CN111324116A - Robot positioning method based on particle filtering - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000001914 filtration Methods 0.000 title claims abstract description 11
- 238000005070 sampling Methods 0.000 claims abstract description 42
- 238000004364 calculation method Methods 0.000 claims description 8
- 239000003550 marker Substances 0.000 claims description 4
- 239000013589 supplement Substances 0.000 claims description 2
- 238000012952 Resampling Methods 0.000 description 8
- 206010033307 Overweight Diseases 0.000 description 4
- 230000035772 mutation Effects 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
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Abstract
The invention discloses a robot positioning method based on particle filtering, which belongs to the field of mobile robots and specifically comprises the following steps: s1: the robot moves to an actual position D according to a control instruction, an ideal position u is taken as a center, and n position coordinates are sampled as a sample set during first sampling; s2: calculating the weight of each position coordinate and the effective particle number of the sample set, if the effective particle number is greater than the effective particle number threshold, turning to S4, otherwise, turning to S3; s3: eliminating samples with weights less than weight threshold in the sample set, wherein the number of the samples is y; re-sampling y position coordinates, and obtaining y new position coordinates by using a cross algorithm; replacing the original y position coordinates with the y new position coordinates to obtain a new sample set; judging whether the maximum sampling frequency is reached, if so, turning to S4; if not, go to S2; s4: calculating the weighted average of all samples in the new sample set; and takes this value as the coordinate of D. The invention improves the positioning precision.
Description
Technical Field
The invention belongs to the field of mobile robots, and particularly relates to a robot positioning method based on particle filtering.
Background
As the mobile robot gradually enters the life of people, the obstacle avoidance system of the mobile robot also becomes a basic requirement for the mobile robot to complete tasks. In a robot formation obstacle avoidance system under actual conditions, the positioning of robots in the system is also a research hotspot. The mobile robot positioning method can be divided into two types according to the range of the perception environment: absolute positioning and relative positioning. The absolute positioning is to set a coordinate system including all robots and obstacles on the basis of knowing the global environment, and all robots in the coordinate system obtain coordinates based on the coordinate system. The absolute positioning method generally has higher precision, but needs to acquire global environment information in advance, cannot provide a global map when a robot avoids obstacles in real time, and needs to use a relative positioning method. When the finger is positioned relatively, the coordinate of the robot relative to the initial position is calculated by measuring the initial position coordinate of the robot and the distance and the posture of each step of action. The method is widely used in multi-robot formation obstacle avoidance without acquiring global information in advance due to relatively loose use conditions. The relative positioning algorithm generates a large error, and the error gradually increases with the increase of the moving distance of the robot, so that the elimination of the error becomes a hot spot in the research of the robot positioning technology. The existing methods for solving the positioning error of the robot mainly include a Kalman filtering method, a maximum expectation method and a particle filtering method, wherein the particle filtering method is most suitable for being used in an obstacle avoidance system of the robot. The problem that the sampling range is too large and the particle diversity is reduced during resampling exists in a classical particle filter algorithm MCL, and an optimized space still exists in the robot positioning problem.
In the existing robot particle filter algorithm MCL, many low weight particles exist in the sample set due to the inaccuracy of the sampling function. The low-weight particles consume a large amount of computing resources in the calculation of the particle matching degree and the weight by the algorithm; when the robot determines the position by weighted average, the influence on the positioning result is very limited. Thus, it is necessary to remove the low weight particles in the sample set to ensure efficient operation of the algorithm, i.e. to use a resampling process.
In the original MCL algorithm, the resampling mode is to directly copy the high-weight particles in the sample set to the low-weight particles, and on the premise of ensuring that the size of the sample set is not changed, the proportion of the high-weight particles is increased, so that the influence of the low-weight particles on the calculation process is reduced. The way of directly copying high-weight particles can greatly reduce the particle diversity of the sample set, and a few particles directly determine the positioning result of the robot, thereby affecting the positioning accuracy, so that the problem of too low particle diversity in the original algorithm is urgently needed to be improved.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a robot positioning method based on particle filtering, aiming at solving the problems that the positioning precision of particles with low weight values is reduced and the like in the prior art.
