CN108896049A - A kind of motion positions method in robot chamber - Google Patents
A kind of motion positions method in robot chamber Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention discloses a kind of motion positions methods in robot chamber, include the following steps：1) encoder data synchronous acquisition step, for acquiring the corner cumulative data of original two driving wheel of left and right；2) substitution of left and right sidesing driving wheel data is calculated cumulative movement distance and current real-time attitude angle according to the kinematics model of the differential trolley of two-wheel by encoder data scaling step；3) with the cumulative movement distance and real-time attitude angle obtained in a sampling period, the displacement increment in current sample period is calculated；4) it according to the displacement increment in step 3), carries out adaptive α β filtering algorithm (a kind of stable state under Kalman filtering algorithm) and calculates, obtain the estimated coordinates of the robot displacement of subsequent time.The present invention provides a kind of robot ambulation localization method that can complete adaptive-filtering, is filtered estimation using adaptive α β filter to reduce model error, inhibits filtering divergence, obtains the current robot high quality coordinates of motion.
Description
Technical Field
The invention relates to the field of automatic control, in particular to a robot indoor motion positioning method.
Background
The robot needs to monitor the moving coordinates constantly in the moving process, the robot usually adopts differential double-wheel driving movement, a rotary encoder is installed on a motor shaft, real-time corner data of two motors can be output in real time, the data is collected and input into a kinematic model of the double-wheel differential trolley, path data and attitude angle information of the double-wheel trolley are obtained, the current world coordinates of the trolley can be calculated, and the set of coordinates can carry certain system noise, measurement noise and random noise. In the prior art, no robot indoor movement positioning method exists, which can eliminate various noises and realize the accuracy of indoor positioning in the movement process.
Therefore, those skilled in the art are devoted to developing a robot indoor motion positioning method, which can accurately realize positioning.
Disclosure of Invention
In view of the above-mentioned defects in the prior art, the technical problem to be solved by the present invention is to provide a robot indoor motion positioning method, which can accurately realize indoor positioning.
In order to achieve the aim, the invention provides a robot indoor motion positioning method, which comprises the following steps:
1) a synchronous encoder data acquisition step, which is used for acquiring the original corner accumulated data of the left driving wheel and the right driving wheel;
2) the encoder data conversion step, namely substituting left and right driving wheel data into the calculation of the accumulated movement distance and the current real-time attitude angle according to a kinematic mathematical model of the double-wheel differential trolley;
3) calculating the displacement increment in the current sampling period by using the accumulated motion distance and the real-time attitude angle obtained in the previous sampling period;
4) and 3) performing Kalman filtering calculation or adaptive α and β filtering calculation according to the displacement increment in the step 3) to obtain the estimated coordinates of the robot displacement at the next moment.
Preferably, the method further comprises the following steps:
5) converting and calculating the code disc data in the step 1) to obtain a distance increment delta s and a yaw angle delta theta, and converting through Cartesian coordinates to obtain an observation coordinate;
6) calculating to obtain an observed value;
7) carrying out statistic parameter calculation analysis such as covariance on the observation coordinates in the step 5) and the estimation coordinates in the step 4) to obtain filtering parameters and a gain matrix at the current moment;
8) and 7) combining the result of the step 7) with a state transition equation to calculate and obtain a new filtered coordinate.
Preferably, in the step 5), the observation coordinates of the robot are calculated according to the following formula:
Δxk＝Δsk·sin(θk(k))
Δyk＝Δsk·cos(θk(k))
wherein (Δ x)k，Δyk) Is a machineThe observed coordinates of the person.
Preferably, in the step 4), the predicted displacement of the robot at the next moment is obtained according to the following formula:
wherein,the displacement is predicted for the robot at the next moment.
In step 6), the observed value at the time k +1 can be derived from the filtering result at the previous time by using the following state transition equation:
X(k+1)＝Φ(l(k),r(k))+G(k)W(k)
where Φ is the state transition matrix;
l (k), r (k) are coded disc signals of a left wheel and a right wheel respectively;
g (k) is a constant matrix G (k) ═ T, T2/2]TT is a statistical period;
w (k) is a noise signal;
x (k +1) is the observed value at time k + 1.
Preferably, in the step 7), the αβ filter is used to calculate the filter parameters and the gain matrix.
Preferably, in step 7), the gain matrix is calculated according to the following formula:
where K (K +1) is the prediction gain matrix at time K + 1.
