CN110231030A - Sweeping robot angle maximum likelihood estimation method based on gyroscope - Google Patents
Sweeping robot angle maximum likelihood estimation method based on gyroscope Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000010408 sweeping Methods 0.000 title claims abstract description 28
- 238000007476 Maximum Likelihood Methods 0.000 title abstract 3
- 238000005259 measurement Methods 0.000 claims abstract description 11
- 238000005070 sampling Methods 0.000 claims abstract description 9
- 238000001914 filtration Methods 0.000 claims abstract description 7
- 230000002401 inhibitory effect Effects 0.000 abstract description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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Abstract
The invention discloses a kind of, and the sweeping robot angle maximum likelihood estimation method based on gyroscope includes: to establish the continuous kinematics model of Two-wheeled sweeping robot and the kinematics model of discretization in global coordinate system { O, X, Y }；Based on the state of system last moment, using the process model of system, the state at forecasting system current k moment；Filtering mean square error is updated using process noise Q, obtains the prediction mean square error P at current timek|k‑1；Based on prediction mean square error Pk|k‑1With measurement noise R, kalman gain K is calculatedk；In conjunction with predicted value θk|k‑1With the measured value Z of gyroscopek, obtain the angle maximum likelihood estimate θ at current timek|k；The filtering mean square error for updating current time, obtains optimal estimation value Pk|k；When system enters subsequent time, step 2 is repeated to step 6, realizes autoregressive operation.This method has merged driving wheel rotary encoder data and gyro data, and the optimal estimation value of current time angle is calculated in each sampling time point, has good inhibiting effect to the random error of system.
Description
Technical Field
The invention discloses a gyroscope-based optimal angle estimation method for a sweeping robot, relates to the technical field of automatic control, and particularly relates to a control estimation method.
Background
The forward movement and the steering of the double-wheel drive sweeping robot are realized by independent direct current motor drive. In the prior art, the determination of the two-wheel moving steering is mostly processed by depending on the measurement data of a gyroscope.
For example, chinese patent application No. CN201610081865 discloses an observation method and a related observation control system for torque of a driving shaft of an electric vehicle, in which a sensor module detects a motor speed and a wheel speed of a driving system; and transmitting the motor rotating speed and wheel speed data detected by the sensor module to a UKF observation module for simulation operation, and outputting a driving shaft torque estimation result. The advantages are that: the observation and estimation of the driving shaft torque can be realized, and the observation method is applied to the driving system control, so that the advanced prejudgment and processing of the driving shaft torque by the automobile controller are realized, and the aim of reliably controlling the driving system of the automobile is fulfilled. However, compared with the automobile, the two-wheel drive sweeping robot is greatly different in both hardware structure and data measurement processing, and the observation method of the torque of the drive shaft of the electric vehicle is not suitable for the two-wheel drive sweeping robot.
For another example, chinese patent application No. CN201010132143 discloses a sensor processing and balance control algorithm for a wheel-type inverted pendulum, which includes a sensor processing algorithm, a balance control algorithm, an overspeed protection algorithm, and an emergency processing algorithm; the sensor processing algorithm comprises an acceleration sensor and gyroscope data fusion algorithm and a filtering algorithm; the data fusion algorithm of the acceleration sensor and the gyroscope is used for obtaining a stable inverted pendulum deflection angle under a dynamic condition by utilizing the low-frequency characteristic of the acceleration sensor and the high-frequency characteristic output by the gyroscope; the balance control algorithm controls the current acceleration and angle of the vehicle body by using the current deflection angle of the inverted pendulum and the deflection angular velocity of the inverted pendulum; the input parameters of the control process are the speed of the vehicle body, the deflection angle of the vehicle body and the velocity of the deflection angle; the output is the acceleration of the vehicle body; the overspeed protection algorithm outputs an angular offset related to the speed when the speed is higher than a certain speed threshold; the offset is added to the angle calculated by the sensor fusion algorithm and is used for decelerating the whole vehicle body, and when the speed is reduced to a certain threshold value, the offset is gradually removed, so that the system is recovered to be normal; the emergency processing algorithm is that under the emergency condition, the system automatically starts the emergency processing algorithm, the speed threshold value of the emergency processing algorithm is near 0, and the control target is to rapidly reduce the speed of the vehicle body so as to ensure the safety of the vehicle-mounted equipment. Although the application refers to a data fusion algorithm of an acceleration sensor and a gyroscope, the calculation and processing mode of the algorithm is mainly used for solving the stability problem of the two-wheel balance car, and the disclosed content is not very helpful for a use scene of a two-wheel drive sweeping robot.
