Rail-mounted robot positioning method and device, electronic equipment, and storage medium

Abstract

The present application provides a method and device for positioning a rail-mounted robot, an electronic device, and a storage medium, which belong to the field of robot positioning technology, including: determining the target transmission power of electromagnetic signals transmitted by a reader to multiple tags based on the electromagnetic interference intensity of the working environment of the rail-mounted robot; determining the reference position information of the rail-mounted robot based on the position information corresponding to the multiple tags and the return information sent by each of them; determining the detection sensitivity of the Hall sensor set on the rail-mounted robot based on the magnetic field anomaly of the working environment of the rail-mounted robot, and obtaining correction data of the reference position information based on the detection sensitivity; fusing the correction data with the reference position information of the rail-mounted robot to obtain the target position information of the rail-mounted robot. The method and device for positioning a rail-mounted robot, the electronic device, and the storage medium provided in the present application can improve the positioning accuracy of the robot.

Description

Technical Field

[0001] The present application belongs to the field of robot positioning technology, and more specifically, relates to a rail-mounted robot positioning method and device, electronic equipment, and storage medium. Background Art

[0002] Rail-mounted robots are widely used in industrial automation, logistics, and warehousing for tasks like inspection and transportation. Their positioning accuracy directly impacts operational efficiency and safety, but existing positioning technology still has many shortcomings.

[0003] Currently, traditional rail-mounted robot positioning methods rely primarily on single sensors, such as radio frequency identification (RFID), lasers, or vision-based navigation. However, these methods have limited effectiveness in practical applications. For example, RFID positioning is susceptible to electromagnetic interference in the operating environment. When interference intensity is high, the signal transmission quality between the reader and the tag degrades, resulting in increased positioning errors and inaccurate positioning. Summary of the Invention

[0004] The purpose of this application is to provide a rail-mounted robot positioning method and device, electronic equipment, and storage medium to improve the positioning accuracy of the robot.

[0005] A first aspect of the embodiments of the present application provides a positioning method for a rail-mounted robot, comprising:

[0006] A target transmission power for transmitting electromagnetic signals from a reader to a plurality of tags is determined based on the electromagnetic interference intensity of the working environment of the rail-mounted robot; the plurality of tags are configured to receive the electromagnetic signals transmitted by the reader based on the target transmission power and to send return information to the reader in response to the electromagnetic signals; the reader is disposed on the rail-mounted robot, and the plurality of tags are distributed on the track;

[0007] Determine the reference position information of the rail-mounted robot based on the position information corresponding to the multiple tags and the return information sent by each tag;

[0008] Determining the detection sensitivity of a Hall sensor provided on the rail-mounted robot based on the degree of magnetic field anomaly in the working environment of the rail-mounted robot, and obtaining correction data of the reference position information based on the detection sensitivity;

[0009] The correction data is fused with the reference position information of the rail-mounted robot to obtain the target position information of the rail-mounted robot.

[0010] A second aspect of the embodiments of the present application provides a rail-mounted robot positioning device, comprising:

[0011] A power determination module is configured to determine a target transmission power for an electromagnetic signal transmitted by a reader to a plurality of tags based on the electromagnetic interference intensity of the working environment of the rail-mounted robot; the plurality of tags are configured to receive the electromagnetic signal transmitted by the reader based on the target transmission power and to send return information to the reader based on the electromagnetic signal; the reader is mounted on the rail-mounted robot, and the plurality of tags are distributed on the track;

[0012] A first positioning module is used to determine the reference position information of the rail-mounted robot based on the position information corresponding to the multiple tags and the return information sent by each tag;

[0013] a correction module, configured to determine the detection sensitivity of a Hall sensor provided on the rail-mounted robot based on the degree of magnetic field anomaly in the working environment of the rail-mounted robot, and to obtain correction data of the reference position information based on the detection sensitivity;

[0014] The second positioning module is used to fuse the correction data with the reference position information of the rail-mounted robot to obtain the target position information of the rail-mounted robot.

