CN110602651A - Positioning method based on WIFI position fingerprint and positioning system of robot - Google Patents
Positioning method based on WIFI position fingerprint and positioning system of robot Download PDFInfo
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Abstract
The invention discloses a positioning method based on WIFI position fingerprints and a positioning system of a robot, wherein the positioning method based on the WIFI position fingerprints comprises the following steps: acquiring WIFI position fingerprint data of an object to be positioned at each position of a region to be detected so as to establish a WIFI position fingerprint database, and classifying the data of the WIFI position fingerprint database by adopting a K-means clustering and dividing method so as to divide the data into K classes; the RSSI value of each wireless access point received by the object to be positioned at the current position is obtained, the RSSI values of some wireless access points with poor signal strength are removed, Euclidean distances between sampling data formed by the rest RSSI values and K classes are calculated, and a weighted K nearest neighbor method is adopted to carry out matching positioning in the class with the minimum Euclidean distance value, so that the positioning result of the object to be positioned is obtained. The positioning method based on the WIFI position fingerprint and the positioning system of the robot can improve the operation efficiency and the positioning accuracy.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a positioning method based on WIFI location fingerprints and a positioning system of a robot.
Background
Wireless location, as the name implies, utilizes various types of received signals to determine the geographic location of a locating terminal.
GPS (global positioning system) and Beidou navigation system in China have already reached very high-precision positioning in wide areas. In an indoor scene, some positioning methods such as infrared positioning, ultrasonic positioning, bluetooth positioning, positioning based on WIFI fingerprint information and the like exist, and the methods have advantages and disadvantages. At present, with the rapid development of the internet and mobile communication, a large number of WIFI nodes are arranged on a plurality of occasions, and the mobile terminal is generally provided with a WIFI module, so that the research based on the WIFI fingerprint information positioning method is well developed.
The WIFI-based fingerprint positioning technology mainly utilizes correlation characteristics of space information and RSSI (received signal strength indicator) of wireless signals to mutually match WIFI wireless information and geographical position information collected from a position to be detected. In an actual positioning environment, RSSI values of n wireless access points received by a point to be measured form a group of n-dimensional vectors and a one-by-one mapping relation with two-dimensional geographic positions, different geographic positions corresponding to different n-dimensional RSSI vectors are collected to form a fingerprint database, and each group of data in the database is called as a position fingerprint. And finally, uploading a group of RSSI values measured at the moment to a positioning server according to the change of the RSSI signal strength of the acquired wireless access point, matching the RSSI values with the position fingerprint of the fingerprint database, and selecting the geographical position corresponding to the position fingerprint with the best similarity as an estimated position.
The current positioning algorithm based on WIFI fingerprint comprises two stages, namely an off-line training stage and a real-time positioning stage. The off-line training stage mainly works in that a worker selects the positions of a plurality of reference points in an area to be tested, simultaneously acquires a plurality of signal strength values from different wireless access points at each reference point, and then forms a group of associated ternary data groups by the RSSI value, the MAC address and the geographic position information of the reference points of each wireless access point received by each reference point and stores the ternary data groups in a database, wherein each group of data in the database is a position fingerprint. In the real-time positioning stage, a user collects WIFI information of all wireless access points in a positioning area through terminal equipment, MAC addresses and RSSI values of the wireless access points form a binary group to be used as input content of a fingerprint positioning algorithm, the most similar items are found out through one-by-one comparison, and then geographical position coordinates corresponding to one or more groups of position fingerprints which are closest are used for carrying out related calculation, so that the position of a point to be measured is estimated.
The inventor researches the current positioning algorithm based on WIFI fingerprint and finds that the positioning algorithm has the following problems: in the online positioning stage, when the fingerprint library is small, matching can be performed quickly by comparing one by one, but when the data volume of the fingerprint library is large, a large amount of time is consumed by the operation, so that the positioning efficiency is influenced, and a large amount of repeated operation is generated on the positioning server. Therefore, it is a very great challenge to improve the fingerprint matching algorithm, improve the positioning real-time performance of the positioning system, and reduce the power consumption of the server under the condition of ensuring the positioning accuracy.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention aims to provide a positioning method based on WIFI position fingerprints and a positioning system of a robot, which can improve the operation efficiency, provide the positioning real-time performance of the positioning system and reduce the power consumption of a server.
