CN107127755A - A kind of real-time acquisition device and robot polishing method for planning track of three-dimensional point cloud - Google Patents
A kind of real-time acquisition device and robot polishing method for planning track of three-dimensional point cloud Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
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- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
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Abstract
The invention discloses a kind of real-time acquisition device of three-dimensional point cloud, including robot, laser displacement sensor, robot real-time control system, the laser displacement sensor is arranged on the end of the robot by fixture, the robot real-time control system connects robot and laser displacement sensor by real-time industrial ethernet bus, for making the reading of laser displacement sensor and the pose of robot obtain synchronization, one-dimensional measurement is expanded into three-dimensional measurement, so as to obtain the three-dimensional point cloud of workpiece by scanning.The invention also discloses a kind of robot polishing method for planning track based on described device.Pose when positional information and normal direction information of the present invention based on workpiece three-dimensional point cloud are polished come planning robot, and corrected polishing track as robot to scan track, precision is high, with real-time and flexibility characteristics, and cost is low, disclosure satisfy that the processing of different workpieces.
Description
Technical field
Acquisition and robot polishing of the present invention applied to three-dimensional point cloud, be both related to based on laser displacement sensor and machine
Real-time Communication for Power obtains the device of three-dimensional point cloud between people, is directed to polishing method for planning track of the robot based on three-dimensional point cloud,
There is provided a set of new three-dimensional point cloud acquisition methods, apply to polishing field for robot and provide new test method and dress
Put.
Background technology
Now, the acquisition equipment of three-dimensional point cloud mainly has three-dimensional laser scanner, and two-dimensional line laser displacement sensor adds auxiliary
Motion is helped, above two method can obtain huge point off density cloud of counting.It can also be obtained in addition by three-coordinates measuring machine
Point cloud, the point cloud positional precision that this method is obtained is high, for sparse less cloud of counting.These equipment prices are expensive so that
Point cloud procurement cost is higher, with the appearance of consumer level products, such as Kinect so that point cloud is developed faster.
But the point cloud that the above method is obtained can be comprising the environmental information residing for target object, it is necessary to be filtered, go to original point cloud
Make an uproar, split and the processing such as registration, could obtain the point cloud of object, post-processing complexity.
Point cloud mainly applies to reverse-engineering, carries out the three-dimensionalreconstruction of existing object, is that it creates digital information storehouse；
Field of machine vision, point cloud can be used for the directions such as target identification, target three-dimensional values and map structuring, and vision is provided for robot
Information, aids in its decision-making and path planning.
Robot technology has been widely used in the fields such as welding, spraying, but the grinding workpieces of complicated shape are main still
By manual polishing and numerical control machine tooling, the former damages workers ' health because polishing noise, dust are big, and to the skill of workman
Art level requirement is higher；The latter's machining accuracy is high, but process equipment is expensive, and flexibility is poor to limit its extensive use.
Industrial robot has multiple degrees of freedom, and flexibility is good, is relatively adapted to apply to polishing field.At present, using robot
Off-line programming technique, successfully applies to complex part polishing by robot technology, but off-line programming technique requires right in advance
Instrument and workpiece coordinate system are demarcated, and the clamping error due to workpiece and processing inconsistency, cause robot polishing one
When criticizing workpiece, there is the inconsistent situation of precision, or even waster occur.
The content of the invention
To solve the problems, such as the acquisition of three-dimensional point cloud and a cloud being applied into robot polishing field, the invention provides one kind
Real-time acquisition device and robot the polishing method for planning track of three-dimensional point cloud based on laser displacement sensor.Design not similar shape
The workpiece of shape is tested, planning robot's scanning track, logical by bus based on bus communication protocol and real time operating system
Letter mode realizes the real-time Communication for Power of robot and laser displacement sensor, workpiece three-dimensional point cloud is obtained, for three-dimensional point cloud is used
In robot, polishing trajectory planning provides external condition.
To realize the above-mentioned purpose of the present invention, one aspect of the present invention provides a kind of real-time acquisition device of three-dimensional point cloud,
Including robot, laser displacement sensor, the robot real-time control system based on real time operating system, the laser displacement is passed
Sensor is arranged on the end of the robot, the artificial six degree of freedom articulated robot of machine, the robot by fixture
Real-time control system connects robot and laser displacement sensor by real-time industrial ethernet bus, for passing laser displacement
The reading of sensor and the pose of robot obtain synchronization, and one-dimensional measurement is expanded into three-dimensional measurement, so as to obtain work by scanning
The three-dimensional point cloud of part.
Further, it is Millisecond between described robot real-time control system and robot and laser displacement sensor
Communication.
Another aspect of the present invention provides a kind of robot polishing method for planning track based on described device, including step
Suddenly：
(1) by the measurement coordinate system of multi-point calibration method Calibration of Laser displacement transducer relative to robot end's coordinate system
Transition matrix；
(2) one-dimensional measurement is expanded to three-dimensional measurement, obtained by robotically-driven laser displacement sensor scanning workpiece, realization
The three-dimensional point cloud of workpiece；
(3) normal estimation is carried out to the three-dimensional point cloud, while the spatial positional information and method that are included based on three-dimensional point cloud
Pose and feed path when being polished to information planning robot, and corrected polishing rail as robot to scan track
Mark.