Preferably, in the step 7), the following steps are carried out:
71) judging whether the number of cycles is accumulated to the set number of cycles;
72) if the set number of cycles has not been reached, the filter parameters α, β are calculated according to the following formula:
73) the filter parameters are calculated according to the following formula:
wherein,
σvis the standard deviation of the filtered estimate and measured value errors;
w is Gaussian white noise with zero mean, i.e., 1 variance;
74) the gain matrix is calculated according to the following formula:
preferably, in the step 8), the calculation is performed according to the following formula:
wherein Y (k +1) is an observed value at time (k + 1);
a transfer matrix h (k) ═ 10;
is an estimate of time (k + 1);
x (k +1/k +1) is the result of the filtering at time (k + 1).
The self-adaptive robot walking positioning method has the advantages that the self-adaptive robot walking positioning method can be achieved, firstly, the self-adaptive αβ filtering algorithm is adopted for positioning prediction in the technical scheme, and compared with a common filtering algorithm, the self-adaptive αβ filtering algorithm can show a better tracking positioning effect when a robot target is maneuvered.
In addition, the invention adopts a self-adaptive filtering method in the motion data filtering link, and continuously estimates and corrects inaccurate parameters and noise variance matrixes in the model while carrying out recursive filtering by using measured data, thereby reducing model errors, inhibiting filtering divergence and improving the estimation precision of the current robot coordinate.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Fig. 2 is a flowchart of the statistical error calculation of fig. 1.
Fig. 3 is a flow chart of the adaptive αβ filtered signal processing of fig. 1.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
as shown in fig. 1, a robot indoor movement positioning method includes the following steps:
1) a synchronous encoder data acquisition step, which is used for acquiring the original corner accumulated data of the left driving wheel and the right driving wheel;
2) the encoder data conversion step, namely substituting left and right driving wheel data into the calculation of the accumulated movement distance and the current real-time attitude angle according to a kinematic mathematical model of the double-wheel differential trolley;
3) calculating the displacement increment in the current sampling period by using the accumulated motion distance and the real-time attitude angle obtained in the previous sampling period;
4) and 4) carrying out self-adaptive αβ filtering or Kalman filtering calculation according to the displacement increment in the step 4) to obtain the estimated coordinate of the robot displacement at the next moment.
The double-wheel differential robot has unique motion characteristics, when the two driving wheels rotate at the same speed and in the same direction, the robot can do linear motion at a nearly uniform speed, and more times, the two driving wheels cannot meet the rotation conditions of the same speed and the same direction.
When the robot target moves linearly at a constant speed, the tracking target can be well positioned by adopting a basic filtering and predicting method. In practice, however, the robot target is often maneuvered, and based on the algorithm of the kalman filter, the robot target can achieve more accurate tracking and positioning effects.
Further, the method also comprises the following steps:
5) converting and calculating the code disc data in the step 1) to obtain a distance increment delta s and a yaw angle delta theta, and converting through Cartesian coordinates to obtain an observation coordinate;
5) calculating the current observation coordinate of the code disc data in the step 1) according to an observation equation;
and obtaining an observed value of the robot motion information, and inputting the observed value into an adaptive αβ filter, wherein the filter starts to work after the system is started at the initial moment, and the output result is displacement increment processed by a filtering algorithm.
6) Calculating to obtain an observed value;
and calculating the displacement increment in the current sampling period by using the accumulated motion path and the real-time attitude angle obtained in the last sampling period, wherein the displacement increment is the observed input value of the adaptive αβ filter.
7) Carrying out covariance system on the observation coordinates in the step 5) and the estimation coordinates in the step 4), and the like
Calculating and analyzing parameters to obtain a filtering parameter and a gain matrix at the current moment;
8) and 7) combining the result of the step 7) with a state transition equation to calculate and obtain a new filtered coordinate.
Further, as shown in fig. 2, according to the flow shown in fig. 2, data l (k), r (k) of the left and right code wheels of the robot are obtained synchronously. Then, a distance increment delta s and a yaw angle delta theta (attitude angle) are obtained through a kinematic mathematical model of the chassis, and then converted into a Cartesian coordinate representation form (delta x, delta y), namely an observation coordinate.
Further, in the step 5), the observation coordinates of the robot are calculated according to the following formula:
Δxk＝Δsk·sin(θk(k))
Δyk＝Δsk·cos(θk(k))
wherein (Δ x)k，Δyk) Is an observation seat of a robotThe target is also the displacement increment.