In order to achieve the above object, the present invention provides a positioning method based on WIFI position fingerprint, where an object to be positioned is provided with a WIFI module, the object to be positioned establishes communication with a plurality of wireless access points through the WIFI module, and the WIFI module can detect a received RSSI value of each wireless access point, and the positioning method based on WIFI position fingerprint includes:
acquiring WIFI position fingerprint data of each position of the object to be positioned in the area to be detected and storing the WIFI position fingerprint data into a pre-established WIFI position fingerprint database;
classifying the data of the WIFI position fingerprint database by adopting a K-means clustering division method so as to divide the data into K classes;
and obtaining the RSSI value of each wireless access point received by the object to be positioned at the current position, removing the RSSI value of the wireless access point with the signal strength smaller than a preset threshold value from the RSSI value, calculating Euclidean distances between sampling data formed by the rest of RSSI values and the K classes, and performing matching positioning by adopting a weighted K neighbor method in the class with the minimum Euclidean distance value to obtain a positioning result of the object to be positioned.
In an embodiment of the present invention, acquiring WIFI position fingerprint data of the object to be located at a certain position includes:
sampling the RSSI value of each wireless access point received by the object to be positioned at the position;
screening out a part of wireless access points by combining the sampling data and taking the stability and the signal quality of the wireless access points as selection factors;
and performing Gaussian filtering on the selected RSSI value array of each wireless access point of the part, averaging the filtered RSSI value array of each wireless access point, and storing a set formed by the average values as WIFI position fingerprint data of the position.
In an embodiment of the present invention, the screening out a part of the wireless access points by combining the sampled data and taking stability and signal quality of the wireless access points as selection factors includes: calculating the occurrence frequency of the sampling data of each wireless access point in the sampling data, arranging the frequency values in a descending order, and forming a first wireless access point set by Q wireless access points corresponding to the former Q frequency values; calculating the average value of the sampling data of each wireless access point in the sampling data, arranging the average values in a descending order, and forming a second wireless access point set by Q wireless access points corresponding to the former Q average values; calculating the standard deviation of the sampling data of each wireless access point in the sampling data, arranging the standard deviation values in an ascending order, and forming a third wireless access point set by the Q wireless access points corresponding to the previous Q standard deviation values; and solving an intersection of the first wireless access point set, the second wireless access point set and the third wireless access point set, and selecting the wireless access point with better part of stability and signal quality in the intersection.
The present invention also provides a positioning system of a robot, comprising: a robot, a robot controller, and a server. The robot is provided with a WIFI module, the robot is connected to a plurality of wireless access points through the WIFI module, and the WIFI module can detect the received RSSI value of each wireless access point. The robot controller is communicated with the wireless access points and is used for receiving the RSSI value of each wireless access point detected by the WIFI module. A server in communication with the robot controller, the server comprising: the robot positioning system comprises a WIFI position fingerprint database establishing module, a clustering module and a positioning module, wherein the WIFI position fingerprint database establishing module is used for acquiring WIFI position fingerprint data of each position of the robot in a region to be detected and storing the WIFI position fingerprint data into a pre-established WIFI position fingerprint database; the clustering module is coupled with the WIFI position fingerprint database establishing module and is used for classifying the data of the WIFI position fingerprint database by adopting a K-means clustering and dividing method so as to divide the data into K classes; the positioning module is coupled with the clustering module and used for acquiring RSSI data of each wireless access point detected by the WIFI module at the current position of the robot when the robot is positioned, removing RSSI values of the wireless access points of which the signal intensity is smaller than a preset threshold value from the RSSI data, calculating Euclidean distances between sampling data formed by the rest RSSI values and the K classes, and performing matching positioning by adopting a weighted K neighbor method in the class with the minimum Euclidean distance value to acquire a positioning result of the object to be positioned.
In an embodiment of the present invention, the WIFI location fingerprint database establishing module includes: the wireless access point comprises a sampling module, a wireless access point selecting module and a WIFI position fingerprint data generating module.
The sampling module is used for sampling the RSSI value of each wireless access point received by the object to be positioned at a certain position;
the wireless access point selection module is coupled with the sampling module and is used for screening out a part of wireless access points by combining the sampling data of the sampling module and taking the stability and the signal quality of the wireless access points as selection factors;
and the WIFI position fingerprint data generation module is coupled with the wireless access point selection module and is used for performing Gaussian filtering on the RSSI value array of each wireless access point selected by the wireless access point selection module, averaging the filtered RSSI value array of each wireless access point, and storing a set formed by the average values as the WIFI position fingerprint data of the position.