[0015] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned rail-mounted robot positioning method when executing the computer program.

[0016] In a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned rail-mounted robot positioning method are implemented.

[0017] The beneficial effects of the rail-mounted robot positioning method and device, electronic device, and storage medium provided by the embodiments of the present application are as follows: the embodiments of the present application can ensure stable signal transmission between the tag and the reader in a complex electromagnetic environment by dynamically adjusting the reader transmission power according to the electromagnetic interference intensity, thereby improving the anti-interference ability of the positioning of the present application, reducing signal loss or misreading, and enhancing positioning reliability. The detection sensitivity of the Hall sensor is adjusted based on the magnetic field anomaly, which can adapt to different magnetic field environments, accurately obtain correction data, and effectively overcome the influence of magnetic field interference on positioning accuracy. The correction data is integrated with the reference position information, fully combining the advantages of the two positioning methods, making up for the shortcomings of a single method, and greatly improving positioning accuracy, so that the robot can operate accurately under various working conditions, meet the needs of diverse tasks, and provide strong guarantees for the efficient and precise operation of the rail-mounted robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 A schematic flow chart of a positioning method for a rail-mounted robot provided in one embodiment of the present application;

[0020] Figure 2 A schematic structural diagram of a rail-mounted robot provided in one embodiment of the present application;

[0021] Figure 3 This is a structural block diagram of a rail-mounted robot positioning device provided in one embodiment of the present application;

[0022] Figure 4 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0024] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0025] Please refer to Figure 1-2 , Figure 1 This is a flow chart of a positioning method for a rail-mounted robot provided in one embodiment of the present application. Figure 2 This is a schematic structural diagram of a rail-mounted robot provided in one embodiment of the present application. The method is applied to the rail-mounted robot, including:

[0026] S101: Determine the target transmission power of electromagnetic signals transmitted by the reader to multiple tags based on the electromagnetic interference intensity of the working environment of the rail-mounted robot; the multiple tags are used to receive the electromagnetic signals transmitted by the reader based on the target transmission power, and send return information to the reader based on the electromagnetic signals; the reader is set on the rail-mounted robot, and the multiple tags are distributed on the track.

[0027] In this embodiment, the rail-mounted robot 6 is a robot that can run on a specific track 5 and can be used for industrial inspections, logistics transportation, etc., and completes inspection tasks along the preset track 5.

[0028] In this embodiment, reader 4 is a device mounted on a rail-mounted robot 6. Its primary function is to transmit electromagnetic signals to tag 2 and receive information returned by tag 2. Reader 4 uses modulation and demodulation techniques to load the data to be transmitted onto the electromagnetic signal for transmission, and also processes and analyzes the received signal.

[0029] In this embodiment, tags 2 are small electronic devices distributed along track 5. Each tag 2 has unique identification information and pre-stores its own location information. Upon receiving an electromagnetic signal from reader 4, tag 2 responds based on the signal content and returns a signal containing its own location information to reader 4.

[0030] In this embodiment, electromagnetic interference intensity refers to the degree to which various electromagnetic interference sources present in the working environment of the rail-mounted robot 6 affect normal electromagnetic signal transmission. These interference sources can be other electronic devices, power lines, etc., which generate additional electromagnetic signals that overlap or conflict with the signals transmitted by the reader 4, thereby affecting signal transmission quality and normal reception by the tag 2.

[0031] In this embodiment, the electromagnetic interference detection module can detect the electromagnetic signal strength in different frequency ranges in the environment and integrate the electromagnetic signal strength in different frequency ranges into a comprehensive electromagnetic interference strength index. In this embodiment, the electromagnetic interference strength can be expressed as:

[0032]

[0033] in, Indicates the electromagnetic interference intensity of the working environment of the rail-mounted robot 6, represents the electromagnetic intensity of the i-th interference source, and n represents the number of interference sources.

