CN110602651A - Positioning method based on WIFI position fingerprint and positioning system of robot - Google Patents

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

Positioning method based on WIFI location fingerprint and robot positioning system

technical field

The invention relates to the technical field of communication, in particular to a positioning method based on WIFI position fingerprints and a robot positioning system.

Background technique

Wireless positioning, as the name implies, is to use various types of signals received to determine the geographic location of the positioning terminal.

GPS (Global Positioning System) and my country's Beidou navigation system have achieved high-precision positioning in a relatively spacious area. There are also some positioning methods in indoor scenarios, such as infrared positioning, ultrasonic positioning, Bluetooth positioning, and positioning based on WIFI fingerprint information. These methods have their own advantages and disadvantages. At present, with the rapid development of the Internet and mobile communications, a large number of WIFI nodes are deployed in many occasions, and mobile terminals generally have their own WIFI modules, which makes the research on positioning methods based on WIFI fingerprint information get a good development.

The WIFI-based fingerprint positioning technology mainly uses the correlation characteristics of spatial information and wireless signal RSSI (Received Signal Strength Indication) to match the WIFI wireless information collected at the location to be tested with the geographic location information. In the actual positioning environment, the RSSI values of n wireless access points received by the point to be tested form a set of n-dimensional vectors and form a one-to-one mapping relationship with the two-dimensional geographic location. Different n-dimensional RSSI vectors correspond to different Geographic location, and then these n-dimensional RSSI vectors are collected to form a fingerprint database, and each set of data in the database is called a location fingerprint. Finally, according to the change of the RSSI signal strength collected from the wireless access point, a set of RSSI values measured at that time can be used to upload to the positioning server, and the location fingerprint with the best similarity can be selected by matching with the location fingerprint of the fingerprint database. The corresponding geographic location is used as the estimated location.

At present, the positioning algorithm based on WIFI fingerprint includes two stages, offline training stage and real-time positioning stage. The main work of the offline training phase is that the staff select the positions of several reference points in the area to be tested, and collect multiple signal strength values from different wireless access points at each reference point at the same time, and then use each reference point The received RSSI value, MAC address, and geographic location information of each wireless access point form a set of associated triple data groups and store them in the database, where each set of data in the database is a location fingerprint . In the real-time positioning stage, the user collects the WIFI information of all wireless access points through the terminal device in the positioning area, and forms a two-tuple of the MAC address and RSSI value of the wireless access point as the input content of the fingerprint positioning algorithm , by comparing them one by one to find out the closest entry, and then use the closest group or groups of location fingerprints corresponding to the geographic location coordinates to perform related calculations, and then estimate the location of the point to be measured.

After researching the current location algorithm based on WIFI fingerprints, the inventor found the following problems: in the online location stage, when the fingerprint library is small, the comparison can be quickly matched one by one, but with the large amount of fingerprint library data When , this operation will consume a lot of time, thereby affecting the efficiency of positioning, and will also generate a large number of repeated calculation operations on the positioning server. Therefore, in the case of ensuring the positioning accuracy, it is also a very big challenge to improve the fingerprint matching algorithm, improve the real-time positioning performance of the positioning system, and reduce the power consumption of the server.

The information disclosed in this Background section is only for enhancing the understanding of the general background of the present invention and should not be taken as an acknowledgment or any form of suggestion that the information constitutes the prior art that is already known to those skilled in the art.

Contents of the invention

The object of the present invention is to provide a positioning method based on WIFI position fingerprints and a positioning system of a robot, which can improve computing efficiency, provide real-time positioning of the positioning system and reduce power consumption of servers.

In order to achieve the above object, the present invention provides a positioning method based on WIFI location fingerprints. The object to be positioned is provided with a WIFI module, and the object to be positioned establishes communication with a plurality of wireless access points through the WIFI module. The WIFI module can detect the received RSSI value of each wireless access point, and the positioning method based on the WIFI location fingerprint includes:

Obtain the WIFI location fingerprint data of each position of the object to be positioned in the area to be measured and store it in a pre-established WIFI location fingerprint database;

Using the K-means clustering method to classify the data of the WIFI location fingerprint database so as to be divided into K classes;

Obtain the RSSI value of each wireless access point received by the object to be positioned at the current position and remove the RSSI value of the wireless access point whose signal strength is less than a preset threshold, and calculate the sampling data composed of each remaining RSSI value For the Euclidean distance to the K classes, the weighted K-nearest neighbor method is used for matching and positioning in the class with the smallest Euclidean distance value, so as to obtain the positioning result of the object to be positioned.