In the process of advancing and steering of the double-wheel driving sweeping robot, because a gyroscope measuring signal can drift, if the angle measuring error is larger by only depending on gyroscope data, the angle measuring error is larger.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the defects of the prior art, the method for optimally estimating the angle of the sweeping robot based on the gyroscope is provided, the method integrates the data of the rotary encoder of the driving wheel and the data of the gyroscope, the optimal estimated value of the angle at the current moment is obtained by calculation at each sampling time point, and the method has a good inhibiting effect on the random error of the system.
The invention adopts the following technical scheme for solving the technical problems:
a method for optimally estimating the angle of a sweeping robot based on a gyroscope is characterized by comprising the following steps:
step one, establishing a continuous kinematics model and a discretization kinematics model of the double-wheel drive sweeping robot in a global coordinate system { O, X, Y };
secondly, predicting the state of the system at the current k moment by using a process model of the system based on the state of the system at the previous moment;
step three, updating the filtering mean square error by using the process noise Q to obtain the predicted mean square error P of the current momentk|k-1；
Step four, based on the prediction mean square error Pk|k-1And measuring noise R, calculating Kalman gain Kk；
Step five, combining the predicted value thetak|k-1And the measured value Z of the gyroscopekObtaining the angle optimal estimated value theta of the current momentk|k；
Step six, updating the filtering mean square error of the current moment to obtain the optimal estimated value Pk|k；
And step seven, when the system enters the next moment, repeating the step two to the step six to realize autoregressive operation.
As a further preferable aspect of the present invention, in the first step, the coordinates of the two-wheel drive sweeping robot are expressed as:
P＝[x y θ]Tthe point P is the center of the two driving wheel shafts, and theta is an included angle between the two driving wheel shafts and the x axis;
the linear velocity of the advancing robot is upsilon, the angular velocity of the steering is omega, and the continuous kinematics model is expressed as follows:
wherein,υLand upsilonRThe linear speeds of the left driving wheel and the right driving wheel are respectively, and L is the distance between the driving wheels;
selecting T as sampling period time, wherein the discretized kinematic model is as follows:
wherein, thetak-1Angle, omega, of the robot at the previous momentk-1Is the steering angular velocity at the previous moment, thetakThe calculated value of the angle at the current moment is obtained;
by reading the number N of pulses of the rotary encoder of the left and right driving wheels in a sampling periodRAnd NLObtaining a new angle state expression:
wherein r is the radius of the driving wheel, N is the number of pulses of one rotation of the encoder, and wkIs process noise;
reading the angle measurement value Z of the current moment according to the gyroscopek：
Zk＝θk+vk (4)
Wherein v iskIs the measurement noise of the gyroscope.
As a further preferred aspect of the invention, the process noise wkAnd the measurement noise v of the gyroscopekAre all Gaussian whiteNoise.
As a further preferable aspect of the present invention, in the second step, the state θ at the current k time isk|k-1The expression of (a) is:
in the formula (5), θk|k-1Is the result of prediction using the previous state, i.e. the predicted value of the angle at the current time, thetak-1|k-1The optimal estimation value of the angle at the last moment is obtained.
As a further preferred embodiment of the present invention, in the third step, the predicted mean square error P of the current time isk|k-1The expression of (a) is:
Pk|k-1＝Pk-1|k-1+Q (6)
in the formula (6), Pk-1|k-1For the optimum mean square error at the last moment, Pk|k-1Is the predicted mean square error at the current time.
