sampling points, and the threshold of the number of sampling points can be set by technicians according to actual requirements and practical experience, which is not limited by the invention.
It should be noted that a first distance between two points in the first point set is greater than a distance threshold, and a second distance between two points in the second point set is greater than the distance threshold. The distance threshold may be set by a technician according to actual needs and practical experience, which is not limited by the present invention.
Specifically, after a first point set is selected, determining the distance between every two points in the first point set as a first distance, and if the first distance between every two points is greater than a distance threshold, reserving the first point set; otherwise, discarding the first point set, reselecting a new first point set from the actual point cloud, and determining a new first distance between two points in the new first point set until the first distance between every two points in the first point set is greater than the distance threshold.
Correspondingly, after the second point set is selected, the distance between every two points in the second point set is determined and used as the second distance, and if the second distance between every two points is larger than the distance threshold value, the second point set is reserved; otherwise, discarding the second point set, reselecting a new second point set from the actual point cloud, and determining a new second distance between two points in the new second point set until the second distance between every two points in the second point set is greater than the distance threshold.
By adopting the technical scheme, the first distance between two points in the first point set is greater than the distance threshold, and the second distance between two points in the second point set is greater than the distance threshold, so that the situation that the covariance matrix accuracy of the actual point cloud and/or the covariance matrix accuracy of the template point cloud are poor due to too close point distances in the first point set and/or the second point set, and the accuracy of the first rotation matrix, the second rotation matrix, the first transformation matrix and/or the second transformation matrix is poor can be avoided, and the foundation can be laid for improving the accuracy of the target rotation matrix and ensuring the accuracy of object gluing.
S204, determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the first FPFH characteristics and at least one group of first point sets, and determining a second rotation matrix and a second transformation matrix of the template point cloud according to the second FPFH characteristics and at least one group of second point sets.
The first rotation matrix can be a rotation matrix for rotating coordinates of each point in the actual point cloud; the first transformation matrix may be a transformation matrix that performs coordinate transformation on each point in the actual point cloud. The first rotation matrix and the first transformation matrix have the same dimension as the actual point cloud, for example, if the actual point cloud is a three-dimensional point cloud, the first rotation matrix and the first transformation matrix are three-dimensional matrices. The second rotation matrix can be a rotation matrix for rotating coordinates of each point in the template point cloud; the second transformation matrix may be a transformation matrix that performs coordinate transformation on each point in the template point cloud. The dimensions of the second rotation matrix and the second transformation matrix are the same as the dimensions of the template point cloud, for example, if the template point cloud is a three-dimensional point cloud, the second rotation matrix and the second transformation matrix are three-dimensional matrices.
Optionally, a first rotation matrix and a first transformation matrix of the actual point cloud may be determined according to the first FPFH feature and the at least one group of first point sets based on a certain algorithm; at the same time, the same algorithm may be employed to determine a second rotation matrix and a second transformation matrix for the template point cloud based on the second FPFH features and the at least one set of second point sets.
And S205, determining a new actual point cloud according to the first transformation matrix and the actual point cloud, and determining a new template point cloud according to the second transformation matrix and the template point cloud.
The new actual point cloud can be the actual point cloud of each point in the actual point cloud after being transformed by the first transformation matrix; the new template point cloud may be the template point cloud after each point in the template point cloud is transformed by the second transformation matrix.
Optionally, the first transformation matrix is multiplied by the actual point cloud to obtain a new actual point cloud, and the second transformation matrix is multiplied by the template point cloud to obtain a new template point cloud.
It can be understood that, by adopting the above technical scheme, a new actual point cloud is obtained by multiplying the first transformation matrix by the actual point cloud, and a new template point cloud is obtained by multiplying the second transformation matrix by the template point cloud, so that the actual point cloud and the template point cloud in the working coordinate system of the mechanical arm can be obtained, and the distance between the points of the actual point cloud and the points of the template point cloud in the working coordinate system of the mechanical arm can be conveniently compared.
And S206, comparing the distance between the new actual point cloud and the new template point cloud, and determining a target rotation matrix of the template object according to the comparison result.
Optionally, determining a third distance between a point in the new actual point cloud and a point in the new template point cloud corresponding to the point; if all the third distances are within the error range, taking the second rotation matrix as a target rotation matrix; otherwise, at least one first point set is selected from the actual point cloud again, and at least one second point set is selected from the template point cloud again.