In one embodiment of the present invention, obtaining the WIFI location fingerprint data of the object to be positioned at a certain location includes:

Sampling the RSSI value of each wireless access point received by the object to be positioned at the position;

Combining the sampling data and taking the stability and signal quality of the wireless access point as the selection factors to select a part of the wireless access point;

Perform Gaussian filtering on the RSSI value array of each wireless access point in the selected part, and calculate the average value of the RSSI value array of each wireless access point after filtering, and form the average value into The collection of is stored as the WIFI location fingerprint data of the location.

In an embodiment of the present invention, the combination of sampling data and selecting a part of wireless access points by taking stability and signal quality of wireless access points as selection factors includes: calculating The frequency of occurrence of the sampling data of the access point is arranged in descending order of each frequency value, and the Q wireless access points corresponding to the first Q frequency values are formed into the first wireless access point set; The average value of the sampling data of wireless access points, each average value is arranged in descending order, and the Q wireless access points corresponding to the first Q average values are formed into a second wireless access point set; The standard deviation of the sampled data of each wireless access point in the data is arranged in ascending order of each standard deviation value, and the Q wireless access points corresponding to the first Q standard deviation values form the third wireless access point set ; Compute an intersection set for the first set of wireless access points, the second set of wireless access points, and the third set of wireless access points, and select the part of stability and signal in the intersection set All good quality wireless access points.

The present invention also provides a robot positioning system, which includes: a robot, a robot controller and a server. The robot has a WIFI module through which the robot accesses multiple wireless access points, and the WIFI module can detect the received RSSI value of each wireless access point. The robot controller communicates with the plurality of wireless access points for receiving the RSSI value of each wireless access point detected by the WIFI module. The server communicates with the robot controller, and the server includes: a WIFI position fingerprint database building module, a clustering module and a positioning module, and the WIFI position fingerprint database building module is used to obtain each position of the robot in the area to be tested The WIFI location fingerprint data is stored in the pre-established WIFI location fingerprint database; the clustering module is coupled with the WIFI location fingerprint database establishment module, and is used for using the K-means clustering method to classify the WIFI location fingerprint database The data is classified so as to be divided into K categories; the positioning module is coupled with the clustering module, and is used to obtain each wireless access detected by the WIFI module at the current position of the robot when locating the robot The RSSI data of the access point, and remove the RSSI value of the wireless access point whose signal strength is less than the preset threshold, and calculate the Euclidean distance between the sampled data composed of each remaining RSSI value and the K classes, in the Euclidean In the class with the smallest distance value, the weighted K-nearest neighbor method is used for matching and positioning to obtain the positioning result of the object to be positioned.

In one embodiment of the present invention, the WIFI location fingerprint database establishment module includes: a sampling module, a wireless access point selection module, and a WIFI location fingerprint data generation module.

A sampling module, configured to sample the RSSI value of each wireless access point received by the object to be positioned at a certain location;

A wireless access point selection module, coupled with the sampling module, is used to combine the sampling data of the sampling module and select a part of wireless access points with the stability and signal quality of the wireless access points as selection factors ;

The WIFI location fingerprint data generation module is coupled with the wireless access point selection module, and is used to perform Gaussian filtering on the RSSI value array of each wireless access point selected by the wireless access point selection module , and calculate the average value of the filtered RSSI value array of each wireless access point, and store the set composed of the average value as the WIFI location fingerprint data of the location.

In an embodiment of the present invention, the wireless access point selection module includes: a first wireless access point selection module, a second wireless access point selection module, a third wireless access point selection module, and Intersection seeking module. The first wireless access point selection module is used to calculate the frequency of occurrence of the sampled data of each wireless access point in the sampled data of the sampling module, arrange the frequency values in descending order, and sort the frequency values corresponding to the first Q frequency values Q wireless access points form a first set of wireless access points. The second wireless access point selection module is used to calculate the average value of the sampling data of each wireless access point in the sample data, arrange each average value in descending order, and arrange the Q wireless data points corresponding to the first Q average values The access points form a second set of wireless access points. The third wireless access point selection module is used to calculate the standard deviation of the sampling data of each wireless access point in the sampled data, arrange the standard deviation values in ascending order, and arrange the Q values corresponding to the first Q standard deviation values Wireless access points form a third set of wireless access points. The intersection obtaining module is coupled with the first wireless access point selection module, the second wireless access point selection module, and the third wireless access point selection module, and is used to select the second wireless access point A set of wireless access points, the second set of wireless access points, and the third set of wireless access points are intersected, and in the intersection, the part of stability and signal quality is selected. wireless access point.