In an embodiment of the present invention, the wireless access point selecting module includes: the wireless access system comprises a first wireless access point selection module, a second wireless access point selection module, a third wireless access point selection module and an intersection acquisition module. The first wireless access point selection module is used for calculating the occurrence frequency of the sampling data of each wireless access point in the sampling data of the sampling module, arranging the frequency values in a descending order, and forming a first wireless access point set by the Q wireless access points corresponding to the former Q frequency values. The second wireless access point selection module is used for calculating the average value of the sampling data of each wireless access point in the sampling data, arranging the average values in a descending order, and forming a second wireless access point set by the Q wireless access points corresponding to the former Q average values. And the third wireless access point selection module is used for calculating the standard deviation of the sampling data of each wireless access point in the sampling data, arranging the standard deviation values in an ascending order, and forming a third wireless access point set by the Q wireless access points corresponding to the previous Q standard deviation values. The intersection solving module is coupled with the first wireless access point selecting module, the second wireless access point selecting module and the third wireless access point selecting module, and is configured to solve an intersection for the first wireless access point set, the second wireless access point set and the third wireless access point set, and select a wireless access point with a portion of better stability and signal quality from the intersection.
In an embodiment of the present invention, the server further includes a positioning correction module. The positioning correction module is coupled with the positioning module and the robot controller, and the robot controller is further used for acquiring the rotating speed of the robot motor; the positioning correction module is used for calculating a first distance between the position of the previous positioning and the position of the current positioning; the robot positioning system is also used for calculating a second distance moved by the robot in the process from the previous positioning to the current positioning according to the rotating speed of the motor of the robot; and the positioning device is also used for comparing the first distance with the second distance, if the difference between the first distance and the second distance is greater than a threshold value, judging that the current positioning result is incorrect, sending a message to the robot controller for re-detecting data, and otherwise, judging that the current positioning result is correct and sending the positioning result to the robot controller.
The invention also provides a server for positioning an object based on the WIFI position fingerprint, which comprises:
the WIFI position fingerprint database establishing module is used for acquiring WIFI position fingerprint data of each position of an object to be positioned in the area to be detected and storing the WIFI position fingerprint data into a pre-established WIFI position fingerprint database;
the clustering module is coupled with the WIFI position fingerprint database establishing module and is used for classifying the data of the WIFI position fingerprint database by adopting a K-means clustering and dividing method so as to divide the data into K classes;
and the positioning module is coupled with the clustering module and used for acquiring RSSI data of each wireless access point at the current position of the object to be positioned when the object to be positioned is positioned, removing the RSSI value of the wireless access point with the signal strength smaller than a threshold value from the RSSI data, calculating Euclidean distances between sampling data formed by each residual RSSI value and the K classes, and performing matching positioning by adopting a weighted K nearest neighbor method in the class with the minimum Euclidean distance value so as to acquire a positioning result of the object to be positioned.
In an embodiment of the present invention, the WIFI location fingerprint database establishing module includes:
the sampling module is used for sampling the RSSI value of each wireless access point received by the object to be positioned at a certain position;
the wireless access point selection module is coupled with the sampling module and is used for screening out a part of wireless access points by combining the sampling data of the sampling module and taking the stability and the signal quality of the wireless access points as selection factors;
and the WIFI position fingerprint data generation module is coupled with the wireless access point selection module and is used for performing Gaussian filtering on the RSSI value array of each wireless access point selected by the wireless access point selection module, averaging the filtered RSSI value array of each wireless access point, and storing a set formed by the average values as the WIFI position fingerprint data of the position.
In an embodiment of the present invention, the wireless access point selecting module includes:
the first wireless access point selection module is used for calculating the occurrence frequency of the sampling data of each wireless access point in the sampling data of the sampling module, arranging the frequency values in a descending order, and forming a first wireless access point set by the Q wireless access points corresponding to the former Q frequency values;
the second wireless access point selection module is used for calculating the average value of the sampling data of each wireless access point in the sampling data, arranging the average values in a descending order, and forming a second wireless access point set by the Q wireless access points corresponding to the former Q average values;
the third wireless access point selection module is used for calculating the standard deviation of the sampling data of each wireless access point in the sampling data, arranging the standard deviation values in an ascending order, and forming a third wireless access point set by the Q wireless access points corresponding to the previous Q standard deviation values;
and the intersection solving module is coupled with the first wireless access point selecting module, the second wireless access point selecting module and the third wireless access point selecting module, and is used for solving an intersection of the first wireless access point set, the second wireless access point set and the third wireless access point set and selecting the part of wireless access points in the intersection.
Compared with the prior art, according to the positioning method based on the WIFI position fingerprint, the positioning system of the robot and the server, the fingerprint data are classified by using the K-means clustering algorithm, the data matching speed can be effectively accelerated in the positioning stage, the operation efficiency can be effectively improved, the power consumption of the server can be reduced, and the positioning real-time performance can be improved.