Further, the step (1) specifically includes：
(11) top in robot end's one demarcation of installation first, control machine people reaches a certain fixing point in space, adopts
With 5 standardizations of robot tool coordinate system, the fixing point is read relative to machine from teaching machine after the completion of tool coordinates system demarcation
The locus of device people's basis coordinates systemBp；
(12) and then in robot end's clamping laser displacement sensor, laser is radiated at the fixation by control machine people
Point, reads pose of the robot current end relative to basis coordinates systemAnd the registration of laser displacement sensorMP, the laser
The registration of displacement transducerMP and the locusBP transformational relation is：
In formulaBP --- coordinates of the spatial point p in robot basis coordinates system；
MCoordinates of p --- the spatial point p in measurement coordinate system；
--- homogeneous transform matrix of robot end's coordinate system relative to basis coordinates system；
--- homogeneous transform matrix of the coordinate system relative to robot end's coordinate system is measured, wherein,
(13) formula (1) is converted toIfThen (1) can be expressed as follows：
(14) multi-point calibration is based on, formula (3) is changed into AX=k form, an over-determined systems are constituted, and using most
A young waiter in a wineshop or an inn multiplies to solve, and form is as follows：
MakeMinimum solution X*As over-determined systems AX=k least square solution；
(15) solve and matrix is obtained after XObtain matrixAfterwards, you can so that the workpiece three-dimensional point cloud of scanning to be turned
Change under robot basis coordinates system.
Further, in step (2), described is real-time by robot the step of one-dimensional measurement is expanded into three-dimensional measurement
Control system, the x of workpiece point cloud, y location is sensed from robot end's pose, the z location of workpiece point cloud by laser displacement
Device is understood, passes through real-time bus mechanics of communication, it is ensured that the synchronization of x, y, z, one-dimensional measurement is expanded into three-dimensional measurement so as to realize.
Further, described step (3) specifically includes step：
(31) spatial positional information of each point based on three-dimensional point cloud and the normal direction information planning machine after optimizing and revising
People's polishing pose；
(32) the processing node of defining point cloud, and processing nodal information is sent to machine successively to scan sequencing
People, robot, which carries out inverse kinematics and moving interpolation, will process the continuous robot polishing rail of node series connection generation one
Mark.
Further, described step (31) specifically includes step：
(301) determine polishing when cutter position, i.e., using each point in three-dimensional point cloud included based on robot base
Mark the spatial positional information of system；Position when make it that cutter reaches polishing by Robotic inverse kinematics control machine people；
(302) posture of cutter during polishing is determined, i.e., each point based on three-dimensional point cloud estimates normal direction information and optimization
The posture of cutter when normal direction information planning robot after adjustment polishes.
Further, described step (302) specifically includes step：
(311) the closest points of k are searched for by improved k Neighborhood-region-search algorithms, i.e., using a point as body-centered, with
Fixed step size builds the square of itself, and k neighborhood search is then carried out in square, if finding k closest points, i.e.,
Search is terminated, otherwise expands the scope of square with fixed step size, k closest points are re-searched for, until in square
Inside search k closest point；
(312) improved PCA algorithms are used, different weighting functions is assigned by the point in the k neighborhoods to certain point, uses
Least square fitting method fits a plane, using the normal direction of institute's fit Plane as the point initial normal direction；It is next based on institute
State initial normal direction and adaptively pinpoint the weighting function of each point in cloud really, then initial normal direction is optimized；
(313) point cloud is redirected, i.e., the normal direction after optimization is adjusted using minimum spanning tree method so that point cloud normal direction
Point to consistent.
Further, described step (312) specifically includes step：
(321) two weighting functions are introduced into PCA algorithms so that approximating method has to the exterior point and noise in a cloud
Certain robustness, least square fitting is represented by：
S.t. | | n | |=1 (5)
ω in formulad(xi) --- Gauss weighting function so that effect of the point near apart from the point to fit Plane is a little big,
Otherwise influence is small, ωd(xi) be expressed as：σdFor apart from bandwidth, its initial value is according to the shape of workpiece
And the distribution situation of scanning element cloud is preset；
ωr(ri) --- the Gauss weighting function relevant with regression criterion, ωr(ri) be expressed as：σrFor
Regression criterion bandwidth, its initial value is preset according to the shape of workpiece and the distribution situation of scanning element cloud；
ri m--- the m times iteration, xiThe regression criterion of point, ri mIt is represented by：ri m=dm+(xi-x)Tnm；
(322) formula (5) construction covariance matrix C is solved, is expressed as follows：
In formula--- the center of neighborhood is represented, is expressed as：
K --- the points in neighborhood are represented,
By solving covariance matrix C, its corresponding characteristic vector of minimum characteristic value is the normal direction n of fit Plane,
I.e. as the initial normal direction of the point, and
(323) a characteristic coefficient F is defined for each point in point cloudcIt is positioned at smooth region, sharp spy to quantify the point
Region or transitional region are levied, characteristic coefficient is represented by：
S=2 in formula, represents when ratio is 0.5, then to judge that the point is located at sharp features region；
δl--- represent the average distance of the point 6 point closest with it；