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 also used to acquire the motor speed of the robot; the positioning correction module is used to calculate the position of the previous positioning and the current positioning The first distance between positions; it is also used to calculate the second distance moved by the robot in the process from the previous positioning to this positioning according to the robot motor speed; it is also used to compare the first distance and the second distance , if the difference between the two is greater than the threshold, it is determined that the positioning result is incorrect, and a message is sent to the robot controller for re-detection data; otherwise, it is determined that the positioning result is correct and the positioning result is sent to the robot controller .

The present invention also provides a server for locating objects based on WIFI location fingerprints, the server comprising:

The WIFI location fingerprint database building module is used to obtain the WIFI location fingerprint data of each position of the object to be positioned in the area to be measured and store it in the pre-established WIFI location fingerprint database;

A clustering module, coupled with the module for establishing the WIFI location fingerprint database, is used to classify the data of the WIFI location fingerprint database into K categories by using the K-means clustering method;

A positioning module, coupled with the clustering module, used to obtain the RSSI data of each wireless access point at the current position of the object to be positioned when locating the object to be positioned, and remove the RSSI data of each wireless access point whose signal strength is less than The RSSI value of the wireless access point of the threshold value, calculate the Euclidean distance between the sampling data composed of each remaining RSSI value and the K classes, and use the weighted K nearest neighbor method for matching and positioning in the class with the smallest Euclidean distance value. A positioning result of the object to be positioned is obtained.

In one embodiment of the present invention, described WIFI location fingerprint database establishment module comprises:

A sampling module, configured to sample the RSSI value of each wireless access point received by the object to be positioned at a certain location;

A wireless access point selection module, coupled with the sampling module, is used to combine the sampling data of the sampling module and select a part of wireless access points with the stability and signal quality of the wireless access points as selection factors ;

The WIFI location fingerprint data generation module is coupled with the wireless access point selection module, and is used to perform Gaussian filtering on the RSSI value array of each wireless access point selected by the wireless access point selection module , and calculate the average value of the filtered RSSI value array of each wireless access point, and store the set composed of the average value as the WIFI location fingerprint data of the location.

In an embodiment of the present invention, the wireless access point selection module includes:

The first wireless access point selection module is used to calculate the frequency of occurrence of the sampled data of each wireless access point in the sampled data of the sampling module, arrange the frequency values in descending order, and sort the frequency values corresponding to the first Q frequency values The Q wireless access points form the first set of wireless access points;

The second wireless access point selection module is used to calculate the average value of the sampling data of each wireless access point in the sampled data, arrange each average value in descending order, and select the Q corresponding to the first Q average values The wireless access points form a second set of wireless access points;

The third wireless access point selection module is used to calculate the standard deviation of the sampled data of each wireless access point in the sampled data, arrange the standard deviation values in ascending order, and arrange the standard deviation values corresponding to the first Q standard deviation values Q wireless access points form a third wireless access point set;

The intersection obtaining module is coupled with the first wireless access point selection module, the second wireless access point selection module, and the third wireless access point selection module, and is used for the The first set of wireless access points, the second set of wireless access points, and the third set of wireless access points calculate an intersection set, and select the part of wireless access points from the intersection set.

Compared with the prior art, according to the positioning method based on WIFI position fingerprint of the present invention and the positioning system and server of the robot, the K-means clustering algorithm is used to classify the fingerprint data, and the data matching speed can be effectively accelerated in the positioning stage , which can effectively improve computing efficiency, reduce server power consumption, and improve positioning real-time performance.

Description of drawings

Fig. 1 is the step composition of the positioning method based on WIFI location fingerprint according to an embodiment of the present invention;

Fig. 2 is the step composition of the K-means clustering method according to an embodiment of the present invention;

3 is a schematic diagram of a network topology of a robot positioning system according to 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 ways

The specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

Unless expressly stated otherwise, throughout the specification and claims, the term "comprise" or variations thereof such as "includes" or "includes" and the like will be understood to include the stated elements or constituents, and not Other elements or other components are not excluded.