Drawings
Fig. 1 is a block diagram of steps of a positioning method based on WIFI location fingerprints according to an embodiment of the present invention;
FIG. 2 is a block diagram of the steps of a K-means clustering partitioning method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a network topology of a positioning system of a robot in accordance with an embodiment of the present invention;
fig. 4 is a structural composition of a positioning system of a robot according to an embodiment of the present invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
In order to overcome the problems in the prior art, the invention provides a positioning method based on WIFI position fingerprints, a positioning system of a robot and a server, wherein in the process of establishing a WIFI position fingerprint database, the received signal information is preprocessed, the preprocessing mainly comprises the selection of a wireless access point and the smooth processing of an RSSI signal, the fingerprint data are classified by using a K-means clustering algorithm, the data matching speed can be effectively accelerated in the positioning stage, the operation efficiency can be effectively improved, the power consumption of the server can be reduced, and the positioning real-time performance and the positioning precision can be improved.
Example 1
Fig. 1 is a flowchart of a positioning method based on WIFI location fingerprints according to an embodiment of the present invention. The object to be positioned is provided with a WIFI module, the object to be positioned is communicated with the wireless access points through the WIFI module, and the WIFI module can detect the received RSSI value of each wireless access point. The positioning method based on the WIFI position fingerprint comprises the following steps: step S1 to step S3.
In step S1, WIFI position fingerprint data of each position of the object to be positioned in the area to be measured is acquired, so as to establish a WIFI position fingerprint database. Specifically, acquiring WIFI position fingerprint data of an object to be located at a certain position includes: sampling the RSSI value of each wireless access point received by the object to be positioned at the position, and sampling for multiple times at the position; selecting a part of wireless access points with better stability and signal quality by combining the sampling data; and Gaussian filtering is carried out on the RSSI value array of each wireless access point of the selected part so as to remove some RSSI values, the average value of the filtered RSSI value array of each wireless access point is obtained, and a set formed by the obtained average values of each wireless access point is used as WIFI position fingerprint data of the position to be stored.
Selecting a part of wireless access points with better stability and signal quality by combining the sampling data comprises calculating the occurrence frequency of the sampling data of each wireless access point in the sampling data, arranging the frequency values in a descending order, and forming a first wireless access point set by K wireless access points corresponding to the first K frequency values; calculating the average value of the sampling data of each wireless access point in the sampling data, arranging the average values in a descending order, and forming a second wireless access point set by K wireless access points corresponding to the first K average values; calculating the standard deviation of the sampling data of each wireless access point in the sampling data, arranging the standard deviation values in an ascending order, and forming a third wireless access point set by K wireless access points corresponding to the first K standard deviation values; intersecting the first set of wireless access points, the second set of wireless access points, and the third set of wireless access points. And selecting a part of wireless access points with better stability and signal quality from the wireless access points in the intersection according to the actual situation.
Specifically, within the area under test, assume that sampling point L receives a set of your n wireless access points, which may be denoted as { AP }1,AP2,…APnThen at that point, M samples are taken, taking into account the frequency of occurrence of each radio access point in the whole sample, as shown in equation 1:
in formula 1, M (AP)i) Representing wireless access points, APs, in a sampleiThe number of samples present, M represents the total number of samples sampled. Then to Fre (AP)i) Performing descending order, and selecting the first Q wireless access points as the selection result, which is expressed as Q1。
And considering the signal strength of the wireless access points in the sample, averaging the M measured signal strengths of each wireless access point, as shown in equation 2:
in equation 2, RSSIiRepresenting the i-th measurement of a certain radio access point.Can represent the signal quality of the wireless access point, and the N wireless access points of the sampling point L are according toSorting in descending order, and selecting the first Q as selection results, which are expressed as Q2。
Meanwhile, the standard deviation is also considered, and the wireless access point AP is sampled for M times at the sampling point L1Receiving M1Group of sample samples with data of { RSSI1,RSSI2,…,RSSIm1And } the standard deviation of the fluctuation of the wireless access point, as shown in equation 3.
In the formula 3, the first and second organic solvents,represents M1Average of the subsampled samples. The standard deviation sigma can reflect the fluctuation range of the wireless access point, the N wireless access points of the sampling point L are arranged in an ascending order according to the standard deviation sigma, and the N wireless access points are selected as wellThe first Q wireless access points are taken as selection results and are denoted as Q3。
For the above-obtained Q1，Q2，Q3And (5) solving the intersection, as shown in the formula 4.