(324) characteristic coefficient F is determinedcThe first defining point cloud of value in point cloud cluster in a certain vertex neighborhood, when certain point cloud
Points in cluster are more than the business of the points and the quantity of point cloud cluster in neighborhood, then it is assumed that this cloud cluster is principal point cloud cluster；In order to certainly
The quantity of dynamic acquisition point cloud cluster, sets an angle threshold, when 2 points of normal direction angles are more than threshold value, that is, thinks at this 2 points
In different point cloud clusters；A quantity of cloud cluster is obtained, so as to calculate the corresponding characteristic coefficient F of each pointc, a, 2 points of b's
Normal direction angle is represented by：
D (a, b)=cos-1(na,nb)； (8)
(325) F is determinedcValue after, it is adaptive calculate each point apart from bandwidth σd, regression criterion bandwidth σr, normal direction
Difference bandwidth σn, it is expressed as follows：
σd=(1+Fc)·δl (9)
σr=rmax/3Fc (10)
σn=| | ni-n||max/3Fc； (11)
(326) the 3rd weighting function is introduced in PCA algorithms, then least square fitting is expressed as：
S.t. | | n | |=1 (12)
ω in formulan(ni) --- the weighting function relevant with normal direction difference, it is expressed as：σnFor normal direction
Difference bandwidth, niFor certain initial normal direction of point, gained is solved by formula (6)；
(327) calculated by adaptometer in step (325) each put apart from bandwidth σd, regression criterion bandwidth σr, method
To difference bandwidth σnThe weighting function each put in point cloud is calculated, is replaced in covariance matrix C
Then formula (12) is solved by the covariance matrix C after replacement, obtains the optimization normal direction of each point.
Further, the step (313) specifically includes step：
(331) the maximum point of z coordinate, as root node, adjusts its normal direction and is allowed to and vector (0,0,1) in selected point cloud
Dot product is more than 0, and this will be such that the normal orientation after adjustment points on the outside of workpiece；
(332) weight c=1- is set | ni·nj|, this ensure that the nonnegative weights, and represent two neighbor points when c is smaller
Normal orientation it is more parallel；
(333) traveled through according to weight c size, if father node niWith child node njMeet ni·nj<0, then by njInstead
To；
(334) all nodes are traveled through, normal orientation is carried out to set a threshold value e in Spreading and diffusion, communication process, works as c
During≤e, multiple spot in One Diffusion Process to neighborhood works as c>During e, one point of One Diffusion Process, after the completion of judge whether to meet c≤e, then enter
Row multiple spot spreads, until being diffused into whole point cloud space；
(335) if failing to travel through all points, point cloud is not connected, and partial data forms local isolated island, if do not traveled through
Count number less than point cloud sum an one thousandth, then give up as noise, otherwise search in the data set not traveled through with
Closest point between having traveled through a little, the starting point traveled through as continuation simultaneously goes to step (334).
Compared with prior art, the present invention to scanning element cloud by carrying out normal estimation, the position based on workpiece three-dimensional point cloud
Information and normal direction information carry out pose during planning robot's polishing, and are corrected polishing rail as robot to scan track
Mark, reliable and stable, precision is high, with real-time and flexibility characteristics, and cost simple in construction is low, disclosure satisfy that different workpieces plus
Work.
Brief description of the drawings
Fig. 1 is the point cloud real-time acquisition device schematic diagram of the present invention；
Fig. 2 is caliberating device schematic diagram.
In figure：1- robots, 2- laser displacement sensors；3- robots real-time control system；4- workpiece；5- demarcation top
Point.
Embodiment
To further understand the present invention, the present invention will be further described with reference to the accompanying drawings and examples, but needs
Illustrate, the scope of protection of present invention is not limited to the scope of embodiment statement.
Embodiment
As shown in figure 1, a kind of real-time acquisition device of three-dimensional point cloud, including robot 1, laser displacement sensor 2, it is based on
The robot real-time control system 3 of real time operating system, the laser displacement sensor 2 is arranged on the robot by fixture
1 end, the robot 1 be six degree of freedom articulated robot, the robot real-time control system 3 by real-time industrial with
Too network bus connects robot 1 and laser displacement sensor 2, for making the reading of laser displacement sensor 2 and the position of robot 1
Appearance obtains synchronization, and one-dimensional measurement is expanded into three-dimensional measurement, so as to obtain the three-dimensional point cloud of workpiece 4 by scanning.
Described robot real-time control system between robot 1 and laser displacement sensor 2 for Millisecond with communicating.
During laser scanning, six axle crossmachine people are controlled to move in the way of teaching playback, with laser displacement sensor 2
Workpiece 4 is scanned, the point on workpiece 4 is gathered in real time based on apparatus of the present invention, finally gives the work based on robot basis coordinates system
Part three-dimensional point cloud.Point cloud real-time acquisition device is as shown in Figure 1.
The robot real-time control system 3 of this device is built based on real time operating system and EPA bussing technique,
The x of workpiece point cloud, y location is from robot end's pose, and the z location of workpiece point cloud is led to from laser displacement sensor 2
Cross real-time bus mechanics of communication, it is ensured that the synchronization of x, y, z, one-dimensional measurement is expanded into three-dimensional measurement, and the control so as to realize
The communication cycle of system meets requirement of real-time up to 1ms, can use the three-dimensional point cloud of device scanning generation workpiece.
