CN111090281A - Method and device for estimating accurate azimuth of mobile robot based on improved particle filter algorithm - Google Patents
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Abstract
The invention discloses a method and a device for estimating the accurate azimuth of a mobile robot based on an improved particle filter algorithm, wherein the method comprises the following steps: s1, establishing a motion model of the mobile robot according to the system dynamics characteristics; and S2, acquiring the orientation information data of the mobile robot through a sensor, processing the significant errors in the system by adopting an improved particle filter algorithm, and performing corresponding compensation operation on the significant errors to obtain the orientation state parameters of the accurately estimated mobile robot. The method realizes the detection, identification and compensation of the significant errors based on the improved particle filter algorithm, thereby realizing the accurate azimuth estimation of the mobile robot system and effectively improving the accuracy of the azimuth estimation of the mobile robot.
Description
Technical Field
The invention relates to the field of mobile robot positioning, in particular to a method and a device for realizing accurate direction estimation of a mobile robot by utilizing an improved particle filter algorithm to process the problem of significant errors.
Background
With the rapid development of big data and artificial intelligence, the mobile robot obtains rapid development and wide application with the advantages of large working space, strong adaptability and the like. The mobile robot moves in a complex environment, and the environment faced by the mobile robot has the characteristics of complexity, unknown and unstructured. In order to ensure that the robot can effectively complete various tasks in various environments, the robot should have the capabilities of autonomous positioning navigation and path tracking so as to accurately estimate the self orientation. The self-positioning algorithm is one of key technologies for realizing the mobile robot, and the positioning function of the mobile robot is the most basic and important function in various mobile robot systems and is also the key for realizing various functions. Estimating the precise orientation is a basic requirement for the robot to work normally and is also the basis for completing other work.
As the most important state estimation tool, filters have undergone a progression from non-recursive to recursive, frequency-domain to time-domain, non-stationary random processes to state-space models. Today, there are numerous filtering algorithms for state estimation, the most typical being: kalman Filtering (KF), Extended Kalman Filtering (EKF), Unscented Kalman Filtering (UKF), and Particle Filtering (PF). The particle filter algorithm is a filtering method which is most emphasized in the modern nonlinear filtering, has great functions in various fields, and in recent years, scholars at home and abroad combine the particle filter algorithm into state estimation to form state estimation based on particle filtering.
In practical systems, it is considered that the measurement data may be disturbed by non-random events, i.e. significant errors. Significant errors are typically caused by single or multiple phenomena, such as instrument failure, measurement equipment calibration errors, sensor damage, analog-to-digital conversion errors, process defects, and the like. The presence of significant errors introduces inaccurate information that makes it extremely difficult to solve the problem of estimating the position of the mobile robot, and improvements are therefore necessary.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art and provides a method and a device for estimating the accurate orientation of a mobile robot based on an improved particle filter algorithm. According to the method and the device, the problem of significant errors is solved by using an improved particle filter algorithm, and the accurate direction estimation of the mobile robot is realized.
In order to achieve the above object, the present invention provides a method for estimating an accurate orientation of a mobile robot based on an improved particle filter algorithm, which is characterized by comprising:
s1, establishing a motion model of the mobile robot according to the system dynamics characteristics;
and S2, acquiring the orientation information data of the mobile robot through a sensor, processing the significant errors in the system by adopting an improved particle filter algorithm, and performing corresponding compensation operation on the significant errors to obtain the orientation state parameters of the accurately estimated mobile robot.
The further setting is that: the method for improving the significant error in the particle filter algorithm processing system comprises the steps of detecting collected azimuth information data, judging whether significant errors exist or not, and if not, iteratively performing the next filtering estimation; if the significant errors exist, the significant errors are identified to judge which type of significant errors belong to, and after the significant errors are judged, corresponding compensation operation is carried out on the significant errors according to the type of the significant errors.
The further setting is that: and taking the compensated azimuth information data as initial data of next filtering to carry out next state estimation.
The further setting is that: the significant error is set to be an abnormal value, a static error and a drift, wherein the abnormal value shows that a plurality of burst peak values appear in the measured data; the static difference refers to the residual deviation after the completion of the transient process, namely the difference between the stable value of the controlled variable and the given value, the value of the static difference can be positive or negative, the static difference is required to be limited within a certain allowed small range near the given value, and the static difference is represented by the fact that a continuous and relatively stable error value is generated on a measuring device; drift reflects a continuous or incremental change in a measurement characteristic of a measurement instrument over a period of time under specified conditions.