[0034] In this embodiment, the operating environment of rail-mounted robot 6 is subject to varying degrees of electromagnetic interference, which can weaken the electromagnetic signal transmitted by reader 4 or distort the signal received by tag 2. To ensure that tag 2 accurately receives the electromagnetic signal transmitted by reader 4, the target transmit power of the electromagnetic signal transmitted by reader 4 to tag 2 is determined based on the intensity of the electromagnetic interference in the environment. If the electromagnetic interference is strong, the transmit power needs to be increased to ensure that the signal can penetrate the interference and reach tag 2. Conversely, if the interference is weak, the transmit power can be appropriately reduced to reduce energy consumption and interference with other devices.

[0035] S102: Determine the reference position information of the rail-mounted robot based on the position information corresponding to the multiple tags and the return information sent by each tag.

[0036] In this embodiment, the position information is the specific position data of the tag 2 on the track 5 stored therein. The position information can be expressed in the form of coordinates (such as two-dimensional or three-dimensional coordinates) or relative position (such as the distance from the starting point of the track 5).

[0037] In this embodiment, the return information is a feedback signal generated by the tag 2 based on its own position information and the content of the received signal after receiving the electromagnetic signal transmitted by the reader 4. The return information includes the unique identifier of the tag 2, its own position information, and some status information.

[0038] In this embodiment, when a track-mounted robot 6 operates on a track 5, a reader 4 mounted on the robot transmits electromagnetic signals at a predetermined target transmission power to surrounding tags 2. Upon receiving the signals, the tags 2 transmit their position information via return messages to the reader 4. After receiving the return messages from multiple tags 2, the reader 4 calculates the position of the track-mounted robot 6 relative to these tags 2 based on factors such as signal strength and timing of the return messages, combined with the tag 2's position information, thereby obtaining a preliminary reference position.

[0039] For example, using the principle of time difference in signal propagation, assuming that the signal propagation speed between tag 2 and reader 4 is c, the following uses two tags (tag A and tag B shown below) as an example to explain how to obtain the reference position information of reader 4. If reader 4 receives return signals from two different tags A and B at the same time, and knows the position coordinates of tags A and B respectively (x A ,y A ) and (x B ,y B ), by measuring the time difference between the signal from tags A and B reaching the reader 4 , we can get the reference position information of the rail-mounted robot 6. In order to ensure the calculation accuracy, the position coordinates and time difference are normalized so that all parameters are calculated in the same dimension. The specific calculation formula is:

[0040]

[0041] The solution obtained That is, the position coordinates (reference position information) of the reader 4, i.e., the robot.

[0042] S103: Determine the detection sensitivity of the Hall sensor provided on the rail-mounted robot based on the degree of magnetic field anomaly in the working environment of the rail-mounted robot, and obtain correction data of the reference position information based on the detection sensitivity.

[0043] In this embodiment, magnetic field anomaly refers to the stability of the magnetic field in the operating environment of rail-mounted robot 6. Under normal circumstances, the magnetic field in the environment should be relatively stable. However, the presence of various ferromagnetic objects, electrical equipment, and other surrounding objects can cause magnetic field fluctuations, resulting in magnetic field anomalies. The magnitude of the magnetic field anomaly reflects the severity of these fluctuations.

[0044] In this embodiment, Hall effect sensor 3 is a sensor based on the Hall effect. When a magnetic field acts on a Hall element, a potential difference is generated across the element. By measuring this potential difference, the presence and changes of the magnetic field can be detected. In this embodiment, Hall effect sensor 3 is used to detect changes in the magnetic field generated by magnets 1 placed on both sides of tag 2 along track 5.

[0045] In this embodiment, the detection sensitivity refers to the sensitivity of the Hall sensor 3 to magnetic field changes. The higher the sensitivity, the weaker the magnetic field changes that the Hall sensor 3 can detect; the lower the sensitivity, the larger the magnetic field changes that can be detected.