Wherein the third distance may be a distance between a point in the new actual point cloud and a point in the new template point cloud corresponding to the point.
Specifically, if the third distance between each point in the new actual point cloud and the point in the new template point cloud corresponding to each point is within the error range, the second rotation matrix is used as the target rotation matrix; otherwise, at least one group of first point sets is selected from the actual point cloud again, and at least one group of second point sets is selected from the template point cloud again; determining a new first rotation matrix and a new first transformation matrix of the actual point cloud according to the first FPFH characteristics and the at least one reselected group of first point sets, and determining a new second rotation matrix and a new second transformation matrix of the template point cloud according to the second FPFH characteristics and the at least one reselected group of second point sets; determining a new actual point cloud according to the new first transformation matrix and the actual point cloud, and determining a new template point cloud according to the new second transformation matrix and the template point cloud; determining a third distance between the point in the new actual point cloud and the point in the new template point cloud corresponding to the point; and until the third distances between the points in the new actual point cloud and the points in the new template point cloud corresponding to the points are within the error range.
It should be noted that the error range can be set by a skilled person according to actual needs and practical experience, and the present invention is not limited to this.
In one embodiment, the error range may be 5 mm, that is, if the third distance between each point in the new actual point cloud and the point in the new template point cloud corresponding to each point is within 5 mm, the second rotation matrix is taken as the target rotation matrix; otherwise, at least one group of first point set is selected from the actual point cloud again, and at least one group of second point set is selected from the template point cloud again.
It can be understood that, with the above technical solution, if the third distances between each point in the new actual point cloud and the point in the new template point cloud corresponding to each point are within the error range, the second rotation matrix is taken as the target rotation matrix; otherwise, at least one group of first point sets is selected from the actual point clouds again, and at least one group of second point sets is selected from the template point clouds again, so that the similarity degree between the points in the new actual point clouds and the corresponding points in the new template point clouds is improved, and the object gluing accuracy is improved.
S207, determining a target gluing point position according to the target rotation matrix of the template object and the teaching gluing point position of the template object, and determining a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point position.
And S208, sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose.
The method comprises the steps of respectively determining a first FPFH (field programmable gate flash) characteristic of actual point cloud and a second FPFH characteristic of template point cloud; selecting at least one first point set from the actual point cloud, and selecting at least one second point set from the template point cloud; determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the first FPFH characteristics and at least one group of first point sets, and determining a second rotation matrix and a second transformation matrix of the template point cloud according to the second FPFH characteristics and at least one group of second point sets; determining a new actual point cloud according to the first transformation matrix and the actual point cloud, and determining a new template point cloud according to the second transformation matrix and the template point cloud; and comparing the distance between the new actual point cloud and the new template point cloud, and determining a target rotation matrix of the template object according to the comparison result. By adopting the scheme, the matching degree of the obtained target gluing point position and the object to be glued can be improved after the teaching gluing point position is converted by the target rotation matrix, so that the matching degree of the target gluing pose and the object to be glued can be improved, and the gluing precision of the object can be improved.
EXAMPLE III
Fig. 3 is a flowchart of an object gluing method according to a third embodiment of the present invention, and this embodiment optimizes and improves the determination operations of the first rotation matrix, the first transformation matrix, the second rotation matrix, and the second transformation matrix based on the above embodiments.
In the embodiments of the present invention, reference may be made to the description of the foregoing embodiments, which are not described in detail.
As shown in fig. 3, the method includes:
s301, acquiring actual point cloud of the object to be coated with glue.
S302, respectively determining a first FPFH (flat-bed flash) feature of the actual point cloud and a second FPFH feature of the template point cloud.
S303, selecting at least one group of first point set from the actual point cloud, and selecting at least one group of second point set from the template point cloud.
S304, determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the first FPFH characteristics and at least one group of first point sets, and determining a second rotation matrix and a second transformation matrix of the template point cloud according to the second FPFH characteristics and at least one group of second point sets.