The shown end clamping laser displacement sensor 2 of robot 1, reads laser displacement by analog input and output module and passes
The analog quantity output signals of sensor are simultaneously connected, then total based on EPA with the pulse signal of six controlled motors of robot
All signal acquisitions are returned to carry out data processing, communication cycle energy by the real time operating system of line technology by bus communication mode
Millisecond is reached, so that the reading synchronization of the pose and laser displacement sensor of robot is realized, by the pose of robot 1 and laser
The distance of displacement transducer 2 is solved by robot transformation equation, obtains the workpiece three-dimensional point cloud under robot basis coordinates system.
The present embodiment utilizes the workpiece of the different surface configuration of SolidWorks Software for Design, for sweep test and polishing
Experiment.This experimental design S-shaped curve surface work pieces, using aluminium alloy, and are processed, for test experiments.
A kind of robot polishing method for planning track based on described device, including step：
(1) by the measurement coordinate system of multi-point calibration method Calibration of Laser displacement transducer 2 relative to the ending coordinates of robot 1
The transition matrix of system, in order to which the point cloud measured under coordinate system is transformed under robot basis coordinates system, must shift to an earlier date Calibration of Laser position
The measurement coordinate system of displacement sensor 2 is relative to the transition matrix of robot end's coordinate system, abbreviation hand and eye calibrating；
(2) one-dimensional measurement is expanded to three-dimensional measurement, obtained by the driving of robot 1 laser displacement sensor 2 scanning workpiece, realization
Take the three-dimensional point cloud of workpiece；
(3) by setting three-dimensional point cloud described in different adaptive weighting function pairs to carry out normal direction to each point in three-dimensional point cloud
Estimation and optimization, while the position when spatial positional information included based on three-dimensional point cloud and the polishing of normal direction information planning robot 1
Appearance and feed path, and corrected continuous polishing track as robot 1 to scan track.
Specifically, the step (1) specifically includes：
(11) demarcation top 5 is installed in the end of robot 1 first, control machine people 1 reaches a certain fixing point in space,
It is relative that from teaching machine the fixing point is read using robot 1 tool coordinates system, 5 standardizations, after the completion of tool coordinates system demarcation
In the locus of the basis coordinates system of robot 1Bp；
(12) and then in the end clamping laser displacement sensor 2 of robot 1, laser is radiated at described by control machine people 1
Fixing point, reads pose of the current end of robot 1 relative to basis coordinates systemAnd the registration of laser displacement sensor 2MP, institute
State the registration of laser displacement sensor 2MP and the locusBP transformational relation is：
In formulaBP --- coordinates of the spatial point p in the basis coordinates system of robot 1；
MCoordinates of p --- the spatial point p in measurement coordinate system；
--- homogeneous transform matrix of the ending coordinates system of robot 1 relative to basis coordinates system；
--- homogeneous transform matrix of the coordinate system relative to ending coordinates system of robot 1 is measured, wherein,
(13) formula (1) is converted toIfThen (1) can be expressed as follows：
(14) in order to reduce calibrated error, based on multi-point calibration, formula (3) is changed into AX=k form, one is constituted and surpasses
Determine equation group, and solved using least square, form is as follows：
MakeMinimum solution X*As over-determined systems AX=k least square solution；
(15) solve and matrix is obtained after XObtain matrixAfterwards, you can so that the workpiece three-dimensional point cloud of scanning to be turned
Change under the basis coordinates system of robot 1.
Specifically, in step (2), described is real by robot 1 the step of one-dimensional measurement is expanded into three-dimensional measurement
When control system, the x of workpiece point cloud, y location passes from the end pose of robot 1, the z location of workpiece point cloud by laser displacement
Sensor 2 is understood, passes through real-time bus mechanics of communication, it is ensured that the synchronization of x, y, z, one-dimensional measurement is expanded into three-dimensional survey so as to realize
Amount.
Can whether the point cloud obtained by said apparatus can really reflect workpiece surface shape facility, as follow-up
The polishing trajectory planning data of robot 1, can be verified by following methods.
When carrying out replication experiment, its three-dimensional CAD model known to the workpiece 4 processed can be discrete by three-dimensional CAD model
Into a cloud as standard point cloud, because standard point cloud is, based on workpiece coordinate system, workpiece can be demarcated in advance relative to machine
The homogeneous transform matrix of the basis coordinates system of people 1, first some points are marked relative to the coordinate of workpiece coordinate system during demarcation on workpiece, then
Control machine people 1 arrives corresponding points, obtains coordinate of this in the basis coordinates system of robot 1.Obtain at same o'clock in two coordinate systems
Under expression, then the transition matrix for calculating two coordinate systems, i.e. workpiece coordinate system and the basis coordinates of robot 1 are connect by svd algorithm
The transition matrix of system.Standard point cloud can be just transformed into after the completion of demarcation under basis coordinates system, then with scanning gained workpiece point cloud
Contrasted, that is, obtain the error existed between scanning element cloud and standard point cloud, so as to verify the feasibility of the device.Due to machine
The presence of the position error of device people 1, the measurement error and calibrated error of laser displacement sensor 2, should allow scanning element cloud and standard
There is error between point cloud, but this error should be controlled within zone of reasonableness.