In order to overcome the problems in the prior art, the present invention provides a positioning method based on WIFI location fingerprints, a robot positioning system, and a server. In the process of establishing a WIFI location fingerprint database, the received signal information is preprocessed. It mainly includes the selection of wireless access points and smooth processing of RSSI signals. The K-means clustering algorithm is used to classify fingerprint data. In the positioning stage, it can effectively speed up data matching, effectively improve computing efficiency, and reduce server power consumption, improve positioning real-time performance and positioning accuracy.

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 equipped with a WIFI module, and the object to be positioned establishes communication with multiple 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 location fingerprint includes: step S1 to step S3.

In step S1, the WIFI location fingerprint data of each position of the object to be located in the area to be tested is obtained to establish a WIFI location fingerprint database. Specifically, obtaining the WIFI location fingerprint data of the object to be positioned at a certain location includes: sampling the RSSI value of each wireless access point received by the object to be positioned at the location, and sampling multiple times at the location; The data selects a part of wireless access points with better stability and signal quality; Gaussian filtering is performed on the RSSI value array of each wireless access point in the selected part to remove some RSSI values, and each Calculate the average value of the filtered RSSI value arrays of four wireless access points, and store the set of the obtained average values of each wireless access point as the WIFI location fingerprint data of the location.

Wherein, selecting a part of wireless access points with better stability and signal quality by combining the sampled data includes calculating the frequency of occurrence of the sampled data of each wireless access point in the sampled data, sorting each frequency value in descending order, and sorting the previous The K wireless access points corresponding to the K frequency values form the first wireless access point set; calculate the average value of the sampling data of each wireless access point in the sampled data, arrange each average value in descending order, and The K wireless access points corresponding to the first K average values form the second wireless access point set; calculate the standard deviation of the sampling data of each wireless access point in the sampling data, and arrange each standard deviation value in ascending order , the K wireless access points corresponding to the first K standard deviation values form the third wireless access point set; for the first wireless access point set, the second wireless access point set and the third wireless access point set Find the intersection set of access points. A part of wireless access points with better stability and better signal quality are selected from the wireless access points in the above intersection according to actual conditions.

Specifically, in the area to be tested, suppose that the sampling point L receives your set of n wireless access points, which can be expressed as {AP ₁ ,AP ₂ ,…AP _n }, and then perform M times at this point Sampling, considering the frequency of occurrence of each wireless access point in the entire sampling sample, as shown in Equation 1:

In Equation 1, M(AP _i ) represents the number of samples in which the wireless access point AP _i appears in the sampling, and M represents the total number of samples in the sampling. Then Fre(AP _i ) is sorted in descending order, and the top Q wireless access points are selected as the selection result, denoted as Q ₁ .

And considering the signal strength of the wireless access point in the sample, the M times measured signal strength of each wireless access point is averaged, as shown in Equation 2:

In Equation 2, RSSI _i represents the i-th measurement value of a certain wireless access point. It can represent the signal quality of the wireless access point, and the N wireless access points of the sampling point L according to Arranged in descending order, select the first Q as the selection result, denoted as Q ₂ .

At the same time, the standard deviation should also be considered. After M times of sampling at the sampling point L, the wireless access point AP ₁ receives M ₁ sets of sampling samples, and its data is {RSSI ₁ ,RSSI ₂ ,...,RSSI _m1 }, then the wireless The standard deviation of the fluctuation of the access point is shown in Equation 3.

In formula 3, Indicates the average value of M ₁ sampling samples. The standard deviation σ can reflect the fluctuation range of the wireless access point. The N wireless access points of the sampling point L are arranged in ascending order according to the standard deviation σ, and the first Q wireless access points are also selected as the selection result. Denoted as Q ₃ .

Calculate the intersection of Q ₁ , Q ₂ , and Q ₃ obtained above, as shown in Equation 4.

Q＝Q ₁ ∩Q ₂ ∩Q ₃ (4)

Generally, the point to be tested can receive RSSI samples of more than 5 wireless access points, which can achieve good positioning accuracy. The Q value can be set to be greater than 5 according to the number of wireless access points in the actual environment to be tested. If the number of received wireless access points is relatively small, the above solution can be selectively adopted according to the actual situation.

For the selected wireless access point, due to the flow of people and the occlusion of objects during the measurement, some values with small probability inevitably appear. In order to improve the accuracy of the final positioning result, this embodiment uses a Gaussian filtering model to exclude these small probability values, and performs Gaussian filtering on the selected RSSI value array of each wireless access point in this part to remove some RSSI values and average the filtered array of RSSI values for each access point.