Optionally, determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the first FPFH feature and the at least one group of first point sets, includes: determining a first Euclidean distance between every two points in the first point set according to the first FPFH characteristic, and determining a first centroid from the first point set according to the first Euclidean distance; determining a covariance matrix of the actual point cloud according to the at least one group of first point sets and the first centroid of the at least one group of first point sets; determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the covariance matrix of the actual point cloud; correspondingly, according to the second FPFH characteristic and at least one group of second point set, a second rotation matrix and a second transformation matrix of the template point cloud are determined, which comprises: determining a second Euclidean distance between every two points in the second point set according to the second FPFH characteristic, and determining a second centroid from the second point set according to the second Euclidean distance; determining a covariance matrix of the template point cloud according to the at least one group of second point sets and a second centroid of the at least one group of second point sets; and determining a second rotation matrix and a second transformation matrix of the template point cloud according to the covariance matrix of the template point cloud.
Wherein, the FPFH characteristic can be expressed as a 32-bit array; the centroid may be the point at which the sum of the euclidean distances with the other points in the set of points to which it belongs is smallest.
Taking a group of first point sets as an example, a method for determining a first centroid of the first point set is described: subtracting the numerical value of the corresponding bit in the 32-bit array of the FPFH characteristic of one point in the first point set from the numerical value of the corresponding bit in the 32-bit array of the FPFH characteristic of other points in the first point set to obtain a 32-bit subtraction result; squaring each phase subtraction result in the subtraction results, and superposing the 32-phase subtraction results to obtain a first Euclidean distance between the two points; determining a first Euclidean distance between the point and other points in the first point set by the determination method of the first Euclidean distance between the two points; superposing the first Euclidean distance between the point and other points in the first point set to obtain the total Euclidean distance of the point; determining the total Euclidean distance of each point in the first point set by the determination method of the total Euclidean distance of the one point; and comparing the total Euclidean distances of the points, and taking the point in the first point set corresponding to the minimum total Euclidean distance as the first centroid of the group of first point sets.
Determining a first centroid of each group of first point sets by the first centroid method, and removing the first centroid from each group of first point sets; arranging the groups of first point sets with the first mass centers removed according to rows to form a first point set matrix, wherein element values of the matrix are coordinate values of all points in the first point sets; determining the average value of each column of elements in the first point set matrix, and subtracting the average value of the corresponding column from the element value of each column of the first point set matrix to be used as a new first point set matrix; determining a first point set transpose matrix corresponding to the new first point set matrix; and multiplying the new first point set matrix by the corresponding first point set transpose matrix, and dividing by the number of the first point set groups to obtain the covariance matrix of the actual point cloud. Illustratively, the covariance matrix of the actual point cloud can be obtained by the following formula:
wherein, COV S Covariance matrix, m, representing the actual point cloud S S Representing a number of groups of the first set of points; x S Representing a new first point set matrix; x S T A first point set transpose matrix corresponding to the new first point set matrix is represented.
And determining two first eigenvalues of the covariance matrix of the actual point cloud, and taking a vector of which the multiplication result of the corresponding first eigenvalue is equal to the multiplication result of the covariance matrix of the actual point cloud as a first eigenvector of the covariance matrix of the actual point cloud. Illustratively, the first eigenvector of the covariance matrix of the actual point cloud can be obtained by the following formula:
COV S v 1 ＝λ 1 v 1 ；
COV S v 2 ＝λ 2 v 2 ；
wherein, COV S The covariance matrix of the actual point cloud; v. of 1 Is a first feature vector; v. of 2 Is another first feature vector; lambda [ alpha ] 1 Is a first feature vector v 1 Corresponding characteristic values; lambda [ alpha ] 2 Is a first feature vector v 2 The corresponding characteristic value. In addition, λ is 1 And λ 2 May be determined by at least one of the prior art, and the present invention is not limited thereto.
Cross-multiplying the two first feature vectors to obtain a first cross-multiplied vector; combining the two first eigenvectors and the first cross-product vector to obtain a first rotation matrix; the first centroid of the first set of points is combined with the first rotation matrix to obtain a first transformation matrix.
Correspondingly, the determination method of the second rotation matrix and the second transformation matrix of the template point cloud is the same as the determination method of the first rotation matrix and the first transformation matrix of the actual point cloud, and the details are not repeated here.
It can be understood that, with the above technical solution, the covariance matrix of the actual point cloud is determined by determining the first centroid of at least one group of first point sets and according to at least one group of first point sets; determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the covariance matrix of the actual point cloud; determining a covariance matrix of the template point cloud by determining a second centroid of the at least one set of second point sets and according to the at least one set of second point sets; according to the covariance matrix of the template point cloud, the second rotation matrix and the second transformation matrix of the template point cloud are determined, the accuracy of the first rotation matrix and the first transformation matrix of the actual point cloud is improved, the accuracy of the second rotation matrix and the second transformation matrix of the template point cloud is improved, the accuracy of the target rotation matrix is further improved, and the accuracy of object gluing is improved.