[0046] In this embodiment, because the degree of magnetic field anomaly in the working environment can affect the Hall sensor 3's ability to detect the magnetic field of magnet 1, the detection sensitivity of Hall sensor 3 is determined based on the degree of magnetic field anomaly. If the magnetic field anomaly is large, the sensitivity of Hall sensor 3 needs to be increased to more accurately detect changes in magnet 1's magnetic field. Conversely, the sensitivity can be reduced to minimize the impact of external interference on the detection results. Once the detection sensitivity is determined, Hall sensor 3 begins detecting changes in the magnetic field generated by magnet 1. Because magnet 1 is positioned on either side of tag 2 along track 5, when robot 6 passes tag 2, the two Hall sensors 3 mounted on robot 6 convert the magnetic signals detected by magnet 1 into electrical signals. When robot 6 receives two identical electrical signals, it indicates that the robot has passed tag 2 at that location. Based on the location of tag 2, correction data for the reference position information can be obtained. This correction data can be used to compensate for errors in the reference position information, improving positioning accuracy.

[0047] S104: Fusing the correction data with the reference position information of the rail-mounted robot to obtain the target position information of the rail-mounted robot.

[0048] In this embodiment, the obtained correction data and the reference position information are fused, and the fusion process can adopt various methods, such as weighted averaging method, Kalman filtering, etc.

[0049] For example, using a weighted average method, different weights are assigned to the reference position information and the correction data based on the reliability of the two data sources. If tag 2's positioning is relatively stable and accurate in the current environment, the reference position information based on tag 2 can be given a higher weight. Conversely, if the correction data detected by Hall sensor 3 in the current magnetic field environment is more reliable, it can be given a higher weight.

[0050] By fusing the correction data with the reference position information of the rail-mounted robot 6, the advantages of both positioning methods are utilized. The positioning of the tag 2 can quickly provide an approximate position range, while the correction data from the Hall effect sensor 3 based on magnetic field detection can accurately correct this approximate position, thereby obtaining more accurate target position information for the rail-mounted robot 6, meeting the robot's high-precision positioning requirements in various mission scenarios.

[0051] From the above, it can be concluded that this embodiment can ensure stable signal transmission between tag 2 and reader 4 in complex electromagnetic environments by dynamically adjusting the transmission power of reader 4 based on the intensity of electromagnetic interference, thereby improving the anti-interference ability of the positioning of this embodiment, reducing signal loss or misreading, and enhancing positioning reliability. By adjusting the detection sensitivity of Hall sensor 3 based on the degree of magnetic field anomaly, it can adapt to different magnetic field environments, accurately obtain correction data, and effectively overcome the impact of magnetic field interference on positioning accuracy. By integrating the correction data with the reference position information, the advantages of the two positioning methods are fully combined, the shortcomings of a single method are compensated, and positioning accuracy is greatly improved, enabling the robot to operate accurately under various working conditions, meet the needs of diverse tasks, and provide strong guarantees for the efficient and precise operation of the rail-mounted robot 6.

[0052] In one embodiment of the present application, the rail-mounted robot positioning method further includes:

[0053] Determining the attenuation rate of the electromagnetic signal based on the distance between the reader and each tag;

[0054] The target transmission power is adjusted based on the attenuation rate of the electromagnetic signal to obtain a new target transmission power; so that the reader transmits electromagnetic signals to multiple tags based on the new target transmission power.

[0055] In this embodiment, the distance between reader 4 and tag 2 refers to the physical distance between reader 4 mounted on track-mounted robot 6 and tags 2 distributed along track 5. This distance changes continuously as the robot moves along track 5. After reader 4 transmits a signal, tag 2 receives and returns the signal. By accurately measuring the round-trip signal time, the distance between reader 4 and tag 2 can be calculated.

[0056] In this embodiment, the attenuation rate of an electromagnetic signal refers to the proportional relationship between the intensity of the electromagnetic signal and the increase in distance during the propagation process. Generally speaking, when an electromagnetic signal propagates through the air, its intensity gradually decreases as the propagation distance increases, and the attenuation rate reflects the degree of this decrease.

