CN105425582B  A kind of Stewart mechanisms online calibration method based on Kalman filtering  Google Patents
A kind of Stewart mechanisms online calibration method based on Kalman filtering Download PDFInfo
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 CN105425582B CN105425582B CN201510741814.5A CN201510741814A CN105425582B CN 105425582 B CN105425582 B CN 105425582B CN 201510741814 A CN201510741814 A CN 201510741814A CN 105425582 B CN105425582 B CN 105425582B
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Abstract
A kind of Stewart mechanisms online proving algorithm based on Kalman filtering, it has five big steps：First, pose measurement equipment is fixed on Stewart mechanisms side, the range of movement of its working space covering Stewart mechanisms；2nd, Stewart mechanisms pose is adjusted, Stewart mechanisms moving platform is moved along different directions, using pose measurement device measuring Stewart mechanisms moving platform pose, the pose data that theoretical pose data obtain with measurement is contrasted, obtains position and attitude error data；3rd, theoretical pose data and position and attitude error data are substituted into Kalman filtering algorithm, by interative computation, obtains error source data；4th, error source data is substituted into Stewart kinematics of mechanism normal solutions, forward kinematics solution is modified；5th, circulation performs second step to the 4th step, untill position and attitude error Data Convergence.The present invention is simple to operate, implementation cost is low, demarcates efficiency high.
Description
Technical field
The present invention relates to parallel institution technical field, more particularly to a kind of Stewart mechanisms based on Kalman filtering to exist
Line scaling method.
Background technology
Stewart mechanisms have the series of advantages such as higher precision, rigidity and load capacity, are obtained in industrial circle
It is widely applied.Precision is to weigh the important indicator of Stewart mechanism performances.Using the design of advanced mechanism, processing and manufacture
Method avoids mechanism error, can improve the precision of Stewart mechanisms, but implementation cost is higher, it is impossible to meets industrial
Demand.Kinematic Calibration is to carry highprecision effective ways as a kind of posterior error compensating method.
The step of mechanism calibration is：The mathematical modeling of mechanism is initially set up, is missed for describing mechanism side chain error with end
Functional relation between difference；Mechanism is measured by way of experiment on this basis, obtains the measured data of mechanism；It
Obtained data are substituted into mechanism mathematical modeling afterwards and carry out identification solution, calculation error amount；Error is finally substituted into mechanism
Kinematical equation compensates.
The data model of Stewart mechanisms is multivariate linear equations, in theory can be directly by seeking solution of equations
Calculate mechanism end error.But because the measured data of mechanism has certain measurement error, make the coefficient of system of linear equations
Larger perturbation is produced, so as to obtain wrong solution.This phenomenon is referred to as the illconditioning problem of system of linear equations.Solution method is logical
Optimized algorithm is crossed to estimate system of linear equations.The conventional method of estimation of Stewart mechanism calibrations is least square method.It is minimum
Although square law can solve the problem that problem of calibrating, but this method is a kind of offline method of estimation, it is necessary to be obtained before demarcation big
The measurement data of amount, demarcation are less efficient.Kalman filtering algorithm is carried out as a kind of online calibration method using real time data
Demarcation, improve the efficiency of least square method.Kalman filtering algorithm is mainly used in navigation or process control field at present, still
It is not applied to the demarcation of Stewart mechanisms.Patent " a kind of light based on Kalman filtering proposed such as inventor Li Bao states et al.
Fine SINS field calibration method " (number of patent application：201410116707.9), the patent is by kalman filter method
Applied to strapdown inertial measurement unit guiding systems.
The content of the invention
The present invention provides a kind of Stewart mechanisms online calibration method based on Kalman filtering, for solving existing skill
Implementation cost in art is high, can not the problem such as online proving, demarcation efficiency is low.
A kind of Stewart mechanisms online proving algorithm based on Kalman filtering provided by the invention, it is characterised in that：It
Comprise the following steps：
Step 1：Pose measurement equipment is fixed on Stewart mechanisms side, its working space covering Stewart mechanisms
Range of movement；
Step 2：Stewart mechanisms pose is adjusted, Stewart mechanisms moving platform is moved along different directions, utilizes pose
Measuring apparatus measurement Stewart mechanisms moving platform pose, contrasts the pose data that theoretical pose data obtain with measurement, obtains in place
Appearance error information；
Step 3：Theoretical pose data and position and attitude error data are substituted into Kalman filtering algorithm, by interative computation,
Obtain error source data；
Step 4：Error source data is substituted into Stewart kinematics of mechanism normal solutions, forward kinematics solution is modified.