Specifically, described step (3) specifically includes step：
(31) spatial positional information of each point based on three-dimensional point cloud and by adaptive weighting function optimization adjust after
Normal direction information planning robot 1 polishing pose, the polishing pose planning dependent on three-dimensional point cloud gather accuracy and
The degree of accuracy of point cloud normal estimation, and finally determine the polishing quality of workpiece；
(32) the processing node of defining point cloud, and processing nodal information is sent to robot successively to scan sequencing
1, robot 1 carries out inverse kinematics and moving interpolation and will process one continuous robot 1 of node series connection generation polishing rail
Mark.
Specifically, described step (31) specifically includes step：
(301) position of cutter during polishing is determined, because containing the sky that scanning workpiece surface is each put in three-dimensional point cloud
Between positional information, and it is based on the basis coordinates system of robot 1, so as to utilize each point institute in three-dimensional point cloud to put the positional information of cloud
Comprising the spatial positional information based on the basis coordinates system of robot 1；Knife is caused by resolved motion control robot 1 of robot 1
Tool reaches position during polishing；
(302) posture of cutter during polishing is determined, that is, passes through the pre- of each point of adaptive weighting function pair three-dimensional point cloud
The posture of cutter when normal direction information planning robot 1 after estimating normal direction information and optimizing and revising polishes, therefore accurately point cloud method
There is material impact to the polishing quality of workpiece to estimation.
Specifically, described step (302) specifically includes step：
(311) the closest points of k are searched for by improved k Neighborhood-region-search algorithms, i.e., using a point as body-centered, with
Fixed step size builds the square of itself, and k neighborhood search is then carried out in square, if finding k closest points, i.e.,
Search is terminated, otherwise expands the scope of square with fixed step size, k closest points are re-searched for, until in square
Inside search k closest point, this avoid search for each neighborhood of a point when will travel through a little, improve search
Efficiency；And traditional k Neighborhood-region-search algorithms, be traversal point cloud in institute a little, calculate corresponding Euclidean distance, and carry out ascending order
Arrangement, takes out k closest points and is used as k neighborhoods；This algorithm is required for traversal point cloud when calculating each neighborhood of a point
In each point, take it is longer, do not apply to large-scale point cloud situation；
(312) improved PCA algorithms are used, different weighting functions is assigned by the point in the k neighborhoods to certain point, uses
Least square fitting method fits a plane, using the normal direction of institute's fit Plane as the point initial normal direction；It is next based on institute
State initial normal direction and adaptively pinpoint the weighting function of each point in cloud really, then initial normal direction is optimized；Traditional PCA algorithms
It is in the k neighborhoods of certain point, using least square fitting method, to fit a plane in a cloud, the normal direction of fit Plane is made
For the normal direction of the point, due to inevitably there are noise, exterior point and sharp features in a cloud, so this step is to PCA algorithms
It is improved, different weighting functions is assigned to the point in neighborhood, so as to improve the quality of fit Plane, the final normal direction that improves is estimated
The precision of meter.
(313) point cloud is redirected, i.e., the normal direction after optimization is adjusted using minimum spanning tree method so that point cloud normal direction
Point to unanimously, minimum spanning tree method is：First provide that the normal direction of any is pointed to, diffusion path then generated according to the weight of each point,
It is final to be diffused into whole point cloud space, complete the adjustment of point cloud normal direction.
Specifically, described step (312) specifically includes step：
(321) plane that be fitted PCA algorithms accurately reflects the normal direction at the point, should try one's best in guarantee fitting neighborhood
Point is located on same dough sheet, but conventional measurement means can make space exterior point and noise spot with measured deviation there is scanning
In point cloud, therefore two weighting functions are introduced into PCA algorithms and cause approximating method has to the exterior point and noise in a cloud certain
Robustness, least square fitting is represented by：
S.t. | | n | |=1 (5)
ω in formulad(xi) --- Gauss weighting function so that effect of the point near apart from the point to fit Plane is a little big,
Otherwise influence is small, ωd(xi) be expressed as：σdFor apart from bandwidth, its initial value is according to the shape of workpiece
And the distribution situation of scanning element cloud is preset；
ωr(ri) --- the Gauss weighting function relevant with regression criterion, ωr(ri) be expressed as：σrFor
Regression criterion bandwidth, its initial value is preset according to the shape of workpiece and the distribution situation of scanning element cloud；
ri m--- the m times iteration, xiThe regression criterion of point, ri mIt is represented by：ri m=dm+(xi-x)Tnm；
(322) formula (5) construction covariance matrix C is solved, is expressed as follows：
In formula--- the center of neighborhood is represented, is expressed as：
K --- the points in neighborhood are represented,
By solving covariance matrix C, its corresponding characteristic vector of minimum characteristic value is the normal direction n of fit Plane,
I.e. as the initial normal direction of the point, and
(323) a characteristic coefficient F is defined for each point in point cloudcIt is positioned at smooth region, sharp spy to quantify the point
Region or transitional region are levied, characteristic coefficient is represented by：
S=2 in formula, represents when ratio is 0.5, then to judge that the point is located at sharp features region；
δl--- represent the average distance of the point 6 point closest with it；