S305, determining a new actual point cloud according to the first transformation matrix and the actual point cloud, and determining a new template point cloud according to the second transformation matrix and the template point cloud.
And S306, comparing the distance between the new actual point cloud and the new template point cloud, and determining a target rotation matrix of the template object according to the comparison result.
S307, determining a target gluing point position according to the target rotation matrix of the template object and the teaching gluing point position of the template object, and determining a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point position.
And S308, sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose.
According to the technical scheme of the embodiment of the invention, a first Euclidean distance between every two points in a first point set is determined according to a first FPFH (field-programmable gate FH) characteristic, and a first mass center is determined from the first point set according to the first Euclidean distance; determining a covariance matrix of the actual point cloud according to the at least one group of first point sets and a first centroid of the at least one group of first point sets; determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the covariance matrix of the actual point cloud; correspondingly, according to the second FPFH characteristics and the at least one set of second point sets, determining a second rotation matrix and a second transformation matrix of the template point cloud, including: determining a second Euclidean distance between every two points in the second point set according to the second FPFH characteristic, and determining a second centroid from the second point set according to the second Euclidean distance; determining a covariance matrix of the template point cloud according to the at least one group of second point sets and a second centroid of the at least one group of second point sets; and determining a second rotation matrix and a second transformation matrix of the template point cloud according to the covariance matrix of the template point cloud. By adopting the technical scheme, the accuracy of the first rotation matrix and the first transformation matrix of the actual point cloud is improved, the accuracy of the second rotation matrix and the second transformation matrix of the template point cloud is improved, the accuracy of the target rotation matrix is further improved, and the object gluing accuracy is improved.
Example four
Fig. 4 is a flowchart of an object gluing method according to a fourth embodiment of the present invention, and this embodiment optimizes and improves the determination operation of the target gluing point location based on the foregoing embodiment.
Further, the method comprises the steps of refining the target gluing point position determined according to the target rotation matrix of the template object and the teaching gluing point position of the template object into a method for calibrating the target rotation matrix; and determining a target gluing point position according to the calibrated target rotation matrix and the teaching gluing point position of the template object so as to complete the determination operation of the target gluing point position.
In the embodiments of the present invention, reference may be made to the description of the foregoing embodiments, which are not described in detail.
As shown in fig. 4, the method includes:
s401, acquiring actual point cloud of an object to be glued.
S402, determining a target rotation matrix of the template object according to the actual point cloud and the template point cloud of the template object.
And S403, calibrating the target rotation matrix.
Specifically, a point cloud registration algorithm is adopted to calibrate the target rotation matrix so as to improve the accuracy of the target rotation matrix and further improve the accuracy of the target gluing point location. It should be noted that the Point cloud registration algorithm of the present invention is not limited, for example, the Point cloud registration algorithm may be an ICP (Iterative Closest Point) algorithm, an nicap (Normal Iterative Closest Point) algorithm, a PL-ICP (Point to Line-Iterative Closest Point) algorithm, and the like.
S404, determining a target gluing point position according to the calibrated target rotation matrix and the teaching gluing point position of the template object, and determining a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point position.
Specifically, the teaching gluing point position is converted according to the calibrated target rotation matrix to obtain an updated target gluing point position, the target gluing pose is determined according to the updated target gluing point position, and the target gluing point position of the mechanical arm for the object to be glued is determined according to the target gluing point position.
S405, sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose.
The embodiment of the invention calibrates the target rotation matrix; and determining the target gluing point position according to the calibrated target rotation matrix and the teaching gluing point position of the template object. By adopting the technical scheme, the precision of the target rotating matrix is improved, the precision of the target gluing point position is improved, and the precision of gluing the object is further improved.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an object gluing device according to a fourth embodiment of the present invention, where this embodiment is applicable to a situation where a glue is applied to an object to be glued, the object gluing device may be implemented in a form of hardware and/or software, and the object gluing device may be configured in an electronic device, for example, in an object gluing main control device.