The specific algorithm is as follows: first, assuming that {RSSI ₁ , RSSI ₂ ,...,RSSI _n } are used to represent n sampling values from a certain wireless access point at a sample point, then these RSSI values can be represented by a normal model, As shown in formula 5.

in:

In Equation 5, x represents the RSSI signal strength value.

Then take the RSSI value between u-3σ<x≤u+3σ, remove the value outside the range, and save it as {RSSI ₁ ,RSSI ₂ ,…,RSSI _n1 }.

Finally, the average value of the selected RSSI values is calculated according to formula 8, and the average value is the calculated RSSI signal strength value R of the sample point from the wireless access point.

In Equation 8, n ₁ represents the total number of filtered samples.

In step S2, the K-means clustering method is used to classify the WIFI location fingerprint database so as to be divided into K classes.

Specifically, as shown in FIG. 2 , the K-means clustering method includes steps S20 to S24.

The WIFI location fingerprint data set X={x ₁ ,x ₂ ,…,x _n } is used as input, which contains N data objects, the number of cluster centers is K, and the clustering criterion threshold is ε. The dataset divided into K clusters is set as output.

In step S20, K cluster centers M={m ₁ ⁽¹⁾ , m ₂ ⁽¹⁾ , . . . m _k ⁽¹⁾ } are randomly selected from the data set X.

In step S21, calculate the Euclidean distance from each remaining data x _i (i=1,2,...,NK) to the K cluster centers M, denoted by D(i,r), n is the positioning area The number of all wireless access points in , _xi (RSSI _j ) represents the signal strength value of the jth wireless access point received by the i-th data object, m _r ⁽¹⁾ (RSSI _j ) represents the The r cluster centers receive the signal strength value of the jth wireless access point, if the data object x _i does not receive the signal of the jth wireless access point, it is replaced by an infinitely small value, then The algorithm of D(i,r) adopts formula 9.

In step S22, assign x _i to the cluster center with the shortest Euclidean distance to it.

In step S23, calculate the average value of all objects in each class, and use it as the new cluster center point, as shown in formula 10.

Among them, in formula 10, _{Cr represents the data contained in the jth category, and N r represents} the number of the jth category of data.

Repeat the above three steps in step S24, and classify the remaining x _i one by one. When the cluster center does not change or the fluctuation of m _r ^(p) is less than a given threshold, the centers of K classes are output, and the algorithm ends.

In step S3, the weighted K-nearest neighbor method is used for matching and positioning. Specifically, obtain the RSSI value of each wireless access point received by the object to be positioned at the current position and remove the RSSI values of some wireless access points with poor signal strength, and calculate the sampling data composed of the remaining RSSI values For the Euclidean distance with K classes, the weighted K-nearest neighbor method is used for matching and positioning in the class with the smallest Euclidean distance value to obtain the positioning result of the object to be located.

Example 2

Based on the same inventive concept, the present invention also provides a robot positioning system, which will be described below.

FIG. 3 is a schematic diagram of the network topology of the positioning system of the robot. Fig. 4 is a structural composition of a positioning system of a robot according to an embodiment. It includes: a robot 1 , a robot controller 2 , and a server 3 .

The robot 1 has a WIFI module 10, and the robot 1 accesses multiple 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 also has a camera 11, a single-chip microcomputer 12, a motor 13, and the like. The robot controller 2 communicates with multiple wireless access points for receiving the RSSI value of each wireless access point detected by the WIFI module 10 . Optionally, an OpenWrt router 10a is set in the WIFI module 10 of the robot 1, and data interaction can be performed with the robot controller 2 through the OpenWrt router 10a. The image of the robot is encoded, and the video from robot 1 can be seen on the robot control terminal 2. The conversion between the serial port and the network port can also be carried out through the OpenWrt router 10a, the control data of the robot control terminal 2 can be sent to the single-chip microcomputer 12 of the robot 1, and the single-chip microcomputer 12 sends instructions to act from the motor 13 of the control robot 1.

The server 3 communicates with the robot controller 2 . The server 3 includes: a WIFI location fingerprint database establishment module 31 , a clustering module 32 and a positioning module 33 .

The WIFI location fingerprint database establishment module 31 is used to obtain the WIFI location fingerprint data of each location of the robot 1 in the area to be tested so as to establish a WIFI location fingerprint database.