The invention also provides a method and a device for estimating the accurate azimuth of the mobile robot based on the improved particle filter algorithm, which comprises an azimuth signal collecting module, a signal processing module and an upper computer, wherein the azimuth signal collecting module is used for collecting the azimuth information of the mobile robot and inputting the azimuth information into the signal processing module for the improved particle filter algorithm processing, the type of the significant error is identified, after the significant error is distinguished, the significant error is set to be an abnormal value, a static error and a drift, and the corresponding compensation operation is carried out on the significant error according to the type of the significant error, so that the azimuth state parameter of the mobile robot is accurately estimated.
The method realizes the detection, identification and compensation of the significant errors based on the improved particle filter algorithm, thereby realizing the accurate azimuth estimation of the mobile robot system and effectively improving the accuracy of the azimuth estimation of the mobile robot.
The invention realizes the detection and compensation of significant errors based on the improved particle filter algorithm, thereby realizing the accurate azimuth estimation of the mobile robot system and effectively improving the accuracy of the azimuth estimation of the mobile robot.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
FIG. 1 is a flow chart of precise position estimation for a mobile robot;
FIG. 2 contains measured data for outliers;
FIG. 3 contains the measurement data of the static error;
FIG. 4 contains measurement data for drift;
FIG. 5 a model of the dynamics of a nonlinear system of a mobile robot;
FIG. 6 is a dynamic model of the mobile robot in a global coordinate system;
fig. 7 is a modified particle filter algorithm based on significant errors.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the method of this embodiment includes: establishing a motion model of the mobile robot according to the system dynamics characteristics; acquiring azimuth information data of the mobile robot through a sensor; the method verifies the superiority of the improved particle filter algorithm by comparing the running tracks of the mobile robot before and after compensating the significant error.
S1: establishing motion model of mobile robot according to system dynamics characteristics
And setting the position estimation parameters of the mobile robot, and establishing a mathematical model of the nonlinear position estimation system, as shown in fig. 5. When selecting the orientation variable of the mobile robot, selecting the linear velocity v and the steering angular velocity w as measurement data to obtain the position and the posture of the mobile robot: x, y and θ. Due to the presence of noise, there is some error in both measurement and control, i.e., there is noise information in both v and w. Its non-linear description can be expressed as follows:
according to the dynamic model of the mobile robot, a mathematical model of the mobile robot under a global coordinate system is established, so that the construction of a system state space is realized, as shown in fig. 6. Based on the mathematical model, a state space model of the particle filter algorithm is established, which is expressed as follows.
Wherein the state space model comprises 6The state variables are respectively: x, y, theta, vx，vy，vθ. V hereinxAnd vvLinear velocity, v, representing x-axis and y-axis respectivelyθIndicating steering angular velocity, i.e. vθW. The data obtained in this step form a real operation track model of the mobile robot, and is used for comparison with the estimated track obtained after filtering.
S2: method for accurately estimating orientation parameters of mobile robot by adopting improved particle filter algorithm
In step 1, a state space model of the mobile robot, which is an orientation estimation system, is obtained through mathematical modeling. The process of processing the collected data follows, and the specific steps are as follows.
1) Firstly, according to the principle method of particle filtering, the probability p (x) is tested in advancek|xk-1) To obtain a random set of samples, called particles
i represents the ith particle and the weight of the initial particle is set as
2) In the prediction phase, the particles at the k-1 time are used for calculating a prior sample set at the k time according to a state transition equation:
3) in the update phase, based on the measurement data ykAnd calculating the weight of each particle from the prior sample
Wherein
Is the likelihood probability. And then, normalizing the weights so as to unify the distribution characteristics of the samples. Posterior distribution after update:
4) due to p (x)k|y1：k) The method is not the conventional PDF and can not carry out direct sampling, so the importance sampling is introduced to obtain the weight of particle swarm and union. By defining an importance density q (x)k|y1：k) Then the joint weight is expressed as:
5) using the state transition probability function as the proposed distribution, normalizing the weights:
6) in the iterative process, due to the particle degradation problem, the covariance of the importance weights may increase, which may adversely affect the accuracy of the state estimation. Therefore, resampling is introduced, and the parent particle with large weight is introduced
The particles are copied according to the weight size to be used as child particles, and the parent particles with small weight are discarded. And setting effective particle number (Neff) to measure the degradation degree of the particle weight:
after resampling, the posterior estimate of the daughter particles is expressed as:
7) computing state estimation vectors
And corrected measured value
8) Method for detecting significant errors in corrected measured valuesThe residual error size is calculated, and the representation method of the residual error is as follows:
the significant errors mainly comprise three types of abnormal values, static errors and drifts. Where outliers appear as several burst peaks in the measurement data, as shown in figure 2. The static error is the residual deviation after the completion of the transient process, namely the difference between the stable value of the controlled variable and the given value, the value of which can be positive or negative, and is an important index for indicating the accuracy. The static error requirement of the controlled variable in production is limited to some allowed small range around the set point. This appears to produce a persistent and relatively stable error value on the measurement device, as shown in fig. 3. Drift reflects the ability of a measuring instrument to measure a characteristic, under specified conditions, continuously or incrementally over a period of time, to maintain its constant measurement characteristic over a period of time. Drift is often caused by external factors such as pressure, temperature, humidity, etc., or by instability in the performance of the instrument itself. It is difficult to correct if measurement errors drift. Measurement errors that contain drift are much more complex than the other two types of errors, as shown in fig. 4.