Step 5：Circulation performs second step to the 4th step, untill position and attitude error Data Convergence.
Wherein, the pose measurement equipment described in step 1 includes but is not limited to threecoordinates measuring machine, vision measurer；
Wherein, the position and attitude error data described in step 2It is expressed as：
δe_{i}=[δ p_{i} ^{T}δω_{i} ^{T}]^{T}=P '_{i}P_{i}
In formula, P '_{i}To measure obtained pose data, P_{i}For theoretical pose data, n is pendulous frequency, δ_{p}For site error
Vector, δ_{ω}For attitude error vector；
Described error source data x=[δ_{L} ^{T}δ_{d} ^{T}]^{T}, define a_{i}For Stewart mechanisms moving platform hinge A_{i}Position in { A }
Put vector, b_{i}For silent flatform hinge B_{i}Position vector in { B }, L_{i}It is ith of drive rod from B_{i}To A_{i}Length, i=1,
2 ..., 6, in formula, δ_{L}For L_{i}Error, δ_{d}For a_{i}And b_{i}Error.
Wherein, " theoretical pose data and position and attitude error data are substituted into Kalman filtering algorithm, led to described in step 3
Interative computation is crossed, obtains error source data；" its implement process it is as follows：
Step 1：Establish the error model of Stewart mechanisms
Define u_{i}For L_{i}Direction vector, i=1,2 ..., 6,Arrived for Stewart mechanisms moving platform coordinate system { A }
The direction cosine matrix of Stewart mechanisms silent flatform coordinate system { B }；
The error delta of Stewart mechanisms moving platform central point_{e}It is expressed as：
δ_{e}=Jx
In formula, J is error Jacobian matrix；
In formula, J_{P}For Jacobian matrix；
J_{C}For along drive rod direction vector；
Step 2：Establish kalman filter models；
The error model of Stewart mechanisms is further rewritten as：
δe_{t}=Jx_{t}+ε_{t}, t=1,2 ..., N；
In formula, δ e_{t}For observation error amount, x_{t}For quantity of state.N carries out the number of pose conversion, ε for robot_{t}Made an uproar for measurement
Sound, ε_{t}:N(0,R_{t})。
Define ω_{t}For process noise, w_{t}:N(0,Q_{t}), y_{t}For observed quantity.Then the state of Stewart mechanism calibrations model turns
Move equation and measurement equation is：
DefinitionAnd P_{t}It is t system mode x_{t}Estimation and covariance, then system mode predictive equation and y_{t}Estimation
Value：
Gain equation：
Filtering equations：
Predict error：
P_{tt1}=P_{t1}+Q_{t1}
Evaluated error：
P_{t}=(IK_{t}J_{t})P_{tt1}。
It is abovementioned it is various in symbol description it is as follows：K_{t}For kalman gain, P_{t}For the variance of system mode.
Compared with prior art, the beneficial effects of the invention are as follows：
1. this method is a kind of Kinematic Calibration method, proving operation is simple, implementation cost is low；
2. this method can realize online proving, efficiency high is demarcated；
Brief description of the drawings
Fig. 1 is the Stewart mechanisms online calibration method flow chart provided in an embodiment of the present invention based on Kalman filtering.
Fig. 2 is Stewart schematic diagram of mechanisms.
Symbol description is as follows in figure：
In Fig. 1,For the estimation of system mode, P_{t}For the variance of system mode, t is system time, K_{t}Increase for Kalman
Benefit, J_{t}For error Jacobian matrix, R_{t}For the variance of measurement noise, y_{t}For observed quantity.I is unit matrix.x_{t}For quantity of state.
In Fig. 2, L_{i}(i=1,2 ..., 6) be Stewart mechanisms pole, O_{A}X_{A}Y_{A}Z_{A}For Stewart mechanisms moving platform
Coordinate system { A }, O_{B}X_{B}Y_{B}Z_{B}For Stewart mechanisms silent flatform coordinate system { B }, a_{i}(i=1,2 ..., 6) it is in moving platform hinge
Position vector of the heart point in { A }, b_{i}(i=1,2 ..., 6) it is position vector of the silent flatform hinge centres point in { B }.