Specifically, the WIFI location fingerprint database establishment module 31 includes: a sampling module 311 , a wireless access point selection module 312 , and a WIFI location fingerprint data generation module 313 . The sampling module 311 is used for sampling the RSSI value of each wireless access point received by the object to be positioned at a certain position, and sampling at this position for multiple times. The wireless access point selection module 312 is coupled with the sampling module 311 , and is used to select a part of wireless access points with better stability and signal quality in combination with the sampling data of the sampling module 311 . The WIFI location fingerprint data generation module 313 is coupled with the wireless access point selection module 312, and is used to perform Gaussian filtering on the RSSI value array of each wireless access point selected by the wireless access point selection module 312 to remove Some RSSI values, and calculate the average value of the filtered RSSI value array of each wireless access point, and use the set of the average value of each wireless access point as the WIFI location fingerprint data of the location to store.

Wherein, the wireless access point selection 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 312d .

The first wireless access point selection module 312a is used to calculate the frequency of occurrence of the sampling data of each wireless access point in the sampling data of the sampling module, arrange each frequency value in descending order, and calculate the K corresponding to the first K frequency values Wireless access points form a first set of wireless access points. The second wireless access point selection module 312b is used to calculate the average value of the sampling data of each wireless access point in the sampling data, arrange each average value in descending order, and sort the K wireless access points corresponding to the first K average values The access points form a second set of wireless access points. The third wireless access point selection module 312c is used to calculate the standard deviation of the sampling data of each wireless access point in the sampling data, arrange each standard deviation value in ascending order, and sort the K corresponding to the first K standard deviation values The wireless access points form a third set of wireless access points. The intersection calculating module 312d is used to calculate the intersection of the first wireless access point set 312a, the second wireless access point set 312b and the third wireless access point set 312c, wherein each wireless access point in the intersection The access points are selected wireless access points with better stability and signal quality.

The clustering module 32 is coupled with the WIFI location fingerprint database building module 31, and is used to classify the WIFI location fingerprint database into K classes by using the K-means clustering method.

The positioning module 33 is coupled with the clustering module 32, and is used to obtain the RSSI data of each wireless access point detected by the WIFI module 10 at the current position of the robot 1 when locating the robot, and remove some wireless access points with poor signal strength. The RSSI value of the wireless access point, calculate the Euclidean distance between the sampled data composed of each remaining RSSI value and K classes, and use the weighted K nearest neighbor method for matching and positioning in the class with the smallest Euclidean distance value to obtain the positioning of the object to be located result.

In this embodiment, in order to further improve the positioning accuracy, a positioning correction module 34 is also provided in the server 3 . It is coupled with the positioning module 33 and the robot controller 2 , and the robot controller 2 is also used to acquire the rotational speed of the robot motor 13 . The positioning correction module 34 is used to calculate the first distance between the position of the previous positioning and the position of this positioning; it is also used to calculate the moving distance of the robot 1 from the previous positioning to the current positioning according to the rotating speed of the robot motor 13 The second distance, where the time of this process can be calculated by a timer; it is also used to compare the first distance and the second distance, if the difference between the two is greater than the threshold, it is determined that the positioning result is incorrect, and a message is sent Go to the robot controller 2 to re-detect the data, otherwise it is determined that the positioning result is correct and the positioning result is sent to the robot controller 2. The robot controller 2 can be displayed through the interface.

To sum up, according to the positioning method based on WIFI location fingerprint and the positioning system of the robot in this embodiment, in the stage of establishing the WIFI location fingerprint database, the stability and signal quality of the wireless access point are double considered to select the appropriate The wireless access point improves the accuracy and avoids invalid wireless access points; Gaussian filtering is performed on the RSSI value, which makes the value significantly smoother and removes the messy data with small probability; and uses K The mean value clustering algorithm classifies the fingerprint data, which can speed up the matching speed and improve the real-time positioning in the data matching process of the subsequent positioning stage; in addition, the positioning result is corrected in the positioning system of the robot, which further improves the positioning result. accuracy.

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. 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, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowcharts and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. These descriptions are 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 the specific principles of the invention and its practical application, thereby enabling others skilled in the art to make and use various exemplary embodiments of the invention, as well as various Choose and change. It is intended that the scope of the invention be defined by the claims and their equivalents.