The technical scheme is as follows: the invention provides a robot positioning method based on particle filtering, which comprises the following steps:
step 1: setting J RFID tags as mark points on the ground; installing a sensor on the robot, wherein the sensor is used for detecting the distance between the robot and each mark point;
step 2: the robot moves from the current position to an actual position D according to a control instruction sent by a worker; the ideal position to which the robot needs to move is u; taking an ideal position u as a center, setting the sampling frequency as t, carrying out first sampling, namely, taking t as 1, sampling n position coordinates, taking the n position coordinates as a sample set, and taking the n position coordinates as sample particles; t is 1, 2, …, max, max is the total number of samples;
the specific sampling method comprises the following steps: randomly generating n integers, and generating n position coordinates corresponding to the n integers according to a sampling formula, wherein the sampling formula is as follows:
wherein xqIs an integer q, X is an integer ofqCorresponding position coordinates, Σ is a range of control sampling, q is 1, 2, …, n;
and step 3: calculating the matching degree of each sample particle according to the distance between each sample particle and each mark point in the sample set and the distance between the robot and each mark point, and normalizing the matching degree to obtain the weight of each sample particle;
and 4, step 4: calculating the number of effective particles of the sample set, judging whether the number of effective particles of the sample set is less than or equal to a preset threshold value of the number of effective particles, and if so, turning to the step 9; otherwise, turning to the step 5:
and 5: deleting the sample particles with the weight value smaller than a preset weight value threshold; the number of the remaining particles is n-y, wherein y is the number of the sample particles with the weight value smaller than the weight value threshold; t + 1;
step 6: sampling for the t time: taking the ideal position u of the robot as a center, and sampling y position coordinates by using a sampling formula so as to supplement a sample particle set;
and 7: randomly selecting one particle from the rest particles in the previous sampling, performing cross calculation based on the randomly selected particle and the y position coordinates sampled in the step 6 to obtain y new position coordinates, and replacing the original y position coordinates with the y new position coordinates; obtaining a new sample set;
and 8: judging whether t is smaller than max, if yes, taking the new sample set as a sample set for the next calculation, and turning to the step 3; otherwise, turning to step 9;
and step 9: calculating a weighted average of the coordinates of all sample particles in the new sample set; and the weighted average is taken as the coordinates of the actual position D of the robot.
Further, the specific method for calculating the matching degree of each sample particle in step 3 is as follows:
wherein M isiMatching degree of the ith sample particle is shown, and j is the jth mark point; dijIs the distance of the ith sample particle from the jth marker point,
measuring robot and second for robot sensorActual distances of j marker points; 1, 2, …, n; j is 1, 2, …, J.
Further, the effective particle number of the sample set is calculated in step 4, and the effective particle number of the sample set is calculated by using the following formula:
further, the specific method for obtaining y new position coordinates in step 7 is as follows: taking the abscissa of the w-th position coordinate in the y-position coordinates as the abscissa of the w-th new position coordinate, and taking the ordinate of the randomly selected particle as the ordinate of the w-th new position coordinate; w is 1, 2, …, y.
Has the advantages that: according to the invention, the diversity of the particles is enriched through the variation step in the cross variation resampling process, so that more particles can participate in robot positioning, and the position information of the high-weight particles is applied in the cross process, so that the probability of low-weight particles is reduced to a certain extent, and the positioning precision is improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a sampling process;
FIG. 3 is a diagram of cross mutation process.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention.
As shown in fig. 1, the present embodiment provides a robot positioning method based on particle filtering; the method comprises the following specific steps:
Step 2: workerThe member sends a control instruction to the robot to enable the robot to move from the current position R1Moving to an ideal position u; due to the error, the robot may not move to the ideal position u, but move to the actual position D; taking an ideal position u as a center, setting the sampling frequency as t, carrying out first sampling, namely, taking t as 1, and sampling n position coordinates; as shown in FIG. 2, in the present embodiment, 4 position coordinates R are collected2、R3、R4、R5Taking the position coordinates as a sample set Reservoir, and taking the n position coordinates as sample particles; t is 1, 2, …, max, max is the total number of samples
The specific sampling method comprises the following steps: randomly generating n integers, and generating n position coordinates corresponding to the n integers according to the following sampling formula:
wherein xqIs an integer q, X is an integer ofqCorresponding position coordinates, Σ is a range of control sampling, q is 1, 2, …, n;
and 3, calculating the position information of all sample particles in the sample set relative to the environmental mark points, namely obtaining the matching degree of each sample particle compared with the robot sensor data (the distance between the robot and each mark point) according to the distance between each sample particle and each mark point in the sample set, and carrying out normalization processing on the matching degree to obtain the Weight Weight of each sample particle.
The specific method for calculating the matching degree of each sample particle comprises the following steps:
wherein M isiMatching degree of the ith sample particle is shown, and j is the jth mark point; dijIs the distance of the ith sample particle from the jth marker point,
measured for a robotic sensorThe actual distance between the robot and the jth mark point; 1, 2, …, n; j is 1, 2, …, J.
And 4, step 4: calculating the number of effective particles of the sample set, and determining whether the number of effective particles of the sample set is less than or equal to a preset threshold of the number of effective particles (in this embodiment, the threshold of the number of effective particles is 1/5), if yes, going to step 8; otherwise, turning to the step 5;
and 5: at this time, the sample set contains many sample particles with lower weights, which occupy a large amount of calculation time in the process of calculating the weights, but occupy an extremely low proportion in the positioning result in the final positioning. In this embodiment, sample particles with weights smaller than a preset weight threshold (in this embodiment, the weight threshold is 1/2n) are deleted from the sample set, and the number of the remaining particles is n-y, where y is the number of sample particles with weights smaller than the weight threshold; t + 1;
step 6: and (4) sampling for the t time, if the result which is closer to the initial sampling is obtained by using the same sampling formula, more samples with low weight values still exist in the samples. The sample set obtained at this time is not much different from the primary sampling result, and the cross-variant resampling is performed as follows.