Embodiment
Illustrate embodiments of the invention with reference to the accompanying drawings.Retouched in the accompanying drawing of the present invention or a kind of embodiment
The element and feature that the element and feature stated can be shown in one or more other accompanying drawings or embodiment are combined.Should
Work as attention, for purposes of clarity, eliminated in accompanying drawing and explanation known to unrelated to the invention, those of ordinary skill in the art
Part and processing expression and description.
Below in conjunction with the accompanying drawings, technical scheme is described further.See Fig. 1Fig. 2.
A kind of Stewart mechanisms online proving algorithm based on Kalman filtering provided by the invention, is specifically included as follows
Step：
Step 1：Pose measurement equipment is fixed on Stewart mechanisms side, its working space covering Stewart mechanisms
Range of movement；
Described pose measurement equipment includes but is not limited to threecoordinates measuring machine, vision measurer；
Step 2：Stewart mechanisms pose is adjusted, Stewart mechanisms moving platform is moved along different directions, utilizes pose
Measuring apparatus measurement Stewart mechanisms moving platform pose, contrasts the pose data that theoretical pose data obtain with measurement, obtains in place
Appearance error information；
Described position and attitude error dataIt can be expressed as：
δe_{i}=[δ p_{i} ^{T}δω_{i} ^{T}] T=P '_{i}P_{i}
In formula, P_{i}' pose the data obtained for measurement, P_{i}For theoretical pose data, n is pendulous frequency, δ_{p}For site error
Vector, δ_{ω}For attitude error vector.
Described error source data x=[δ_{L} ^{T}δ_{d} ^{T}]^{T}, define a_{i}For Stewart mechanisms moving platform hinge A_{i}Position in { A }
Put vector, b_{i}For silent flatform hinge B_{i}Position vector in { B }, L_{i}It is ith of drive rod from B_{i}To A_{i}Length, i=1,
2 ..., 6, in formula, δ_{L}For L_{i}Error, δ_{d}For a_{i}And b_{i}Error.
Step 3：Theoretical pose data and position and attitude error data are substituted into Kalman filtering algorithm, by interative computation,
Obtain error source data；
Described Kalman filtering algorithm includes following steps：
Step 1：Establish the error model of Stewart mechanisms
Define u_{i}For L_{i}Direction vector, i=1,2 ..., 6,Arrived for Stewart mechanisms moving platform coordinate system { A }
The direction cosine matrix of Stewart mechanisms silent flatform coordinate system { B },
The error delta of Stewart mechanisms moving platform central point_{e}It can be expressed as：
δ_{e}=Jx
In formula, J is error Jacobian matrix.
In formula, J_{C}For Jacobian matrix：
J_{C}For along drive rod direction vector：
Step 2：Establish kalman filter models.
The error model of Stewart mechanisms can be further rewritten as：
δe_{t}=Jx_{t}+ε_{t}, t=1,2 ..., N；
In formula, δ e_{t}For observation error amount, x is quantity of state.N carries out the number of pose conversion, ε for robot_{t}Made an uproar for measurement
Sound, ε_{t}:N(0,R_{t})。
Define ω_{t}For process noise, w_{t}:N(0,Q_{t}).The then state transition equation of Stewart mechanism calibrations model and measurement
Equation is：
DefinitionAnd P_{t}It is t system mode x_{t}Estimation and covariance, then system mode predictive equation and y_{t}Estimation
Value：
Gain equation：
Filtering equations：
Predict error：
P_{tt1}=P_{t1}+Q_{t1}
Evaluated error：
P_{t}=(IK_{t}J_{t})P_{tt1}
It is abovementioned it is various in symbol description it is as follows：K_{t}For kalman gain, P_{t}For the variance of system mode.
Step 4：Error source data is substituted into Stewart kinematics of mechanism normal solutions, forward kinematics solution is modified.
Step 5：Circulation performs second step to the 4th step, untill position and attitude error Data Convergence.