The displacement increments (Δ x, Δ y) carry the system noise, random noise and measurement noise components, and the observation equation is a first order linear equation, and the noise is only linearly transformed. The two-dimensional ground that traveles is not the X-Y level ground of strict meaning, so the barycenter of robot has the vibration displacement on the Z axle in the strict meaning, and this will produce corresponding random noise, and robot actuating system will have certain skidding simultaneously, and this process also can produce random noise. Other noise such as gear friction in the drive system, non-concentricity between two wheels mounting, etc. is system noise. Measurement noise can be smeared in the encoder during acquisition. These three types of noise will accumulate errors in the walking positioning noise of the entire robot system.
Further, the filtering result at the time k is input into a filtering estimation equation, so that an estimation value at the time k +1 is obtainedThe estimation equation in the invention adopts an accelerated motion model of Newton motion mechanics. In the step 4), the estimated coordinates of the robot at the next moment are obtained according to the following formula:
wherein,coordinates are estimated for the robot at the next moment.
During processing of the observed data, a predicted displacement increment is generated, and an estimate is made for the predicted displacement increment and the observed displacement increment. The evaluation process is completed by an algorithm designed by a filter, the filter can calculate the displacement increment of the period of time according to the historical filtering result and the current observation value, and finally the current coordinate data of the robot is updated.
The general flow chart of the robot indoor walking positioning method of the invention is shown in fig. 1, and the motion data of the robot at the last moment is obtained, including the world coordinate (x) at the last momentt,yt) Filtered estimated seating valueFilter estimated coordinates and set historical filtered data for 20 cycles within the sliding window length. After the robot moves within the current cycle, the sensor will capture a new code wheel signal increment. The signal increment is calculated by a kinematic mathematical model of the double-wheel trolley to obtain the path increment delta s of the trolley and the current yaw angle theta (t).
The motion data filtering link in the invention adopts a self-adaptive αβ filter, and continuously estimates and corrects inaccurate parameters and a noise variance matrix in the model while carrying out recursive filtering by using measured data, thereby reducing model errors, inhibiting filtering divergence and improving the estimation precision of the current robot coordinate.
The displacement increment (delta x, delta y) of the robot obtained above enters αβ filter processing, the increment data carries system noise and measurement noise, the αβ filter is a Kalman filter in steady state, the calculation amount of the gain K matrix calculated by the filter is far less than that of the gain K matrix, and the convergence and the robustness of the filter are stronger than those of the Kalman filter for the moving target of a two-dimensional plane.
And calculating the error between the filtering estimation value and the observation data value to perform statistical analysis on the error. The length of the statistical data is determined by the length of the defined sliding window period. The filter sliding window in the present invention is designed to be 20 sample periods. The combination of the sampling frequency of 100Hz is that the data of 0.2 seconds before the history is stored.
In step 6), the observed value at the time k +1 can be derived from the filtering result at the previous time by using the following state transition equation:
X(k+1)＝Φ(l(k),r(k))+G(k)W(k)
where Φ is the state transition matrix;
l (k), r (k) are coded disc signals of a left wheel and a right wheel respectively;
g (k) is a constant matrix G (k) ═ T, T2/2]TT is a statistical period;
w (k) is a noise signal;
x (k +1) is the observed value at time k + 1.
In the step 7), αβ filters are adopted to calculate the filter parameters and the gain matrix.
Further, in the step 7), the following steps are performed:
71) judging whether the number of cycles is accumulated to the set number of cycles; in this embodiment, the historical filtering data of 20 cycles in the length of the sliding window is set as the set number of cycles.
72) If the set number of cycles, i.e. 20 cycles, is not reached, the filter parameters α, β are calculated according to the following formula:
73) the filter parameters are calculated according to the following formula:
wherein,
σvis the standard deviation of the filtered estimate and measured value errors;
w is white Gaussian noise with zero mean, i.e., 1 variance, and it can be seen that the adaptive αβ filter according to the embodiments of the present invention calculates the statistical relationship between the estimated value and the measured value during the filtering process, and then dynamically adjusts the gain matrix K to obtain better dynamic characteristics.
74) In fig. 2, the link 4 calculates a filter gain matrix, and the gain matrix is calculated according to the following formula:
the parameter αβ of the conventional αβ filtering is a fixed value, so the gain matrix K is a constant coefficient matrix, and when the target has mobility, it is difficult to achieve an ideal convergence effect, and the adaptive αβ filter adjusts the parameter αβ according to an error relationship between an estimated value and a measured value to achieve a dynamic convergence.
Prediction gain matrix for time k +1
Fig. 3 shows a flow chart of the signal processing of an adaptive αβ filter used in the present invention, fig. 2 has six steps in total, and elements 1 to 3 are performed as described above.