[0057] The loss of electromagnetic signal propagation in air is:

[0058]

[0059] Here, L represents the loss of electromagnetic signals propagating through air, d represents distance, and f represents the frequency of the electromagnetic signal. 32.5 is a baseline value. The term 20lgd represents the effect of distance on signal loss. The greater the distance, the greater the loss, and the loss is proportional to the logarithm of the distance. The term 20lgf represents the effect of frequency on signal loss. The higher the frequency, the greater the signal loss during propagation, and this is also proportional to the logarithm of the frequency.

[0060] The attenuation rate of the electromagnetic signal is:

[0061]

[0062] Where S represents the attenuation rate of the electromagnetic signal, represents the target transmit power after attenuation, , represents the initial target transmit power,

[0063] In this embodiment, a transmit power adjustment model is established to adjust the target transmit power. Assume that the initial target transmit power is , according to the attenuation rate Calculate the new target transmit power , the calculation formula is:

[0064]

[0065] in, is the adjustment coefficient, which reflects the influence of the attenuation rate on the transmission power adjustment. It can be obtained by experimentally testing the signal reception quality at different distances.

[0066] In this embodiment, since the attenuation of the electromagnetic signal affects the signal strength received by tag 2, if the attenuation is too great, tag 2 will not be able to accurately receive the signal. Therefore, it is necessary to adjust the target transmission power of reader 4 based on the calculated attenuation rate. When the attenuation rate is large, it means that the signal is lost a lot during the propagation process. In order to ensure that tag 2 can receive a signal of sufficient strength, it is necessary to increase the transmission power of reader 4. Conversely, if the attenuation rate is small, it means that the signal loss is relatively small, and the transmission power can be appropriately reduced to reduce energy consumption and electromagnetic interference to the surrounding environment. Through this adjustment, a new target transmission power is obtained, and the new target transmission power is used to send electromagnetic signals to each tag 2, so that the signal transmitted by reader 4 can still be reliably received by tag 2 after propagating for a certain distance.

[0067] In this embodiment, after adjusting to a new target transmit power, reader 4 transmits an electromagnetic signal based on this new target power to tag 2. Upon receiving this electromagnetic signal of a certain strength, tag 2 decodes and processes the signal, extracts the information contained therein, generates return information based on its own location information, and then sends this return information to reader 4.

[0068] From the above, it can be concluded that this embodiment dynamically adjusts the transmission power of the reader 4 by taking into account the distance between the reader 4 and the tag 2 and the attenuation rate of the electromagnetic signal. While ensuring that the tag 2 can stably receive the signal, it can also reduce energy consumption and improve the reliability and accuracy of the positioning of the rail-mounted robot 6.

[0069] In one embodiment of the present application, the magnetic field anomaly of the working environment of the rail-mounted robot is determined based on the ratio of the magnetic field change to the initial magnetic field strength, and the method further includes:

[0070] The magnetic field variation is adjusted based on the magnetic field gradient within a unit distance of the magnet to obtain a new magnetic field anomaly degree; magnets are set on both sides of each tag along the track direction;

[0071] The detection sensitivity of the Hall sensor is adjusted based on the new magnetic field anomaly and the running time of the rail-mounted robot to obtain a new detection sensitivity;

[0072] The correction data of the reference position information is obtained based on the detection sensitivity, including:

[0073] Corrected data of the reference position information is obtained based on the new detection sensitivity.

[0074] In this embodiment, the magnetic field variation refers to the change in magnetic field intensity relative to the initial magnetic field intensity at the location of the rail-mounted robot 6 over a specified period of time. The magnetic field variation reflects the dynamic changes in the magnetic field over time. The magnetic field variation can be measured in real time by the Hall effect sensor 3.

[0075] In this embodiment, the initial magnetic field strength refers to the reference magnetic field strength value at the position where the rail-mounted robot 6 is located when it starts to run, and can be measured by electromagnetic induction.

[0076] In this embodiment, the magnetic field anomaly is an indicator for measuring the stability of the magnetic field in the working environment, and can be determined by the ratio of the magnetic field change to the initial magnetic field strength.