Q＝Q1∩Q2∩Q3 (4)
Usually, the point to be measured can receive RSSI samples of more than 5 wireless access points, so that good positioning accuracy can be achieved, and the Q value can be set to be more than 5 according to the number of the wireless access points in the actual environment to be measured. If the number of the received wireless access points is small, the scheme is selectively adopted according to the actual situation.
For the selected wireless access point, some small probability values are inevitable due to the flow of people and the occlusion of objects during the measurement. In order to improve the accuracy of the final positioning result, the embodiment uses a gaussian filtering model to eliminate the small probability values, performs gaussian filtering on the RSSI value array of each wireless access point of the selected portion to remove some RSSI values, and calculates an average value of the filtered RSSI value array of each wireless access point.
The specific algorithm is as follows: first assume that { RSSI1,RSSI2,…,RSSInRepresents n sample values from a certain radio access point at a sample point, then the RSSI values can be represented by a normal model, as shown in equation 5.
Wherein:
in equation 5, x represents the RSSI signal strength value.
Then take u-3 sigma<The value of RSSI between x ≦ u +3 σ, removing the out-of-range values, and saving it as { RSSI ≦1,RSSI2,…,RSSIn1}。
And finally, calculating the average value of the selected RSSI values according to the formula 8, wherein the average value is the calculated RSSI signal strength value R of the sample point from the wireless access point.
In the formula 8, n1Represents the total number of samples after filtration.
In step S2, a K-means clustering method is used to classify the WIFI location fingerprint database into K classes.
Specifically, as shown in fig. 2, the K-means cluster partitioning method includes steps S20 to S24.
WIFI location fingerprint dataset X ═ { X ═ X1,x2,…,xnAnd the data objects are input, wherein the data objects comprise N data objects, the number of the clustering centers is K, and the threshold value of the clustering criterion is epsilon. The data set divided into K clusters is set as output.
In step S20, K cluster centers M ═ M are arbitrarily selected from the data set X1 (1),m2 (1),…mk (1)}。
In step S21, each of the remaining data x is calculatedi(i-1, 2, …, N-K) euclidean distances to K cluster centers M, denoted D (i, r), N being the number of all wireless access points in the location area, xi(RSSIj) Representing the signal strength value, m, of the jth wireless access point received by the ith data objectr (1)(RSSIj) Indicating the signal strength value of the jth wireless access point received by the r cluster center if the data object xiIf the signal of the jth ap is not received, it is replaced by an infinitesimal value, and the algorithm of D (i, r) uses equation 9.
In step S22, x is addediAssigned to the cluster center whose euclidean distance is the shortest.
The average of all objects of each class is calculated in step S23 and is taken as the new cluster center point, as shown in equation 10.
Wherein in the formula 10, CrIndicating data contained in class j, NrIndicating the number of jth class data.
The above three steps are repeated at step S24, and x is leftiSorting one by one, when the cluster center is not changed any more or mr (p)When the fluctuation of (2) is less than a given threshold, the centers of the K classes are output, and the algorithm is ended.
In step S3, a weighted K-nearest neighbor method is used for matching and positioning. Specifically, the RSSI value of each wireless access point received by the object to be positioned at the current position is obtained, the RSSI values of some wireless access points with poor signal strength are removed, Euclidean distances between sampling data formed by the residual RSSI values and K classes are calculated, and a weighted K nearest neighbor method is adopted to carry out matching positioning in the class with the minimum Euclidean distance value, so that the positioning result of the object to be positioned is obtained.
Example 2
Based on the same inventive concept, the invention also provides a positioning system of the robot, which is explained below.
Fig. 3 is a network topology diagram of the positioning system of the robot. Fig. 4 is a structural component of a positioning system of a robot according to an embodiment. It includes: robot 1, robot controller 2, server 3.
The robot 1 has a WIFI module 10, the robot 1 accesses a plurality of wireless access points through the WIFI module 10, and the WIFI module 10 can detect the received RSSI value of each wireless access point. The robot 1 further includes a camera 11, a single chip microcomputer 12, a motor 13, and the like. The robot controller 2 communicates with a plurality of wireless access points, and is configured to receive the RSSI value of each wireless access point detected by the WIFI module 10. Optionally, an OpenWrt router 10a is arranged in the WIFI module 10 of the robot 1, and data interaction may be performed between the OpenWrt router 10a and the robot controller 2, for example, when video service software is run on the OpenWrt router 10a, an image acquired by the camera 11 of the robot 1 may be encoded, and a video from the robot 1 may be seen at the robot control end 2. The OpenWrt router 10a can also be used for converting a serial port and a network port, control data of the robot control end 2 can be sent to the single chip 12 of the robot 1, and the single chip 12 sends an instruction to control the motor 13 of the robot 1 to act.