(324) characteristic coefficient F is determinedcValue, the point cloud cluster in first defining point cloud in a certain vertex neighborhood, when certain point cloud
Points in cluster are more than the business of the points and the quantity of point cloud cluster in neighborhood, then it is assumed that this cloud cluster is principal point cloud cluster；In order to certainly
The quantity of dynamic acquisition point cloud cluster, sets an angle threshold, when 2 points of normal direction angles are more than threshold value, that is, thinks at this 2 points
In different point cloud clusters；A quantity of cloud cluster is obtained, so as to calculate the corresponding characteristic coefficient F of each pointc, a, 2 points of b's
Normal direction angle is represented by：
D (a, b)=cos-1(na,nb)； (8)
(325) F is determinedcValue after, it is adaptive calculate each point apart from bandwidth σd, regression criterion bandwidth σr, normal direction
Difference bandwidth σn, it is expressed as follows：
σd=(1+Fc)·δl (9)
σr=rmax/3Fc (10)
σn=| | ni-n||max/3Fc； (11)
(326) because workpiece inevitably has the sharp features such as edge and turning so that the point in neighborhood is in multiple
On dough sheet, so as to cause larger normal estimation error, therefore the 3rd weighting function is introduced in PCA algorithms, then a most young waiter in a wineshop or an inn
Multiply fitting to be expressed as：
S.t. | | n | |=1 (12)
ω in formulan(ni) --- the weighting function relevant with normal direction difference, it is expressed as：σnFor normal direction
Difference bandwidth, niFor certain initial normal direction of point, gained is solved by formula (6)；
(327) calculated by adaptometer in step (325) each put apart from bandwidth σd, regression criterion bandwidth σr, method
To difference bandwidth σnThe weighting function each put in point cloud is calculated, is replaced in covariance matrix C
Then formula (12) is solved by the covariance matrix C after replacement, obtains the optimization normal direction of each point.
When being solved using above-mentioned steps, the weighting function for influenceing each point is apart from bandwidth σ respectivelyd, regression criterion bandwidth σr
With normal direction difference bandwidth σn, above-mentioned steps first according to the shape and the distribution situation of scanning element cloud of workpiece, preset σdAnd σr's
Initial value, initial normal direction is calculated using formula (6), and adaptively pinpoints the bandwidth parameter of each point in cloud really based on initial normal direction, then
Initial normal direction is optimized using formula (7).
Traditional algorithm is the global bandwidth parameter of artificial setting, and it is all that experiment proves that this parameter is not adapted to
Point.The present invention sets different bandwidth parameters to complete normal estimation according to the difference in residing region.By the way of adaptive,
According to the neighborhood characteristics of each point in a cloud, to determine corresponding bandwidth parameter, so as to complete the normal estimation of a cloud.
Specifically, the point cloud normal direction estimated by above-mentioned steps, can both have been pointed in workpiece with ambiguity, i.e. normal direction
Portion, can also point to outer workpiece, so the present invention is adjusted using minimum spanning tree method to normal direction so that point cloud normal direction refers to
To consistent, its principle is by x in the cloud that sets up an officei、xjIt is that both dot products are calculated apart from close 2 points, if ni·nj≈ 1, then
Represent that 2 points of normal orientation is identical, otherwise xiOr xjShould be reverse, therefore, the step (313) specifically includes step：
(331) the maximum point of z coordinate, as root node, adjusts its normal direction and is allowed to and vector (0,0,1) in selected point cloud
Dot product is more than 0, and this will be such that the normal orientation after adjustment points on the outside of workpiece；
(332) weight c=1- is set | ni·nj|, this ensure that the nonnegative weights, and represent two neighbor points when c is smaller
Normal orientation it is more parallel；
(333) traveled through according to weight c size, if father node niWith child node njMeet ni·nj<0, then by njInstead
To；
(334) all nodes are traveled through, normal orientation is carried out to set a threshold value e in Spreading and diffusion, communication process, works as c
During≤e, multiple spot in One Diffusion Process to neighborhood works as c>During e, one point of One Diffusion Process, after the completion of judge whether to meet c≤e, then enter
Row multiple spot spreads, until being diffused into whole point cloud space；
(335) if failing to travel through all points, point cloud is not connected, and partial data forms local isolated island, if do not traveled through
Count number less than point cloud sum an one thousandth, then give up as noise, otherwise search in the data set not traveled through with
Closest point between having traveled through a little, the starting point traveled through as continuation simultaneously goes to step (334).
The present invention obtains its normal direction information by carrying out normal estimation to a cloud, then will believe comprising normal direction information and position
The three-dimensional point of breath is as processing node, then by trajectory planning, and x, y, z, Rx, Ry, Rz information of node are processed in generation, and to sweep
The sequencing retouched will process node and synchronously be sent to robot 1 successively, and robot 1 carries out moving interpolation and inverse kinematics is asked
Solution, generation polishing posture, so as to complete to scan the polishing of workpiece.
Above-mentioned steps are completed, that is, complete the three-dimensional point cloud collection based on apparatus of the present invention, and according only to three-dimensional point cloud
Information generate robot 1 polishing track.
The above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not to the present invention
Embodiment restriction.For those of ordinary skill in the field, it can also make on the basis of the above description
Other various forms of changes or variation.There is no necessity and possibility to exhaust all the enbodiments.It is all the present invention
Any modifications, equivalent substitutions and improvements made within spirit and principle etc., should be included in the protection of the claims in the present invention
Within the scope of.