Three types of detection of significant error are described below:
① if an outlier occurs at k0In the mth measurement of a step, its observation function can then represent:
wherein,
represents k0The mth of the step measures the magnitude of the outlier.
Since outliers occur mainly as independent and occasional peaks, outliers at one time are often not correlated to others. Implementing outliers using a distance metric based on the measured residual vector r and the response time point kAnd (6) detecting. E.g. at kcThe m-th measurement data at the moment contains significant errors, and the measurement residual point is
It and all other measurement residual error points
Minimum distance D ofminCan be expressed as:
because the static and drift are representational in the form of many consecutive data points, and the outliers are composed of isolated burst peaks, the D of the static and driftminThe value will be significantly lower than the measured value containing an outlier. When no abnormal value occurred in the measured values, all DminPoint and point
Should exhibit a substantially random distribution, in order to test this hypothesis, the following hypothesis in the test program should satisfy the gaussian distribution.
Detection statistic Dmin＜Zα/2Then receive H0I.e. ym，kAre not considered to be outliers. Otherwise, satisfy alternative hypothesis H1When y ism，kConsidered an outlier, the outlier can be expressed as:
② in the case of the occurrence of the mth measurement with a static error, the observation function can be expressed as:
wherein, BmThe static error of the mth measurement value is shown.
The static error appears to produce a persistent and relatively stable error value on the measuring device, here using a residual time series r of measured values mm，1，rm，2，…，rm，kTo estimate the measurement error including the static error. Specifically, a moving time window spanning W is used to calculate data point rm，k-W+1， rm，k-W+2，…，rm，kThe mean and variance of (a) are as follows:
mean value:
variance:
due to interference wkObeying to a white noise sequence, variance S2The F-distribution will be obeyed so that appropriate thresholds can be selected to identify which measurements are currently most correlated with both significant errors, the static and the drift. S2The variance of (c) can be obtained by the following hypothesis test:
as can be seen from the characteristics of the static error and the drift error, the systematic static error will generate a stable persistent error value, so that the variance of the latest W data points of the static error will be much smaller than those data points where the drift occurs. For statistic S2The single-sided hypothesis test of (1) is calculated based on the F distribution. When variance S2If the measured value is less than a predefined threshold epsilon, the data point corresponding to the mth measured value is determined as the static error, otherwise, the data point is classified as the drift. The magnitude of the mth measurement containing the static error can be estimated by the following equation:
③ in the case of an mth measurement value drift, the measurement function can be expressed as:
wherein D ism(k) Is a function describing the variation of the drift of the measurement error, which may be linear, non-linear or even periodic. It is assumed here that the function is continuous and locally linearizable.
In describing the drift function Dm(k) Linear regression based on the calculated residuals is used to analyze the trend and then the slope and intercept after fitting are used to estimate the variance. The magnitude of the mth measurement containing the drift is calculated as follows:
Cm，k＝Dm(k)≈am，kk+bm，k
9) after the significant error has been detected and its magnitude has been determined, the significant error should be eliminated, i.e. compensation of the measured values is achieved. Compensated measured value ykCan be expressed as:
y′k＝yk-Cm，k
updating the corresponding weights is as follows:
the updated weight value is used in the resampling stage of the particle filter, and the state variable estimation value is obtained through deduction
And corrected measured value
Using corrected measured value
Update the obtained measurementAnd measuring residual error information. Due to Cm，kIs estimated from the measurement residual time series information, so the updated measurement residual can be used to improve the measurement compensation of the subsequent quantity.
After considering the significant error problem, the principle of the improved particle filtering algorithm based on significant errors is summarized as shown in fig. 7.