The server 3 communicates with the robot controller 2. The server 3 includes: a WIFI location fingerprint database establishing module 31, a clustering module 32 and a positioning module 33.
The WIFI position fingerprint database establishing module 31 is configured to acquire WIFI position fingerprint data of each position of the robot 1 in the area to be measured, so as to establish a WIFI position fingerprint database.
Specifically, the WIFI location fingerprint database establishing module 31 includes: the system comprises a sampling module 311, a wireless access point selecting module 312 and a WIFI position fingerprint data generating module 313. The sampling module 311 is configured to sample the RSSI value of each wireless access point received by the object to be located at a certain position, and sample the RSSI value at the certain position for multiple times. The wireless access point selection module 312 is coupled to the sampling module 311, and is configured to select a portion of wireless access points with better stability and signal quality by combining the sampling data of the sampling module 311. The WIFI location fingerprint data generating module 313 is coupled to the wireless access point selecting module 312, and is configured to perform gaussian filtering on the RSSI value array of each wireless access point selected by the wireless access point selecting module 312 to remove some RSSI values, obtain an average value of the filtered RSSI value array of each wireless access point, and store a set formed by the obtained average values of each wireless access point as WIFI location fingerprint data of the location.
The wireless access point selecting module 312 includes: a first wireless access point selection module 312a, a second wireless access point selection module 312b, a third wireless access point selection module 312c, and an intersection calculation module 312 d.
The first wireless access point selection module 312a is configured to calculate a frequency of occurrence of the sampling data of each wireless access point in the sampling data of the sampling module, arrange the frequency values in a descending order, and form a first wireless access point set with K wireless access points corresponding to the first K frequency values. The second wireless access point selection module 312b is configured to calculate an average value of the sampled data of each wireless access point in the sampled data, arrange the average values in a descending order, and form a second wireless access point set with K wireless access points corresponding to the first K average values. The third wireless access point selection module 312c is configured to calculate a standard deviation of the sampled data of each wireless access point in the sampled data, arrange the standard deviation values in an ascending order, and form a third wireless access point set with K wireless access points corresponding to the first K standard deviation values. The intersection solving module 312d is configured to solve an intersection of the first wireless access point set 312a, the second wireless access point set 312b, and the third wireless access point set 312c, where each wireless access point in the intersection is a selected wireless access point with a better portion of stability and signal quality.
The clustering module 32 is coupled to the WIFI location fingerprint database establishing module 31, and is configured to classify the WIFI location fingerprint database by using a K-means clustering partition method so as to partition the WIFI location fingerprint database into K classes.
The positioning module 33 is coupled to the clustering module 32, and configured to acquire RSSI data of each wireless access point detected by the WIFI module 10 at the current position of the robot 1 when the robot is positioned, remove RSSI values of some wireless access points with poor signal strength from the RSSI data, calculate euclidean distances between sampling data formed by remaining RSSI values and K classes, and perform matching positioning in the class with the smallest euclidean distance value by using a weighted K nearest neighbor method to obtain a positioning result of the object to be positioned.
In the present embodiment, in order to further improve the positioning accuracy, the server 3 is further provided with a positioning correction module 34. Which is coupled to the positioning module 33 and to the robot controller 2, the robot controller 2 is further arranged to derive the rotational speed of the robot motor 13. The positioning correction module 34 is configured to calculate a first distance between a position of a previous positioning and a position of a current positioning; the robot is further used for calculating a second distance moved by the robot 1 in the process from the previous positioning to the current positioning according to the rotating speed of the robot motor 13, wherein the time of the process can be calculated by a timer; and the positioning device is also used for comparing the first distance with the second distance, if the difference between the first distance and the second distance is greater than the threshold value, judging that the current positioning result is incorrect, sending a message to the robot controller 2 for re-detecting data, and otherwise, judging that the current positioning result is correct and sending the positioning result to the robot controller 2. The robot controller 2 may display through an interface.
In summary, according to the positioning method based on the WIFI location fingerprint and the positioning system of the robot in the embodiment, at the stage of establishing the WIFI location fingerprint database, stability and signal quality of the wireless access point are considered doubly to select a proper wireless access point, so that accuracy is improved, and an invalid wireless access point is avoided; gaussian filtering is carried out on the RSSI value, so that the numerical value of the RSSI value becomes smooth obviously, and small-probability disordered data are removed; the fingerprint data are classified by using a K-means clustering algorithm, so that the matching speed can be increased in the data matching process in the subsequent positioning stage, and the positioning instantaneity is improved; in addition, the positioning result is corrected in the positioning system of the robot, so that the precision of the positioning result is further improved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.