Further, in fig. 3, after the link 4 is completed, the gain matrix K (K +1) is obtained, and the link 5 calculates according to the following formula in the step 8):
wherein Y (k +1) is an observed value at time (k + 1);
a transfer matrix h (k) ═ 10;
is an estimate of time (k + 1);
x (k +1/k +1) is the result of the filtering at time (k + 1).
After the step 5 in fig. 2 is completed, the whole adaptive filtering is completely performed for one iteration, and the filtered coordinate position (x) is obtainedk+1,yk+1) The signal processing flow of the filter will also go from the (k +1) time to the (k +2) time.
The invention provides a robot indoor motion positioning method, wherein a rotary encoder is arranged on a differential double-wheel drive motor shaft, real-time corner data of two motors can be output in real time, the data is collected and input into a kinematics model of a double-wheel differential trolley, path data and attitude angle information of the double-wheel trolley are obtained, the current world coordinates of the trolley can be calculated, but the coordinates carry certain system noise, measurement noise and random noise, the invention adopts a self-adaptive αβ filter to eliminate the existing noise, and a self-adaptive αβ filter is an extended linear Kalman filter and has good convergence for tracking a high maneuvering target.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (8)
1. A robot indoor motion positioning method is characterized in that: the method comprises the following steps:
1) a synchronous encoder data acquisition step, which is used for acquiring the original corner accumulated data of the left driving wheel and the right driving wheel;
2) the encoder data conversion step, namely substituting left and right driving wheel data into the calculation of the accumulated movement distance and the current real-time attitude angle according to a kinematic mathematical model of the double-wheel differential trolley;
3) calculating the displacement increment in the current sampling period by using the accumulated motion distance and the real-time attitude angle obtained in the previous sampling period;
4) and 3) performing Kalman filtering calculation or adaptive α and β filtering calculation according to the displacement increment in the step 3) to obtain the estimated coordinates of the robot displacement at the next moment.
2. The robot indoor motion positioning method as claimed in claim 1, wherein: further comprising the steps of:
5) converting and calculating the code disc data in the step 1) to obtain a distance increment delta s and a yaw angle delta theta, and converting through Cartesian coordinates to obtain an observation coordinate;
6) calculating to obtain an observed value;
7) carrying out statistic parameter calculation analysis such as covariance on the observation coordinates in the step 5) and the estimation coordinates in the step 4) to obtain filtering parameters and a gain matrix at the current moment;
8) and 7) combining the result of the step 7) with a state transition equation to calculate and obtain a new filtered coordinate.
3. The robot indoor motion positioning method as set forth in claim 2, wherein: in the step 5), the observation coordinates of the robot are calculated according to the following formula:
Δxk＝Δsk·sin(θk(k))
Δyk＝Δsk·cos(θk(k))
wherein (Δ x)k，Δyk) Are the observed coordinates of the robot.
4. The robot indoor motion positioning method as claimed in claim 1, wherein: in the step 4), the predicted displacement of the robot at the next moment is obtained according to the following formula:
wherein,the displacement is predicted for the robot at the next moment.
In step 6), the observed value at the time k +1 can be derived from the filtering result at the previous time by using the following state transition equation:
X(k+1)＝Φ(l(k),r(k))+G(k)W(k)
where Φ is the state transition matrix;
l (k), r (k) are coded disc signals of a left wheel and a right wheel respectively;
g (k) is a constant matrix G (k) ═ T, T2/2]TT is a statistical period;
w (k) is a noise signal;
x (k +1) is the observed value at time k + 1.
5. The robot indoor motion positioning method as claimed in claim 2, wherein in the step 7), αβ filters are used to calculate the filter parameters and the gain matrix.
6. The robot indoor motion positioning method as claimed in claim 2, wherein: in the step 7), the gain matrix is calculated according to the following formula:
where K (K +1) is the prediction gain matrix at time K + 1.
7. The robot indoor motion positioning method as claimed in claim 2, wherein: in the step 7), the method comprises the following steps:
71) judging whether the number of cycles is accumulated to the set number of cycles;
72) if the set number of cycles has not been reached, the filter parameters α, β are calculated according to the following formula:
73) the filter parameters are calculated according to the following formula:
wherein,
σvis the standard deviation of the filtered estimate and measured value errors;
w is Gaussian white noise with zero mean, i.e., 1 variance;
74) the gain matrix is calculated according to the following formula:
8. the robot indoor motion positioning method as claimed in claim 1, wherein: in the step 8), the calculation is carried out according to the following formula:
wherein Y (k +1) is an observed value at time (k + 1);
a transfer matrix h (k) ═ 10;
is an estimate of time (k + 1);
x (k +1/k +1) is the result of the filtering at time (k + 1).
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Application publication date: 20181127 |