[0077] In this embodiment, the ratio of the magnetic field change to the initial magnetic field strength is calculated to quantify the degree of magnetic field anomaly. The larger the ratio, the higher the degree of magnetic field anomaly, that is, the more serious the interference of the environmental magnetic field; the smaller the ratio, the relatively stable magnetic field. For example, if the initial magnetic field strength is At a certain moment, the magnetic field strength becomes , then the magnetic field change is , then the magnetic field anomaly degree is specifically expressed as:

[0078]

[0079] in, Indicates the degree of magnetic field anomaly.

[0080] In this embodiment, the magnetic field gradient per unit distance of magnet 1 refers to the rate of change of magnetic field intensity per unit distance along track 5, reflecting the spatial distribution and variation of the magnetic field. Regions with larger magnetic field gradients experience more dramatic magnetic field variations, potentially causing greater interference to Hall sensor 3 detection. Multiple high-precision magnetometers can be installed along the track at preset intervals. These high-precision magnetometers measure the magnetic field intensity at multiple points, generating a series of magnetic field intensity values. The magnetic field gradient is the rate of change of magnetic field intensity per unit distance and is calculated as the ratio of the difference in magnetic field intensity between two adjacent points to the distance between them.

[0081] In this embodiment, the initial magnetic field anomaly is adjusted based on the magnetic field gradient to obtain a new magnetic field anomaly because the magnetic field gradient can more finely reflect the impact of magnetic field changes on the detection of the Hall sensor 3. The new magnetic field anomaly calculation formula is:

[0082]

[0083] in, Indicates the new magnetic field anomaly degree, Indicates the degree of magnetic field anomaly, Represents the weight coefficient, which is used to measure the influence of the magnetic field gradient on the adjustment of the magnetic field anomaly. It can be determined through experiments to accurately reflect the relationship between the magnetic field gradient and the detection difficulty of the Hall sensor 3. represents the magnetic field gradient.

[0084] In this embodiment, the operating time of the rail-mounted robot 6 may affect the performance of the Hall effect sensor 3. As operating time increases, the sensor may experience issues such as aging and temperature drift, resulting in changes in detection sensitivity. Furthermore, prolonged exposure to abnormal magnetic fields can also have a cumulative impact on detection.

[0085] In this embodiment, the detection sensitivity of the Hall sensor 3 is adjusted by comprehensively considering the new magnetic field anomaly degree and the operation time of the rail-mounted robot 6 .

[0086] Assume that the initial detection sensitivity is , the new detection sensitivity is expressed as:

[0087]

[0088] in and are weight coefficients, respectively measuring the influence of magnetic field anomaly and operation time on the adjustment of detection sensitivity; Indicates the running time of the rail-mounted robot, that is, the time elapsed from the start of the robot's operation to the current moment; is the maximum designed operating time of the rail-mounted robot 6. The new detection sensitivity calculation formula shows that the greater the magnetic field anomaly and the longer the operating time, the greater the adjustment range of the detection sensitivity. In this way, the Hall effect sensor 3 can maintain optimal detection performance under different magnetic field environments and operating times.

[0089] Before calculating the magnetic field anomaly, the new magnetic field anomaly and the detection sensitivity, this embodiment needs to normalize the parameters involved in the formula so that the corresponding parameters are expressed as dimensionless values ​​to ensure that all parameters are calculated in the same dimension.

[0090] In this embodiment, after adjusting the detection sensitivity of Hall sensor 3, the sensor will more accurately detect the magnetic field changes generated by magnet 1 based on the new sensitivity. Because magnet 1 is placed on both sides of tag 2 along track 5, by analyzing the magnetic field change data detected by Hall sensor 3, combined with the position information of magnet 1 and the magnetic field distribution model, the correction data of the reference position information is calculated. For example:

[0091] Assume that the coordinates of magnet 1 on track 5 are M1 and M2 (located on both sides of tag 2 respectively), and the robot positions are H1 and H2 when Hall sensor 3 detects the peak value of the magnetic field. Ideally, H1 and M1, H2 and M2 should coincide, and the actual deviation is =H1-M1, =H2-M2. Corrected data is averaged. , combined with the magnetic field distribution model (such as the law of magnetic field intensity attenuation with distance) to weight the deviation, the final correction amount is (k is a weighting factor based on the new sensitivity.) This correction is used to adjust the reference position information to obtain the corrected data.