Claims (10)
1. The positioning method based on the WIFI position fingerprint is characterized in that an object to be positioned is provided with a WIFI module, the object to be positioned is communicated with a plurality of wireless access points through the WIFI module, and the WIFI module can detect the received RSSI value of each wireless access point, and the positioning method based on the WIFI position fingerprint comprises the following steps:
acquiring WIFI position fingerprint data of each position of the object to be positioned in the area to be detected and storing the WIFI position fingerprint data into a pre-established WIFI position fingerprint database;
classifying the data of the WIFI position fingerprint database by adopting a K-means clustering division method so as to divide the data into K classes;
and obtaining the RSSI value of each wireless access point received by the object to be positioned at the current position, removing the RSSI value of the wireless access point with the signal strength smaller than a preset threshold value from the RSSI value, calculating Euclidean distances between sampling data formed by the rest of RSSI values and the K classes, and performing matching positioning by adopting a weighted K neighbor method in the class with the minimum Euclidean distance value to obtain a positioning result of the object to be positioned.
2. The WIFI location fingerprint based positioning method of claim 1 wherein obtaining WIFI location fingerprint data of the object to be positioned at a certain location comprises:
sampling the RSSI value of each wireless access point received by the object to be positioned at the position;
screening out a part of wireless access points by combining the sampling data and taking the stability and the signal quality of the wireless access points as selection factors;
and performing Gaussian filtering on the selected RSSI value array of each wireless access point of the part, averaging the filtered RSSI value array of each wireless access point, and storing a set formed by the average values as WIFI position fingerprint data of the position.
3. The WIFI location fingerprint based location method of claim 2 wherein said screening out a portion of wireless access points with wireless access point stability and signal quality as selection factors in combination with sampled data comprises:
calculating the occurrence frequency of the sampling data of each wireless access point in the sampling data, arranging the frequency values in a descending order, and forming a first wireless access point set by Q wireless access points corresponding to the former Q frequency values;
calculating the average value of the sampling data of each wireless access point in the sampling data, arranging the average values in a descending order, and forming a second wireless access point set by Q wireless access points corresponding to the former Q average values;
calculating the standard deviation of the sampling data of each wireless access point in the sampling data, arranging the standard deviation values in an ascending order, and forming a third wireless access point set by the Q wireless access points corresponding to the previous Q standard deviation values;
and obtaining an intersection of the first wireless access point set, the second wireless access point set and the third wireless access point set, and selecting the part of wireless access points in the intersection.
4. A positioning system for a robot, comprising:
the robot is provided with a WIFI module, the robot is accessed to a plurality of wireless access points through the WIFI module, and the WIFI module can detect the received RSSI value of each wireless access point;
the robot controller is communicated with the wireless access points and is used for receiving the RSSI value of each wireless access point detected by the WIFI module;
a server in communication with the robot controller, the server comprising: the robot positioning system comprises a WIFI position fingerprint database establishing module, a clustering module and a positioning module, wherein the WIFI position fingerprint database establishing module is used for acquiring WIFI position fingerprint data of each position of the robot in a region to be detected and storing the WIFI position fingerprint data into a pre-established WIFI position fingerprint database; the clustering module is coupled with the WIFI position fingerprint database establishing module and is used for classifying the data of the WIFI position fingerprint database by adopting a K-means clustering and dividing method so as to divide the data into K classes; the positioning module is coupled with the clustering module and used for acquiring RSSI data of each wireless access point detected by the WIFI module at the current position of the robot when the robot is positioned, removing RSSI values of the wireless access points of which the signal intensity is smaller than a preset threshold value from the RSSI data, calculating Euclidean distances between sampling data formed by the rest RSSI values and the K classes, and performing matching positioning by adopting a weighted K neighbor method in the class with the minimum Euclidean distance value to acquire a positioning result of the object to be positioned.
5. The robotic positioning system of claim 4, wherein said WIFI location fingerprint database creation module comprises:
the sampling module is used for sampling the RSSI value of each wireless access point received by the object to be positioned at a certain position;
the wireless access point selection module is coupled with the sampling module and is used for screening out a part of wireless access points by combining the sampling data of the sampling module and taking the stability and the signal quality of the wireless access points as selection factors;
and the WIFI position fingerprint data generation module is coupled with the wireless access point selection module and is used for performing Gaussian filtering on the RSSI value array of each wireless access point selected by the wireless access point selection module, averaging the filtered RSSI value array of each wireless access point, and storing a set formed by the average values as the WIFI position fingerprint data of the position.