As shown in fig. 5, the apparatus includes: an actual point cloud obtaining module 501, a rotation matrix determining module 502, a gluing point location determining module 503 and a pose sending module 504. Wherein the content of the first and second substances,
an actual point cloud obtaining module 501, configured to obtain an actual point cloud of an object to be glued;
a rotation matrix determining module 502 for determining a target rotation matrix for the template object according to the actual point cloud and the template point cloud of the template object;
the gluing point position determining module 503 is configured to determine a target gluing point position according to the target rotation matrix of the template object and the teaching gluing point position of the template object, and determine a target gluing position and pose of the mechanical arm on the object to be glued according to the target gluing point position;
and a pose sending module 504, configured to send the target gluing pose to the mechanical arm, so that the mechanical arm performs gluing on the object to be glued based on the target gluing pose.
The method comprises the steps of acquiring actual point cloud of an object to be coated by an actual point cloud acquisition module; a rotation matrix determining module determines an actual point cloud and a template point cloud of a template object, and determines a target rotation matrix of the template object; the gluing point location determining module determines a target gluing point location according to a target rotation matrix of the template object and a teaching gluing point location of the template object, and determines a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point location; and the pose sending module sends the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose. By adopting the technical scheme, the method has the advantages that,
optionally, the rotation matrix determining module 502 includes:
the characteristic determining unit is used for respectively determining a first FPFH characteristic of the actual point cloud and a second FPFH characteristic of the template point cloud;
the point set selection unit is used for selecting at least one group of first point sets from the actual point cloud and at least one group of second point sets from the template point cloud;
the matrix determining unit is used for determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the first FPFH characteristics and at least one group of first point sets, and determining a second rotation matrix and a second transformation matrix of the template point cloud according to the second FPFH characteristics and at least one group of second point sets;
the point cloud determining unit is used for determining a new actual point cloud according to the first transformation matrix and the actual point cloud, and determining a new template point cloud according to the second transformation matrix and the template point cloud;
and the rotation matrix determining unit is used for comparing the distance between the new actual point cloud and the new template point cloud and determining a target rotation matrix of the template object according to a comparison result.
Optionally, the rotation matrix determining module 502 is configured to determine a first distance between two points in the first point set is greater than a distance threshold, and determine a second distance between two points in the second point set is greater than the distance threshold.
Optionally, the matrix determining unit includes:
the first centroid determining subunit is used for determining a first Euclidean distance between every two points in the first point set according to the first FPFH characteristic, and determining a first centroid from the first point set according to the first Euclidean distance;
the actual covariance matrix determining unit is used for determining a covariance matrix of the actual point cloud according to the at least one group of first point sets and the first centroid of the at least one group of first point sets;
the first rotation matrix determining unit is used for determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the covariance matrix of the actual point cloud;
correspondingly, the matrix determination unit comprises:
the second centroid determining subunit is used for determining a second Euclidean distance between every two points in the second point set according to the second FPFH characteristic, and determining a second centroid from the second point set according to the second Euclidean distance;
the template covariance matrix is used for determining a covariance matrix of the template point cloud according to the at least one group of second point sets and a second centroid of the at least one group of second point sets;
and the second rotation matrix determining subunit is used for determining a second rotation matrix and a second transformation matrix of the template point cloud according to the covariance matrix of the template point cloud.
Optionally, the point cloud determining unit includes:
and the point cloud determining subunit is used for multiplying the first transformation matrix and the actual point cloud to obtain a new actual point cloud, and multiplying the second transformation matrix and the template point cloud to obtain a new template point cloud.
Optionally, the rotation matrix determining unit includes:
a third distance determining subunit, configured to determine a third distance between a point in the new actual point cloud and a point in the new template point cloud corresponding to the point;
the rotation matrix determining subunit is used for taking the second rotation matrix as a target rotation matrix if the third distances are within the error range; otherwise, at least one first point set is selected from the actual point cloud again, and at least one second point set is selected from the template point cloud again.
Optionally, the gluing point position determining module 503 includes:
the matrix calibration unit is used for calibrating the target rotation matrix;
and the gluing point location determining unit is used for determining a target gluing point location according to the calibrated target rotation matrix and the teaching gluing point location of the template object.
The object gluing device provided by the embodiment of the invention can execute the object gluing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects for executing the object gluing method.
EXAMPLE six
FIG. 6 illustrates a block diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), 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 inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to the bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The processor 11 performs the various methods and processes described above, such as the object glue application method.