[0092] From the above, it can be concluded that this embodiment effectively improves the accuracy and reliability of the positioning of the rail-mounted robot 6 by dynamically adjusting the detection sensitivity of the Hall sensor 3 and obtaining correction data of the reference position information based on the adjusted sensitivity, so that it can operate stably in a complex and changeable magnetic field environment.

[0093] In one embodiment of the present application, the method further includes:

[0094] Determining a weight corresponding to the reference position information based on the electromagnetic interference intensity;

[0095] Determining the weight corresponding to the correction data based on the degree of magnetic field anomaly;

[0096] Acquire acceleration data of the rail-mounted robot and determine a weight corresponding to the acceleration data based on the operating state of the rail-mounted robot, wherein the acceleration data is detected by an inertial sensor provided on the rail-mounted robot;

[0097] The correction data and the reference position information of the rail-mounted robot are integrated to obtain the target position information of the rail-mounted robot, including:

[0098] The extended Kalman filter algorithm combines the reference position information, correction data, and acceleration data with their corresponding weights to obtain the target position information of the rail-mounted robot. The sum of the corresponding weights of the reference position information, correction data, and acceleration data is 1.

[0099] In this embodiment, the intensity of electromagnetic interference (EMI) affects the signal transmission quality between reader 4 and tag 2, and thus the accuracy of the reference location information. When the EMI intensity is high, the signal is contaminated by noise, resulting in increased errors in the reference location information. In this case, the weight of the reference location information should be reduced. Conversely, when the EMI intensity is low, the reliability of the reference location information is high, and a higher weight can be assigned. For example, in this embodiment, a mapping table between EMI intensity and weights can be established, and the corresponding weight can be found based on the real-time measured EMI intensity value.

[0100] In this embodiment, the degree of magnetic field anomaly reflects the stability of the magnetic field in the operating environment. A high degree of magnetic field anomaly indicates significant interference with the ambient magnetic field, leading to significant errors in the corrected data detected by Hall sensor 3. In this case, the weight of the corrected data should be reduced. A low degree of magnetic field anomaly indicates that the corrected data is relatively reliable and can be assigned a higher weight. In this embodiment, a mapping table or function can be used to determine the correspondence between magnetic field anomaly and weight.

[0101] In this embodiment, the operating state of the rail-mounted robot 6 (e.g., acceleration, deceleration, or constant speed) affects the accuracy of the acceleration data detected by the inertial sensor. During acceleration or deceleration, the inertial sensor is affected by inertial forces and produces errors. However, during constant speed motion, the acceleration data is relatively stable and reliable. Therefore, the weighting of the acceleration data needs to be dynamically adjusted based on the robot's operating state. For example, when the robot is accelerating or decelerating, the weighting of the acceleration data is reduced; when in constant speed motion, the weighting is increased.

[0102] In this embodiment, the extended Kalman filter algorithm is a nonlinear estimation method based on Kalman filtering. In this embodiment, the motion model and sensor measurement model of the track-mounted robot 6 are nonlinear. By linearizing the nonlinear system equations, an optimal estimate of the system state is obtained at each moment.

[0103] In this embodiment, a motion model of rail-mounted robot 6 is derived based on acceleration data, and the extended Kalman filter algorithm is used to predict the target position information at the next moment. Specifically, the state transition equation combines the state estimate at the previous moment (i.e., the target position information at the previous moment) with the current acceleration data (taking into account their weights) to predict state variables such as position and velocity at the next moment.