As a further preferable embodiment of the present invention, in the fourth step, the kalman gain K iskThe expression of (a) is:
Kk＝Pk|k-1(Pk|k-1+R)-1。 (7)
as a further preferable embodiment of the present invention, in the fifth step, the angle optimal estimation value θ at the current time is obtainedk|kThe expression of (a) is:
θk|k＝θk|k-1+Kk(Zk-θk|k-1)。 (8)
as a further preferable embodiment of the present invention, in the sixth step, the optimal estimated value P is set to be the most optimal estimated valuek|kThe expression of (a) is:
Pk|k＝(1-Kk)Pk|k-1。
compared with the prior art, the invention adopting the technical scheme has the following technical effects: the optimal estimation method for the sweeping robot angle based on the gyroscope integrates the data of the rotary encoder of the driving wheel and the data of the gyroscope, calculates the optimal estimation value of the current time angle at each sampling time point, and has a good inhibition effect on the random error of the system.
Drawings
Fig. 1 is a schematic view of the forward and steering of the two-wheel drive sweeping robot in the present invention.
FIG. 2 is a schematic flow chart of the steps of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The technical scheme of the invention is further explained in detail by combining the attached drawings:
in the invention, the advancing and steering schematic diagram of the two-wheel drive sweeping robot is shown in fig. 1, and a global coordinate system is set
{ O, X, Y }, the robot coordinates may be expressed as P ═ X Y θ]TPoint P is the center of the two drive axles and L is the distance between the drive wheels.
Assuming that the linear velocity of the advancing robot is upsilon and the angular velocity of the steering is omega, the continuous kinematics model can be expressed as:
wherein,υLand upsilonRThe linear velocities of the left and right driving wheels respectively.
Selecting T as sampling period time, wherein the discretized kinematic model is as follows:
wherein, thetak-1Angle, omega, of the robot at the previous momentk-1Is the steering angular velocity at the previous moment, thetakThe angle is calculated at the current moment. By reading the number N of pulses of the rotary encoder of the left and right driving wheels in a sampling periodRAnd NLObtaining a new angle state expression:
wherein r is the radius of the driving wheel, N is the number of pulses of one rotation of the encoder, and wkIs process noise. Simultaneously, reading the angle measurement value Z of the current moment from the gyroscope modulekCan be expressed as
Zk＝θk+vk (4)
Wherein v iskIs the measurement noise of the gyroscope. Suppose wkAnd vkIs Gaussian white noise with covariance of Q and R, respectively, passing KalmanThe filter calculates an optimal angle estimate.
The gyroscope-based optimal angle estimation method for the sweeping robot disclosed by the invention has the specific steps as shown in the schematic diagram of fig. 2, and comprises the following specific steps:
1. predicting the state of the current k moment by using a process model of the system based on the state of the last moment of the system,
in the formula (5), θk|k-1Is the result of prediction using the previous state, i.e. the predicted value of the angle at the current time, thetak-1|k-1The optimal estimation value of the angle at the last moment is obtained.
2. The filtered mean square error is updated with the process noise Q,
Pk|k-1＝Pk-1|k-1+Q (6)
in the formula (6), Pk-1|k-1For the optimum mean square error at the last moment, Pk|k-1Is the predicted mean square error at the current time.
3. Based on the predicted mean square error Pk|k-1And measuring the noise R, calculating the kalman gain,
Kk＝Pk|k-1(Pk|k-1+R)-1 (7)
4. combined predicted value thetak|k-1And the measured value Z of the gyroscopekObtaining the angle optimal estimated value theta of the current momentk|k，
θk|k＝θk|k-1+Kk(Zk-θk|k-1) (8)
5. Updating the filtering mean square error of the current moment to obtain the optimal estimationEvaluating Pk|k，
Pk|k＝(1-Kk)Pk|k-1 (9)
6. And when the system enters the next moment k +1, repeating the steps 1-5 to realize the autoregressive operation.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention. Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A method for optimally estimating the angle of a sweeping robot based on a gyroscope is characterized by comprising the following steps:
step one, establishing a continuous kinematics model and a discretization kinematics model of the double-wheel drive sweeping robot in a global coordinate system { O, X, Y };
secondly, predicting the state of the system at the current k moment by using a process model of the system based on the state of the system at the previous moment;
step three, utilizing process noise QFiltering the mean square error to obtain the predicted mean square error P of the current timek|k-1；
Step four, based on the prediction mean square error Pk|k-1And measuring noise R, calculating Kalman gain Kk；
Step five, combining the predicted value thetak|k-1And the measured value Z of the gyroscopekObtaining the angle optimal estimated value theta of the current momentk|k；
Step six, updating the filtering mean square error of the current moment to obtain the optimal estimated value Pk|k；
And step seven, when the system enters the next moment, repeating the step two to the step six to realize autoregressive operation.