And step 3: and comparing the real running track of the mobile robot with the estimated running tracks under the conditions of the compensated significant errors and the uncompensated significant errors after filtering. In order to achieve accurate state estimation, significant errors should be detected in the state estimation based on particle filtering, since significant errors may adversely affect the state estimation. A state estimation system for a mobile robot based on improved particle filtering. It comprises three parts: detecting measurements, identifying significant errors, and compensating measurements.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, in programmable memory or on a data carrier such as an optical or electronic signal carrier.
While the invention has been described with reference to several particular embodiments, it is to be understood that the invention is not limited to the specific embodiments disclosed. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (7)
1. A method for estimating the accurate orientation of a mobile robot based on an improved particle filter algorithm is characterized by comprising the following steps:
s1, establishing a motion model of the mobile robot according to the system dynamics characteristics;
and S2, acquiring the orientation information data of the mobile robot through a sensor, processing the significant errors in the system by adopting an improved particle filter algorithm, and performing corresponding compensation operation on the significant errors to obtain the orientation state parameters of the accurately estimated mobile robot.
2. The method for estimating the precise orientation of the mobile robot based on the improved particle filter algorithm as claimed in claim 1, wherein: the method for improving the significant error in the particle filter algorithm processing system comprises the steps of detecting collected azimuth information data, judging whether significant errors exist or not, and if not, iteratively performing the next filtering estimation; if the significant errors exist, the significant errors are identified to judge which type of significant errors belong to, and after the significant errors are judged, corresponding compensation operation is carried out on the significant errors according to the type of the significant errors.
3. The method for estimating the precise orientation of the mobile robot based on the improved particle filter algorithm as claimed in claim 2, wherein: and taking the compensated azimuth information data as initial data of next filtering to carry out next state estimation.
4. The method for estimating the precise orientation of the mobile robot based on the improved particle filter algorithm as claimed in claim 1, wherein the significant error is set to be an abnormal value, a static error and a drift, wherein the abnormal value is represented by a plurality of burst peaks appearing in the measured data; the static difference refers to the residual deviation after the completion of the transient process, namely the difference between the stable value of the controlled variable and the given value, the value of the static difference can be positive or negative, the static difference is required to be limited within a certain allowed small range near the given value, and the static difference is represented by the fact that a continuous and relatively stable error value is generated on a measuring device; drift reflects a continuous or incremental change in a measurement characteristic of a measurement instrument over a period of time under specified conditions.
5. The method for estimating the precise orientation of a mobile robot based on an improved particle filtering algorithm according to claim 1, wherein: the step S1 specifically includes:
setting the azimuth estimation parameters of the mobile robot, establishing a mathematical model of a nonlinear azimuth estimation system, and selecting linear velocity v and steering angular velocity w as measurement data when selecting the azimuth variable of the mobile robot to obtain the position and the attitude of the mobile robot: x, y and θ, the non-linear description of which can be expressed as follows:
according to the dynamic model of the mobile robot, a mathematical model of the mobile robot under a global coordinate system is established, and according to the mathematical model, a state space model of a particle filter algorithm is established, which is expressed as follows.
Wherein the state space model comprises 6 state variables, which are respectively: x, y, theta, vx，vy，vθV herein isxAnd vyLinear velocities, v, representing the x-axis and y-axis, respectivelyθIndicating steering angular velocity, i.e. vθ＝w。
6. The method for estimating the precise orientation of a mobile robot based on an improved particle filtering algorithm of claim 5, wherein: the step S2 specifically includes:
1) first according to the principle of particle filteringMethod of processing from a prior probability p (x)k|xk-1) To obtain a random set of samples, called particles
i represents the ith particle and the weight of the initial particle is set as
2) In the prediction phase, the particles at the k-1 time are used for calculating a prior sample set at the k time according to a state transition equation:
3) in the update phase, based on the measurement data ykAnd calculating the weight of each particle from the prior sample
Wherein
And (3) carrying out normalization processing on the weights so as to unify the distribution characteristics of the samples, and updating the posterior distribution:
4) by defining an importance density q (x)k|y1：k) Then the joint weight is expressed as:
5) using the state transition probability function as the proposed distribution, normalizing the weights:
6) introducing resampling to obtain high-weight parent particles
Copying according to the weight to be used as a child particle, discarding a parent particle with small weight, and setting the number of effective particles (Neff) to measure the degradation degree of the weight of the particle:
after resampling, the posterior estimate of the daughter particles is expressed as:
7) computing state estimation vectors
And corrected measured value
8) The method for detecting the significant error of the corrected measured value is to calculate the residual error, and the representation method of the residual error is as follows:
9) after the significant error has been detected and has been dimensioned, the significant error is to be eliminated, i.e. a compensation of the measured values is effected, the compensated measured values y'kCan be expressed as:
y′k＝yk-Cm，k
updating the corresponding weights is as follows:
the updated weight value is used in the resampling stage of the particle filter, and the state variable estimation value is obtained through deduction
And corrected measured value
Using corrected measured value
The measurement residual information is obtained by updating because of Cm，kIs estimated from the measurement residual time series information, so the updated measurement residual can be used to improve the measurement compensation of the subsequent quantity.