Claims (10)

1. A positioning method based on WIFI position fingerprints, the object to be positioned is provided with a WIFI module, and the object to be positioned establishes communication with a plurality of wireless access points through the WIFI module, and the WIFI module can detect received The RSSI value of each wireless access point, it is characterized in that, this positioning method based on WIFI position fingerprint comprises: Obtain the WIFI location fingerprint data of each position of the object to be positioned in the area to be measured and store it in a pre-established WIFI location fingerprint database; Using the K-means clustering method to classify the data of the WIFI location fingerprint database so as to be divided into K classes; Obtain the RSSI value of each wireless access point received by the object to be positioned at the current position and remove the RSSI value of the wireless access point whose signal strength is less than a preset threshold, and calculate the sampling data composed of each remaining RSSI value For the Euclidean distance to the K classes, the weighted K-nearest neighbor method is used for matching and positioning in the class with the smallest Euclidean distance value, so as to obtain the positioning result of the object to be positioned. 2. the positioning method based on WIFI position fingerprint as claimed in claim 1, is characterized in that, obtaining the WIFI position fingerprint data of the object to be positioned at a certain position comprises: Sampling the RSSI value of each wireless access point received by the object to be positioned at the position; Combining the sampling data and taking the stability and signal quality of the wireless access point as the selection factors to select a part of the wireless access point; Perform Gaussian filtering on the RSSI value array of each wireless access point in the selected part, and calculate the average value of the RSSI value array of each wireless access point after filtering, and form the average value into The collection of is stored as the WIFI location fingerprint data of the location. 3. The location method based on WIFI location fingerprint as claimed in claim 2, characterized in that, the combined sampling data and the stability and signal quality of the wireless access point are selected as selection factors to screen out a part of the wireless access points include: Calculate the occurrence frequency of the sampling data of each wireless access point in the sampled data, arrange the frequency values in descending order, and form the first wireless access points corresponding to the first Q frequency values to form the first wireless access point set of points; Calculate the average value of the sampling data of each wireless access point in the sampled data, arrange the average values in descending order, and form the second wireless access point corresponding to the Q wireless access points corresponding to the first Q average values set of points; Calculate the standard deviation of the sampling data of each wireless access point in the sampling data, arrange the standard deviation values in ascending order, and form the third wireless access point corresponding to the Q wireless access points corresponding to the first Q standard deviation values collection of access points; Obtain an intersection set for the first set of wireless access points, the second set of wireless access points, and the third set of wireless access points, and select the part of wireless access points from the intersection set . 4. A positioning system for a robot, comprising: A robot, which has a WIFI module, through which the robot accesses a plurality of wireless access points, and the WIFI module can detect the received RSSI value of each wireless access point; A robot controller, communicating with the plurality of wireless access points, for receiving the RSSI value of each wireless access point detected by the WIFI module; The server communicates with the robot controller, and the server includes: a WIFI position fingerprint database building module, a clustering module and a positioning module, and the WIFI position fingerprint database building module is used to obtain each location of the robot in the area to be tested. The WIFI location fingerprint data of the location is stored in a pre-established WIFI location fingerprint database; the clustering module is coupled with the WIFI location fingerprint database establishment module, and is used to classify the WIFI location fingerprint database using the K-means clustering method Classify the data so as to be divided into K categories; the positioning module is coupled with the clustering module, and is used to obtain each wireless data detected by the WIFI module at the current position of the robot when positioning the robot. Access the RSSI data of the access point, and remove the RSSI value of the wireless access point whose signal strength is less than the preset threshold, and calculate the Euclidean distance between the sampled data composed of each remaining RSSI value and the K classes, in the In the class with the smallest Euclidean distance value, the weighted K-nearest neighbor method is used for matching and positioning to obtain the positioning result of the object to be positioned. 5. the positioning system of robot as claimed in claim 4, is characterized in that, described WIFI position fingerprint database establishment module comprises: A sampling module, configured to sample the RSSI value of each wireless access point received by the object to be positioned at a certain location; A wireless access point selection module, coupled with the sampling module, is used to combine the sampling data of the sampling module and select a part of wireless access points with the stability and signal quality of the wireless access points as selection factors ; The WIFI location fingerprint data generation module is coupled with the wireless access point selection module, and is used to perform Gaussian filtering on the RSSI value array of each wireless access point selected by the wireless access point selection module , and calculate the average value of the filtered RSSI value array of each wireless access point, and store the set composed of the average value as the WIFI location fingerprint data of the location. 