2. The optimal estimation method for the angle of the sweeping robot based on the gyroscope as claimed in claim 1, characterized in that: in the first step, the coordinate of the two-wheel drive sweeping robot is represented as P ═ x y θ]TThe point P is the center of the two driving wheel shafts, and theta is an included angle between the two driving wheel shafts and the x axis;
the linear velocity of the advancing robot is upsilon, the angular velocity of the steering is omega, and the continuous kinematics model is expressed as follows:
wherein,υLand upsilonRThe linear speeds of the left driving wheel and the right driving wheel are respectively, and L is the distance between the driving wheels;
selecting T as sampling period time, wherein the discretized kinematic model is as follows:
wherein, thetak-1Angle, omega, of the robot at the previous momentk-1Is the steering angular velocity at the previous moment, thetakThe calculated value of the angle at the current moment is obtained;
by reading the number N of pulses of the rotary encoder of the left and right driving wheels in a sampling periodRAnd NLObtaining a new angle state expression:
wherein r is the radius of the driving wheel, N is the number of pulses of one rotation of the encoder, and wkIs process noise;
reading the angle measurement value Z of the current moment according to the gyroscopek：
Zk＝θk+vk (4)
Wherein v iskIs the measurement noise of the gyroscope.
3. The optimal estimation method for the angle of the sweeping robot based on the gyroscope as claimed in claim 2, characterized in that: the process noise wkAnd the measurement noise v of the gyroscopekAre all gaussian white noise.
4. The optimal estimation method for the angle of the sweeping robot based on the gyroscope as claimed in claim 2, characterized in that: in the second step, the state theta at the current k momentk|k-1The expression of (a) is:
in the formula (5), θk|k-1Is the result of prediction using the previous state, i.e. the predicted value of the angle at the current time, thetak-1|k-1The optimal estimation value of the angle at the last moment is obtained.
5. The optimal estimation method for the angle of the sweeping robot based on the gyroscope as claimed in claim 2, characterized in that: in the third step, the predicted mean square error P of the current timek|k-1The expression of (a) is:
Pk|k-1＝Pk-1|k-1+Q (6)
in the formula (6), Pk-1|k-1For the optimum mean square error at the last moment, Pk|k-1Is the predicted mean square error at the current time.
6. The optimal estimation method for the angle of the sweeping robot based on the gyroscope as claimed in claim 2, characterized in that: in the fourth step, the Kalman gain KkThe expression of (a) is:
Kk＝Pk|k-1(Pk|k-1+R)-1。 (7)。
7. the optimal estimation method for the angle of the sweeping robot based on the gyroscope as claimed in claim 2, characterized in that: in the fifth step, the angle optimal estimation value theta of the current momentk|kThe expression of (a) is:
θk|k＝θk|k-1+Kk(Zk-θk|k-1)。 (8)。
8. the optimal estimation method for the angle of the sweeping robot based on the gyroscope as claimed in claim 2, characterized in that: in the sixth step, the optimal estimated value Pk|kThe expression of (a) is:
Pk|k＝(1-Kk)Pk|k-1。 (9)。
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| CN113418493A (en) * | 2021-07-23 | 2021-09-21 | 广东工业大学 | Method for auxiliary measurement of angle of servo motor based on gyroscope |
| CN114001656A (en) * | 2021-11-12 | 2022-02-01 | 天津希格玛微电子技术有限公司 | Detection error correction method and device for optical displacement detection device |
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