The invention mainly solves the problem that in the particle filter positioning method, when the initial sampling result is not ideal and needs to be resampled, a cross variation process of a genetic algorithm is introduced, and the diversity of particles during resampling is increased so as to improve the accuracy of the positioning result of the robot. As shown in FIG. 3, assume u is the ideal position of the robot, R3For the remaining high-weighted particles, R, in the last randomly selected round of sampling8Particles that are discarded for lower weight.
The resampling is divided into two steps: and (3) carrying out mutation and crossing, wherein the mutation process is that y position coordinates are sampled by sampling resampling in the t sampling process, so that the sample particle set is supplemented.
The intersection is as follows: the abscissa of the w-th position coordinate of the y-position coordinates of the samples is taken as the abscissa of the y-th new position coordinate (w is 1, 2, …, y), and R is taken as3As the ordinate of the w-th new position coordinate; thereby obtaining y newAnd replacing the original y position coordinates with the y new coordinate positions; a new sample set is obtained.
The specific interleaving process is shown in FIG. 3 as the position coordinate R in the newly sampled y position coordinates6For example, a coordinate R closer to the ideal position is obtained by cross-computing7
R7＝(X6，Y3)
Step 7, judging whether t is smaller than max, if so, taking the new sample set as a sample set for the next calculation, and turning to step 3; otherwise, turning to the step 8;
and 8: calculating a weighted average of the coordinates of all sample particles in the new sample set; and the weighted average is taken as the coordinates of the actual position D of the robot.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.
Claims (4)
1. The robot positioning method based on particle filtering is characterized by comprising the following steps:
step 1: setting J RFID tags as mark points on the ground; installing a sensor on the robot, wherein the sensor is used for detecting the distance between the robot and each mark point;
step 2: the robot moves from the current position to an actual position D according to a control instruction sent by a worker; the ideal position to which the robot needs to move is u; taking an ideal position u as a center, setting the sampling frequency as t, carrying out first sampling, namely, taking t as 1, sampling n position coordinates, taking the n position coordinates as a sample set, and taking the n position coordinates as sample particles; t is 1, 2, …, max, max is the total number of samples;
the specific sampling method comprises the following steps: randomly generating n integers, and generating n position coordinates corresponding to the n integers according to a sampling formula, wherein the sampling formula is as follows:
wherein xqIs an integer q, X is an integer ofqCorresponding position coordinates, Σ is a range of control sampling, q is 1, 2, …, n;
and step 3: calculating the matching degree of each sample particle according to the distance between each sample particle and each mark point in the sample set and the distance between the robot and each mark point, and normalizing the matching degree to obtain the weight of each sample particle;
and 4, step 4: calculating the number of effective particles of the sample set, judging whether the number of effective particles of the sample set is less than or equal to a preset threshold value of the number of effective particles, and if so, turning to the step 9; otherwise, turning to the step 5:
and 5: deleting the sample particles with the weight value smaller than a preset weight value threshold; the number of the remaining particles is n-y, wherein y is the number of the sample particles with the weight value smaller than the weight value threshold; t is 1;
step 6: sampling for the t time: taking the ideal position u of the robot as a center, and sampling y position coordinates by using a sampling formula so as to supplement a sample particle set;
and 7: randomly selecting one particle from the rest particles in the previous sampling, performing cross calculation based on the randomly selected particle and the y position coordinates sampled in the step 6 to obtain y new position coordinates, and replacing the original y position coordinates with the y new position coordinates; obtaining a new sample set;
and 8: judging whether t is smaller than max, if yes, taking the new sample set as a sample set for the next calculation, and turning to the step 3; otherwise, turning to step 9;
and step 9: calculating a weighted average of the coordinates of all sample particles in the new sample set; and the weighted average is taken as the coordinates of the actual position D of the robot.
2. The particle-filter-based robot positioning method according to claim 1, wherein the specific method for calculating the matching degree of each sample particle in the step 3 is as follows:
wherein M isiMatching degree of the ith sample particle is shown, and j is the jth mark point; dijIs the distance of the ith sample particle from the jth marker point,
measuring the actual distance between the robot and the jth mark point for the robot sensor; 1, 2, …, n; j is 1, 2, …, J.
3. The particle-filter-based robot positioning method according to claim 2, wherein the effective particle number of the sample set is calculated in step 4, and the effective particle number of the sample set is calculated by using the following formula:
4. the particle-filter-based robot positioning method according to claim 1, wherein the specific method for obtaining y new position coordinates in step 7 is as follows: taking the abscissa of the w-th position coordinate in the y-position coordinates as the abscissa of the w-th new position coordinate, and taking the ordinate of the randomly selected particle as the ordinate of the w-th new position coordinate; w is 1, 2, …, y.
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| CN113093100A (en) * | 2021-03-09 | 2021-07-09 | 惠州Tcl移动通信有限公司 | Positioning method, intelligent terminal and computer readable storage medium |
| US20220066466A1 (en) * | 2020-09-03 | 2022-03-03 | Honda Motor Co., Ltd. | Self-position estimation method |
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