It is schematical above by reference to the accompanying drawing description of this invention, without restricted, those skilled in the art should
It is understood that in actually implementing, some changes may occur for the shape of each component and layout type in the present invention；And at this
Under the enlightenment of invention, other staff can also make the design similar to the present invention or modification and some structure are made to the present invention
The equivalent substitution of part.In particular, without departing from the design aim of the present invention, it is all it is obvious change with
And the similar Design with equivalent substitution, it is all contained within protection scope of the present invention.
Claims (3)
 A kind of 1. Stewart mechanisms online calibration method based on Kalman filtering, it is characterised in that：It comprises the following steps：Step 1：Pose measurement equipment is fixed on Stewart mechanisms side, the fortune of its working space covering Stewart mechanisms Dynamic scope；Step 2：Stewart mechanisms pose is adjusted, Stewart mechanisms moving platform is moved along different directions, utilizes pose measurement Device measuring Stewart mechanisms moving platform pose, the pose data that theoretical pose data obtain with measurement are contrasted, obtain pose mistake Difference data；Step 3：Theoretical pose data and position and attitude error data are substituted into Kalman filtering algorithm, by interative computation, obtained Error source data；Step 4：Error source data is substituted into Stewart kinematics of mechanism normal solutions, forward kinematics solution is modified；Step 5：Circulation performs second step to the 4th step, untill position and attitude error Data Convergence；Substituting into theoretical pose data and position and attitude error data in Kalman filtering algorithm described in step 3, is transported by iteration Calculate, obtain error source data；It is as follows that it implements process：Step 1：Establish the error model of Stewart mechanismsDefine u_{i}For L_{i}Direction vector, i=1,2 ..., 6,Stewart is arrived for Stewart mechanisms moving platform coordinate system { A } The direction cosine matrix of mechanism silent flatform coordinate system { B }, wherein, L_{i}It is ith of drive rod from B_{i}To A_{i}Length, A_{i}Put down to be dynamic Tablehinges point, B_{i}For silent flatform hinge；The error delta of Stewart mechanisms moving platform central point_{e}It is expressed as：δ_{e}=JxIn formula, J is error Jacobian matrix, and x is error source data；<mrow> <mi>J</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msubsup> <mi>J</mi> <mi>P</mi> <mrow> <mo></mo> <mn>1</mn> </mrow> </msubsup> </mtd> <mtd> <mrow> <mo></mo> <msubsup> <mi>J</mi> <mi>P</mi> <mrow> <mo></mo> <mn>1</mn> </mrow> </msubsup> <msub> <mi>J</mi> <mi>C</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>In formula, J_{P}For Jacobian matrix；<mrow> <msub> <mi>J</mi> <mi>P</mi> </msub> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <msup> <msub> <mi>u</mi> <mn>1</mn> </msub> <mi>T</mi> </msup> <msup> <mrow> <mo>(</mo> <mmultiscripts> <mi>R</mi> <mi>A</mi> <mi>B</mi> </mmultiscripts> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>&times;</mo> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mi>T</mi> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mtable> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> </mtable> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <msub> <mi>u</mi> <mn>6</mn> </msub> <mi>T</mi> </msup> <msup> <mrow> <mo>(</mo> <mmultiscripts> <mi>R</mi> <mi>A</mi> <mi>B</mi> </mmultiscripts> <msub> <mi>a</mi> <mn>6</mn> </msub> <mo>&times;</mo> <msub> <mi>u</mi> <mn>6</mn> </msub> <mo>)</mo> </mrow> <mi>T</mi> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>In formula, a_{i}For Stewart mechanisms moving platform hinge A_{i}Position vector in { A }；J_{C}For along drive rod direction vector；Step 2：Establish kalman filter models；The error model of Stewart mechanisms is further rewritten as：<mrow> <msub> <mi>&delta;e</mi> <mi>t</mi> </msub> <mo>=</mo> <msub> <mi>Jx</mi> <mi>t</mi> </msub> <mo>+</mo> <msub> <mi>&epsiv;</mi> <mi>t</mi> </msub> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>N</mi> <mo>;</mo> </mrow>In formula,For observation error amount, x_{t}For quantity of state, N carries out the number of pose conversion, ε for robot_{t}For measurement