Claims (10)
1. a kind of real-time acquisition device of three-dimensional point cloud, it is characterised in that：Including robot, laser displacement sensor, based on reality
When operating system robot real-time control system, the laser displacement sensor is arranged on the end of the robot by fixture
End, the artificial six degree of freedom articulated robot of described machine；The robot real-time control system passes through real-time industrial ethernet
Bus connects robot and laser displacement sensor, for making the reading of laser displacement sensor and the pose of robot obtain together
Step, three-dimensional measurement is expanded to by one-dimensional measurement, so as to obtain the three-dimensional point cloud of workpiece by scanning.
2. the real-time acquisition device of three-dimensional point cloud according to claim 1, it is characterised in that:Described robot is controlled in real time
System processed between robot and laser displacement sensor for Millisecond with communicating.
3. a kind of robot polishing method for planning track based on the described device of claim 1 or 2, it is characterised in that including step
Suddenly：
(1) measurement coordinate system the turning relative to robot end's coordinate system of multi-point calibration method Calibration of Laser displacement transducer is passed through
Change matrix；
(2) robotically-driven laser displacement sensor scanning workpiece, expands to three-dimensional measurement by one-dimensional measurement, obtains the three of workpiece
Dimension point cloud；
(3) by setting three-dimensional point cloud described in different adaptive weighting function pairs to carry out normal estimation to each point in three-dimensional point cloud
And optimization, while the spatial positional information included based on three-dimensional point cloud and normal direction information planning robot polishing when pose and walk
Cutter track footpath, and corrected continuous polishing track as robot to scan track.
The method for planning track 4. robot according to claim 3 polishes, it is characterised in that the step (1) is specifically wrapped
Include：
(11) top in robot end's one demarcation of installation first, control machine people reaches a certain fixing point in space, using machine
5 standardizations of device people tool coordinates system, the fixing point is read relative to robot after the completion of tool coordinates system demarcation from teaching machine
The locus of basis coordinates systemBp；
(12) and then in robot end's clamping laser displacement sensor, laser is radiated at the fixing point by control machine people,
Read pose of the robot current end relative to basis coordinates systemAnd the registration of laser displacement sensorMP, the laser displacement
The registration of sensorMP and the locusBP transformational relation is：
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MCoordinates of p --- the spatial point p in measurement coordinate system；
--- homogeneous transform matrix of robot end's coordinate system relative to basis coordinates system；
--- homogeneous transform matrix of the coordinate system relative to robot end's coordinate system is measured, wherein,
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(13) formula (1) is converted toIfThen (1) can be expressed as follows：
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(14) multi-point calibration is based on, formula (3) is changed into AX=k form, an over-determined systems are constituted, and use a most young waiter in a wineshop or an inn
Multiply to solve, form is as follows：
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(15) solve and matrix is obtained after XObtain matrixAfterwards, you can so that the workpiece three-dimensional point cloud of scanning to be transformed into
Under robot basis coordinates system.
The method for planning track 5. robot according to claim 3 polishes, it is characterised in that described by one in step (2)
The step of dimension measurement expands to three-dimensional measurement is that y location is by robot by robot real-time control system, the x of workpiece point cloud
End pose understands that the z location of workpiece point cloud passes through real-time bus mechanics of communication from laser displacement sensor, it is ensured that x,
Y, z synchronization, three-dimensional measurement is expanded to so as to realize by one-dimensional measurement.
The method for planning track 6. robot according to claim 3 polishes, it is characterised in that described step (3) is specific
Including step：
(31) spatial positional information of each point based on three-dimensional point cloud and after being optimized and revised by adaptive weighting function
Normal direction information planning robot polishing pose；
(32) the processing node of defining point cloud, and processing nodal information is sent to robot, machine successively to scan sequencing
Device people, which carries out inverse kinematics and moving interpolation, will process the continuous robot polishing track of node series connection generation one.
The method for planning track 7. robot according to claim 6 polishes, it is characterised in that described step (31) is specific
Including step：
(301) determine polishing when cutter position, i.e., using each point in three-dimensional point cloud included based on robot basis coordinates system
Spatial positional information；Position when make it that cutter reaches polishing by Robotic inverse kinematics control machine people；
(302) posture of cutter during polishing is determined, i.e., the estimating to each point of three-dimensional point cloud by adaptive weighting function
The posture of cutter when normal direction information and normal direction information planning robot polishing after optimizing and revising.
The method for planning track 8. robot according to claim 7 polishes, it is characterised in that described step (302) tool
Body includes step：
(311) k closest points are searched for by improved k Neighborhood-region-search algorithms, i.e., using a point as body-centered, with fixation
Step-length builds the square of itself, and k neighborhood search is then carried out in square, if finding k closest points, that is, terminates
Search, otherwise expands the scope of square with fixed step size, k closest points is re-searched for, until being searched in square
Rope is to k closest point；
(312) improved PCA algorithms are used, different weighting functions are assigned by the point in the k neighborhoods to certain point, using minimum
Two, which multiply fitting process, fits a plane, using the normal direction of institute's fit Plane as the point initial normal direction；It is next based on described first
Beginning normal direction adaptively pinpoints the weighting function of each point in cloud really, then initial normal direction is optimized；
(313) point cloud is redirected, i.e., the normal direction after optimization is adjusted using minimum spanning tree method so that point cloud normal direction is pointed to
Unanimously.