In some embodiments, the object gumming method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the above described method of gluing an object may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the object gluing method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage 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. Alternatively, the computer readable storage medium may be a machine readable signal medium. 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device 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 a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally 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. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of gluing an object, comprising:
acquiring actual point cloud of an object to be coated with glue;
determining a target rotation matrix for the template object according to the actual point cloud and the template point cloud of the template object;
determining a target gluing point position according to the target rotation matrix of the template object and the teaching gluing point position of the template object, and determining a target gluing pose of a mechanical arm on the object to be glued according to the target gluing point position;
and sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose.
2. The method of claim 1, wherein determining a target rotation matrix for a template object from the actual point cloud and a template point cloud for the template object comprises:
respectively determining a first FPFH (fast point feature histogram) feature of the actual point cloud and a second FPFH feature of the template point cloud;
selecting at least one first set of points from the actual point cloud and at least one second set of points from the template point cloud;
determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the first FPFH characteristics and the at least one group of first point sets, and determining a second rotation matrix and a second transformation matrix of the template point cloud according to the second FPFH characteristics and the at least one group of second point sets;
determining a new actual point cloud according to the first transformation matrix and the actual point cloud, and determining a new template point cloud according to the second transformation matrix and the template point cloud;
and comparing the distance between the new actual point cloud and the new template point cloud, and determining a target rotation matrix of the template object according to a comparison result.
3. The method of claim 2, wherein a first distance between two points in the first set of points is greater than a distance threshold, and wherein a second distance between two points in the second set of points is greater than a distance threshold.
4. The method of claim 2, wherein determining a first rotation matrix and a first transformation matrix for the actual point cloud from the first FPFH feature and the at least one first set of points comprises:
determining a first Euclidean distance between every two points in the first point set according to the first FPFH characteristic, and determining a first centroid from the first point set according to the first Euclidean distance;
determining a covariance matrix of the actual point cloud according to the at least one first set of points and a first centroid of the at least one first set of points;
determining a first rotation matrix and a first transformation matrix of the actual point cloud according to the covariance matrix of the actual point cloud;
correspondingly, determining a second rotation matrix and a second transformation matrix of the template point cloud according to the second FPFH feature and the at least one second set of points, comprising:
determining a second Euclidean distance between every two points in the second point set according to the second FPFH (fast Fourier transform) characteristic, and determining a second center of mass from the second point set according to the second Euclidean distance;
determining a covariance matrix of the template point cloud according to the at least one second set of points and a second centroid of the at least one second set of points;
and determining a second rotation matrix and a second transformation matrix of the template point cloud according to the covariance matrix of the template point cloud.
5. The method of claim 2, wherein determining a new actual point cloud from the first transformation matrix and the actual point cloud, and determining a new template point cloud from the second transformation matrix and the template point cloud comprises:
and multiplying the first transformation matrix and the actual point cloud to obtain a new actual point cloud, and multiplying the second transformation matrix and the template point cloud to obtain a new template point cloud.
6. The method of claim 2, wherein the comparing the distance between the new actual point cloud and the new template point cloud and determining a target rotation matrix for the template object based on the comparison comprises:
determining a third distance between the point in the new actual point cloud and the point in the new template point cloud corresponding to the point;
if the third distances are within the error range, taking the second rotation matrix as a target rotation matrix; otherwise, at least one first point set is selected from the actual point cloud again, and at least one second point set is selected from the template point cloud again.
7. The method of claim 1, wherein determining a target glue site from the target rotation matrix for the template object and a taught glue site for the template object further comprises:
calibrating the target rotation matrix;
and determining a target gluing point position according to the calibrated target rotation matrix and the teaching gluing point position of the template object.
8. An object gumming apparatus, comprising:
the actual point cloud acquisition module is used for acquiring actual point cloud of the object to be coated with glue;
the rotation matrix determining module is used for determining a target rotation matrix of the template object according to the actual point cloud and the template point cloud of the template object;
the gluing point location determining module is used for determining a target gluing point location according to the target rotation matrix of the template object and the teaching gluing point location of the template object, and determining a target gluing pose of the mechanical arm on the object to be glued according to the target gluing point location;
and the pose sending module is used for sending the target gluing pose to the mechanical arm so that the mechanical arm can glue the object to be glued based on the target gluing pose.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of gluing objects according to any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a processor to execute a method for gluing an object according to any one of claims 1 to 7.
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