7. A device for estimating the precise azimuth of a mobile robot based on an improved particle filter algorithm is characterized in that the device adopts the method of any one of claims 1 to 6 to estimate the precise azimuth of the mobile robot, and comprises an azimuth signal collecting module, a signal processing module and an upper computer, wherein azimuth information of the mobile robot is collected through the azimuth signal collecting module and is input into the signal processing module to be processed through the improved particle filter algorithm, the type of a significant error existing in the mobile robot is identified, after the significant error is distinguished, the significant error is set to be an abnormal value, a static error and a drift, and the corresponding compensation operation is carried out on the significant error according to the type of the significant error, so that the azimuth state parameter of the precisely estimated mobile robot is obtained.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023024264A1 (en) * | 2021-08-23 | 2023-03-02 | 五邑大学 | Trajectory filtering method and apparatus based on numerical control machining system, and electronic device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050251328A1 (en) * | 2004-04-05 | 2005-11-10 | Merwe Rudolph V D | Navigation system applications of sigma-point Kalman filters for nonlinear estimation and sensor fusion |
| US20080119961A1 (en) * | 2006-11-16 | 2008-05-22 | Samsung Electronics Co., Ltd. | Methods, apparatus, and medium for estimating pose of mobile robot using particle filter |
| CN107246873A (en) * | 2017-07-03 | 2017-10-13 | 哈尔滨工程大学 | A kind of method of the mobile robot simultaneous localization and mapping based on improved particle filter |
| CN108318038A (en) * | 2018-01-26 | 2018-07-24 | 南京航空航天大学 | A kind of quaternary number Gaussian particle filtering pose of mobile robot calculation method |
| CN109459033A (en) * | 2018-12-21 | 2019-03-12 | 哈尔滨工程大学 | A kind of robot of the Multiple fading factor positions without mark Fast synchronization and builds drawing method |
| US20190129044A1 (en) * | 2016-07-19 | 2019-05-02 | Southeast University | Cubature Kalman Filtering Method Suitable for High-dimensional GNSS/INS Deep Coupling |
-
2019
- 2019-11-27 CN CN201911184272.0A patent/CN111090281B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050251328A1 (en) * | 2004-04-05 | 2005-11-10 | Merwe Rudolph V D | Navigation system applications of sigma-point Kalman filters for nonlinear estimation and sensor fusion |
| US20080119961A1 (en) * | 2006-11-16 | 2008-05-22 | Samsung Electronics Co., Ltd. | Methods, apparatus, and medium for estimating pose of mobile robot using particle filter |
| US20190129044A1 (en) * | 2016-07-19 | 2019-05-02 | Southeast University | Cubature Kalman Filtering Method Suitable for High-dimensional GNSS/INS Deep Coupling |
| CN107246873A (en) * | 2017-07-03 | 2017-10-13 | 哈尔滨工程大学 | A kind of method of the mobile robot simultaneous localization and mapping based on improved particle filter |
| CN108318038A (en) * | 2018-01-26 | 2018-07-24 | 南京航空航天大学 | A kind of quaternary number Gaussian particle filtering pose of mobile robot calculation method |
| CN109459033A (en) * | 2018-12-21 | 2019-03-12 | 哈尔滨工程大学 | A kind of robot of the Multiple fading factor positions without mark Fast synchronization and builds drawing method |
Non-Patent Citations (2)
| Title |
|---|
| 涂刚毅;金世俊;祝雪芬;宋爱国;: "基于粒子滤波的移动机器人SLAM算法" * |
| 陈杨钟;刘士荣;俞金寿;: "基于非线性滤波的移动机器人位姿估计" * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023024264A1 (en) * | 2021-08-23 | 2023-03-02 | 五邑大学 | Trajectory filtering method and apparatus based on numerical control machining system, and electronic device |
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