6. the positioning system of robot as claimed in claim 5, is characterized in that, described wireless access point selection module comprises: The first wireless access point selection module is used to calculate the frequency of occurrence of the sampled data of each wireless access point in the sampled data of the sampling module, arrange the frequency values in descending order, and sort the frequency values corresponding to the first Q frequency values The Q wireless access points form the first set of wireless access points; The second wireless access point selection module is used to calculate the average value of the sampling data of each wireless access point in the sampled data, arrange each average value in descending order, and select the Q corresponding to the first Q average values The wireless access points form a second set of wireless access points; The third wireless access point selection module is used to calculate the standard deviation of the sampled data of each wireless access point in the sampled data, arrange the standard deviation values in ascending order, and arrange the standard deviation values corresponding to the first Q standard deviation values Q wireless access points form a third wireless access point set; The intersection obtaining module is coupled with the first wireless access point selection module, the second wireless access point selection module, and the third wireless access point selection module, and is used for the The first set of wireless access points, the second set of wireless access points, and the third set of wireless access points calculate an intersection set, and select the part of wireless access points from the intersection set. 7. the positioning system of robot as claimed in claim 4, is characterized in that, described server also comprises: The positioning correction module is coupled with the positioning module and the robot controller, and the robot controller is also used to obtain the motor speed of the robot; the positioning correction module is used to calculate the position of the previous positioning and the current positioning The first distance between the positions; it is also used to calculate the second distance moved by the robot in the process from the previous positioning to this positioning according to the robot motor speed; it is also used to compare the first distance with the second If the difference between the two is greater than the threshold, it is determined that the positioning result is incorrect, and a message is sent to the robot controller for re-detection data, otherwise it is determined that the positioning result is correct and the positioning result is sent to the robot controller device. 8. A server, for positioning an object based on a WIFI location fingerprint, characterized in that the server includes: The WIFI location fingerprint database building module is used to obtain the WIFI location fingerprint data of each position of the object to be positioned in the area to be measured and store it in the pre-established WIFI location fingerprint database; A clustering module, coupled with the module for establishing the WIFI location fingerprint database, is used to classify the data of the WIFI location fingerprint database into K categories by using the K-means clustering method; A positioning module, coupled with the clustering module, used to obtain the RSSI data of each wireless access point at the current position of the object to be positioned when locating the object to be positioned, and remove the RSSI data of each wireless access point whose signal strength is less than The RSSI value of the wireless access point of the threshold value, calculate the Euclidean distance between the sampling data composed of each remaining RSSI value and the K classes, and use the weighted K nearest neighbor method for matching and positioning in the class with the smallest Euclidean distance value. A positioning result of the object to be positioned is obtained. 9. server as claimed in claim 8, is characterized in that, described WIFI location fingerprint database establishment module comprises: A sampling module, configured to sample the RSSI value of each wireless access point received by the object to be positioned at a certain location; A wireless access point selection module, coupled with the sampling module, is used to combine the sampling data of the sampling module and select a part of wireless access points with the stability and signal quality of the wireless access points as selection factors ; The WIFI location fingerprint data generation module is coupled with the wireless access point selection module, and is used to perform Gaussian filtering on the RSSI value array of each wireless access point selected by the wireless access point selection module , and calculate the average value of the filtered RSSI value array of each wireless access point, and store the set composed of the average value as the WIFI location fingerprint data of the location. 10. The server according to claim 9, wherein the wireless access point selection module comprises: The first wireless access point selection module is used to calculate the frequency of occurrence of the sampled data of each wireless access point in the sampled data of the sampling module, arrange the frequency values in descending order, and sort the frequency values corresponding to the first Q frequency values The Q wireless access points form a first set of wireless access points; The second wireless access point selection module is used to calculate the average value of the sampling data of each wireless access point in the sampled data, arrange each average value in descending order, and select the Q corresponding to the first Q average values The wireless access points form a second set of wireless access points; The third wireless access point selection module is used to calculate the standard deviation of the sampled data of each wireless access point in the sampled data, arrange the standard deviation values in ascending order, and arrange the standard deviation values corresponding to the first Q standard deviation values Q wireless access points form a third wireless access point set; The intersection obtaining module is coupled with the first wireless access point selection module, the second wireless access point selection module, and the third wireless access point selection module, and is used for the The first set of wireless access points, the second set of wireless access points, and the third set of wireless access points calculate an intersection set, and select the part of wireless access points from the intersection set.