6. The positioning system of a robot according to claim 5, wherein the wireless access point selection module comprises:
the first wireless access point selection module is used for calculating the occurrence frequency of the sampling data of each wireless access point in the sampling data of the sampling module, arranging the frequency values in a descending order, and forming a first wireless access point set by the Q wireless access points corresponding to the former Q frequency values;
the second wireless access point selection module is used for calculating the average value of the sampling data of each wireless access point in the sampling data, arranging the average values in a descending order, and forming a second wireless access point set by the Q wireless access points corresponding to the former Q average values;
the third wireless access point selection module is used for calculating the standard deviation of the sampling data of each wireless access point in the sampling data, arranging the standard deviation values in an ascending order, and forming a third wireless access point set by the Q wireless access points corresponding to the previous Q standard deviation values;
and the intersection solving module is coupled with the first wireless access point selecting module, the second wireless access point selecting module and the third wireless access point selecting module, and is used for solving an intersection of the first wireless access point set, the second wireless access point set and the third wireless access point set and selecting the part of wireless access points in the intersection.
7. The positioning system of a robot according to claim 4, wherein said server further comprises:
the positioning correction module is coupled with the positioning module and the robot controller, and the robot controller is further used for acquiring the rotating speed of the robot motor; the positioning correction module is used for calculating a first distance between the position of the previous positioning and the position of the current positioning; the robot positioning system is also used for calculating a second distance moved by the robot in the process from the previous positioning to the current positioning according to the rotating speed of the motor of the robot; and the positioning device is also used for comparing the first distance with the second distance, if the difference between the first distance and the second distance is greater than a threshold value, judging that the current positioning result is incorrect, sending a message to the robot controller for re-detecting data, and otherwise, judging that the current positioning result is correct and sending the positioning result to the robot controller.
8. A server for locating an object based on WIFI location fingerprints, the server comprising:
the WIFI position fingerprint database establishing module is used for acquiring WIFI position fingerprint data of each position of an object to be positioned in the area to be detected and storing the WIFI position fingerprint data into a pre-established WIFI position fingerprint database;
the clustering module is coupled with the WIFI position fingerprint database establishing module and is used for classifying the data of the WIFI position fingerprint database by adopting a K-means clustering and dividing method so as to divide the data into K classes;
and the positioning module is coupled with the clustering module and used for acquiring RSSI data of each wireless access point at the current position of the object to be positioned when the object to be positioned is positioned, removing the RSSI value of the wireless access point with the signal strength smaller than a threshold value from the RSSI data, calculating Euclidean distances between sampling data formed by each residual RSSI value and the K classes, and performing matching positioning by adopting a weighted K nearest neighbor method in the class with the minimum Euclidean distance value so as to acquire a positioning result of the object to be positioned.
9. The server of claim 8, wherein the WIFI location fingerprint database establishment module comprises:
the sampling module is used for sampling the RSSI value of each wireless access point received by the object to be positioned at a certain position;
the wireless access point selection module is coupled with the sampling module and is used for screening out a part of wireless access points by combining the sampling data of the sampling module and taking the stability and the signal quality of the wireless access points as selection factors;
and the WIFI position fingerprint data generation module is coupled with the wireless access point selection module and is used for performing Gaussian filtering on the RSSI value array of each wireless access point selected by the wireless access point selection module, averaging the filtered RSSI value array of each wireless access point, and storing a set formed by the average values as the WIFI position fingerprint data of the position.
10. The server of claim 9, wherein the wireless access point selection module comprises:
the first wireless access point selection module is used for calculating the occurrence frequency of the sampling data of each wireless access point in the sampling data of the sampling module, arranging the frequency values in a descending order, and forming a first wireless access point set by the Q wireless access points corresponding to the former Q frequency values;
the second wireless access point selection module is used for calculating the average value of the sampling data of each wireless access point in the sampling data, arranging the average values in a descending order, and forming a second wireless access point set by the Q wireless access points corresponding to the former Q average values;
the third wireless access point selection module is used for calculating the standard deviation of the sampling data of each wireless access point in the sampling data, arranging the standard deviation values in an ascending order, and forming a third wireless access point set by the Q wireless access points corresponding to the previous Q standard deviation values;
and the intersection solving module is coupled with the first wireless access point selecting module, the second wireless access point selecting module and the third wireless access point selecting module, and is used for solving an intersection of the first wireless access point set, the second wireless access point set and the third wireless access point set and selecting the part of wireless access points in the intersection.
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