The method for planning track 9. robot according to claim 8 polishes, it is characterised in that described step (312) tool
Body includes step：
(321) two weighting functions are introduced into PCA algorithms causes approximating method has to the exterior point and noise in a cloud certain
Robustness, least square fitting is represented by：
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S.t. | | n | |=1 (5)
ω in formulad(xi) --- Gauss weighting function so that effect of the point near apart from the point to fit Plane is a little big, on the contrary
Influence is small, ωd(xi) be expressed as：σdFor apart from bandwidth, its initial value is according to the shape of workpiece and sweeps
The distribution situation of described point cloud is preset；
ωr(ri) --- the Gauss weighting function relevant with regression criterion, ωr(ri) be expressed as：σrFor fitting
Residual error bandwidth, its initial value is preset according to the shape of workpiece and the distribution situation of scanning element cloud；
ri m--- the m times iteration, xiThe regression criterion of point, ri mIt is represented by：ri m=dm+(xi-x)Tnm；(322) formula (5) is solved
Covariance matrix C is constructed, is expressed as follows：
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K --- the points in neighborhood are represented,
By solving covariance matrix C, its corresponding characteristic vector of minimum characteristic value is the normal direction n of fit Plane, that is, is made
For the initial normal direction of the point, and
(323) a characteristic coefficient F is defined for each point in point cloudcIt is to be located at smooth region, sharp features region to quantify the point
Or transitional region, characteristic coefficient is represented by：
S=2 in formula, represents when ratio is 0.5, then to judge that the point is located at sharp features region；
δl--- represent the average distance of the point 6 point closest with it；
(324) characteristic coefficient F is determinedcValue, the point cloud cluster in first defining point cloud in a certain vertex neighborhood, when in the cloud cluster of certain point
Points be more than neighborhood in points with point cloud cluster quantity business, then it is assumed that this cloud cluster be principal point cloud cluster；In order to automatic
The quantity of point cloud cluster is obtained, an angle threshold is set, when 2 points of normal direction angles are more than threshold value, that is, thinks that be located at this 2 points
In different point cloud clusters；A quantity of cloud cluster is obtained, so as to calculate the corresponding characteristic coefficient F of each pointc, a, the normal direction that 2 points of b
Angle is represented by：
D (a, b)=cos-1(na,nb)； (8)
(325) F is determinedcValue after, it is adaptive calculate each point apart from bandwidth σd, regression criterion bandwidth σr, normal direction difference
Bandwidth σn, it is expressed as follows：
σd=(1+Fc)·δl (9)
σr=rmax/3Fc (10)
σn=| | ni-n||max/3Fc； (11)
(326) the 3rd weighting function is introduced in PCA algorithms, then least square fitting is expressed as：
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Bandwidth, niFor certain initial normal direction of point, gained is solved by formula (6)；
(327) calculated by adaptometer in step (325) each put apart from bandwidth σd, regression criterion bandwidth σr, normal direction it is poor
Different bandwidth σnThe weighting function each put in point cloud is calculated, is replaced in covariance matrix C
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Then formula (12) is solved by the covariance matrix C after replacement, obtains the optimization normal direction of each point.
The method for planning track 10. robot according to claim 8 polishes, it is characterised in that the step (313) is specific
Including step：
(331) the maximum point of z coordinate, as root node, adjusts its normal direction and is allowed to dot product with vector (0,0,1) in selected point cloud
More than 0, this will be such that the normal orientation after adjustment points on the outside of workpiece；
(332) weight c=1- is set | ni·nj|, this ensure that the nonnegative weights, and when the method for c two neighbor points of smaller expression
It is more parallel to direction；
(333) traveled through according to weight c size, if father node niWith child node njMeet ni·nj<0, then by njReversely；
(334) all nodes are traveled through, normal orientation are carried out to set a threshold value e in Spreading and diffusion, communication process, as c≤e
When, multiple spot in One Diffusion Process to neighborhood works as c>During e, one point of One Diffusion Process, after the completion of judge whether to meet c≤e, then carry out
Multiple spot spreads, until being diffused into whole point cloud space；
(335) if failing to travel through all points, point cloud is not connected, and partial data forms local isolated island, if not traveling through points
Number is then given up less than an one thousandth for point cloud sum as noise, otherwise search in the data set not traveled through with time
Closest point between going through a little, the starting point traveled through as continuation simultaneously goes to step (334).
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| CN117422763A (en) * | 2023-12-19 | 2024-01-19 | 商飞智能技术有限公司 | Method and device for positioning polishing area and planning polishing track on surface of die |
| CN117422763B (en) * | 2023-12-19 | 2024-05-31 | 商飞智能技术有限公司 | Method and device for positioning polishing area and planning polishing track on surface of die |
| CN118066987A (en) * | 2024-04-19 | 2024-05-24 | 中国空气动力研究与发展中心超高速空气动力研究所 | Automatic temperature-sensitive paint film thickness measurement electrical control system and electrical control method |
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