noise, ε_{t}:N(0,R_{t}), R_{t}For the variance of measurement noise；Define ω_{t}For process noise, ω_{t}:N(0,Q_{t}), y_{t}For observed quantity, Q_{t}For the variance of process noise, then Stewart mechanisms mark The state transition equation of cover half type and measurement equation are：<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>x</mi> <mi>t</mi> </msub> <mo>=</mo> <msub> <mi>x</mi> <mrow> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>w</mi> <mrow> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>y</mi> <mi>t</mi> </msub> <mo>=</mo> <msub> <mi>&delta;e</mi> <mi>t</mi> </msub> <mo>=</mo> <msub> <mi>J</mi> <mi>t</mi> </msub> <msub> <mi>x</mi> <mi>t</mi> </msub> <mo>+</mo> <msub> <mi>&epsiv;</mi> <mi>t</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced>DefinitionAnd P_{t}It is t system mode x_{t}Estimation and covariance, then system mode predictive equation and y_{t}Estimate：<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mrow> <mi>t</mi> <mo></mo> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mrow> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mrow> <mi>t</mi> <mo></mo> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>J</mi> <mi>t</mi> </msub> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mrow> <mi>t</mi> <mo></mo> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>J</mi> <mi>t</mi> </msub> <msub> <mi>&phi;</mi> <mrow> <mi>t</mi> <mo></mo> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mrow> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced>Gain equation：<mrow> <msub> <mi>K</mi> <mi>t</mi> </msub> <mo>=</mo> <msub> <mi>P</mi> <mrow> <mi>t</mi> <mo></mo> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> <msubsup> <mi>J</mi> <mi>t</mi> <mi>T</mi> </msubsup> <msup> <mrow> <mo>(</mo> <mrow> <msub> <mi>J</mi> <mi>t</mi> </msub> <msub> <mi>P</mi> <mrow> <mi>t</mi> <mo></mo> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> <msubsup> <mi>J</mi> <mi>t</mi> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>R</mi> <mi>t</mi> </msub> </mrow> <mo>)</mo> </mrow> <mrow> <mo></mo> <mn>1</mn> </mrow> </msup> </mrow>Filtering equations：<mrow> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mi>t</mi> </msub> <mo>=</mo> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mrow> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>K</mi> <mi>t</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>t</mi> </msub> <mo></mo> <msub> <mi>J</mi> <mi>t</mi> </msub> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mrow> <mi>t</mi> <mo></mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> </mrow>Predict error：P_{tt1}=P_{t1}+Q_{t1}Evaluated error：P_{t}=(IK_{t}J_{t})P_{tt1}It is abovementioned it is various in symbol description it is as follows：K_{t}For kalman gain, P_{t}For the variance of system mode.
 2. a kind of Stewart mechanisms online calibration method based on Kalman filtering according to claim 1, its feature exist In：Pose measurement equipment described in step 1 includes but is not limited to threecoordinates measuring machine, vision measurer.
 3. a kind of Stewart mechanisms online calibration method based on Kalman filtering according to claim 1, its feature exist In：Position and attitude error data described in step 2It is expressed as：<mrow> <msub> <mi>&delta;e</mi> <mi>i</mi> </msub> <mo>=</mo> <msup> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <msup> <msub> <mi>&delta;p</mi> <mi>i</mi> </msub> <mi>T</mi> </msup> </mrow> </mtd> <mtd> <mrow> <msup> <msub> <mi>&delta;&omega;</mi> <mi>i</mi> </msub> <mi>T</mi> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mi>T</mi> </msup> <mo>=</mo> <msub> <msup> <mi>P</mi> <mo>&prime;</mo> </msup> <mi>i</mi> </msub> <mo></mo> <msub> <mi>P</mi> <mi>i</mi> </msub> </mrow>In formula, P '_{i}To measure obtained pose data, P_{i}For theoretical pose data, n is pendulous frequency, δ_{p}For site error to Amount, δ_{ω}For attitude error vector；Described error source data x=[δ_{L} ^{T}δ_{d} ^{T}]^{T}, define a_{i}For Stewart mechanisms moving platform hinge A_{i}Position in { A } to Amount, b_{i}For silent flatform hinge B_{i}Position vector in { B }, L_{i}It is ith of drive rod from B_{i}To A_{i}Length, n=6, i=1, 2 ..., 6, in formula, δ_{L}For L_{i}Error, δ_{d}For a_{i}And b_{i}Error, { A } is Stewart mechanisms moving platform coordinate system, and { B } is Stewart mechanisms silent flatform coordinate system.
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