[0104] The reference position information and correction data are used as observations, and their respective weights are combined to update the prediction results. The prediction results and observations are weighted and fused together by calculating the Kalman gain to obtain the updated target position information. The Kalman gain adjusts the contribution of the prediction results and observations to the final estimate based on the accuracy of the observations (determined by the weights), making the fused target position information more accurate and reliable.

[0105] In this embodiment, the prediction and update steps of the extended Kalman filter algorithm comprehensively consider reference position information, correction data, and acceleration data, and then rationally integrate them according to the weight of each data to ultimately obtain the target position information of the rail-mounted robot 6. This target position information combines the advantages of multiple sensor data, effectively reducing positioning errors and improving positioning accuracy in complex working environments.

[0106] It can be concluded from the above that this embodiment achieves accurate estimation of the position information of the rail-mounted robot 6 by dynamically determining the weight of each data and using the extended Kalman filter algorithm for data fusion.

[0107] In one embodiment of the present application, the rail-mounted robot positioning method further includes:

[0108] The acceleration data is compensated based on the temperature drift of the inertial sensor and the vibration interference of the rail-mounted robot to obtain the target acceleration data, and the weight corresponding to the target acceleration data is determined based on the operating state of the rail-mounted robot;

[0109] Based on the extended Kalman filter algorithm, the reference position information, correction data, and acceleration data are fused through their corresponding weights to obtain the target position information of the rail-mounted robot, including:

[0110] Based on the extended Kalman filter algorithm, the reference position information, correction data, and target acceleration data are fused through their corresponding weights to obtain the target position information of the rail-mounted robot.

[0111] During operation, the internal physical properties of inertial sensors change with temperature. For example, the elastic and electronic components within the sensor are sensitive to temperature. Temperature fluctuations can cause these components to change in performance, leading to deviations in measured acceleration values, known as temperature drift. To improve the accuracy of acceleration measurements, compensation for this temperature-induced deviation is necessary.

[0112] Temperature drift compensation can be achieved by using the difference between the inertial sensor's operating temperature and the reference temperature, the temperature change rate, and the corresponding compensation coefficient to correct for the effects of temperature changes on the sensor output. Vibration interference compensation can be achieved by constructing a sinusoidal model that combines the acceleration amplitude, frequency, and time variation of the vibration with the compensation coefficient to eliminate errors caused by robot vibration. After the acceleration data is sequentially compensated for temperature drift and vibration interference, target acceleration data is obtained that accurately reflects the robot's motion state. This target acceleration data can then be fused with reference position information and correction data to determine the target position information, effectively improving positioning accuracy.

[0113] From the above, it can be concluded that the compensation process of this embodiment achieves the coordinated suppression of sensor errors and working condition interference. The compensated acceleration data finally outputted lays the foundation for accurate pose estimation in a track environment.

[0114] In one embodiment of the present application, the rail-mounted robot positioning method further includes:

[0115] Get the current working status data of the tag, reader and Hall sensor respectively:

[0116] In response to a deviation between the working state data and the standard state data being greater than a preset deviation value, the visual positioning mode is started to obtain target position information of the rail-mounted robot.

[0117] In this embodiment, during the inspection process, the rail-mounted robot 6 generates parameter data reflecting its operating status. Examples include the signal strength and response time of the tag 2; the transmit power and receive sensitivity of the reader 4; and the detection accuracy and sensitivity of the Hall effect sensor 3. This embodiment can be equipped with corresponding sensor interfaces to collect relevant data from various components in real time.

[0118] In this embodiment, the standard state data is predetermined and represents the parameter values ​​or parameter ranges that the tag 2, reader 4, and Hall sensor 3 should exhibit under normal operating conditions. For example, the standard signal transmission power of the tag 2 can be set to a fixed value, the standard received signal strength indicator of the reader 4 can be within a certain range, and the standard output voltage of the Hall sensor 3 corresponds to a specific magnetic field strength.

[0119] In this embodiment, the real-time operating status data collected from the tag 2, reader 4, and Hall effect sensor 3 are compared with the corresponding standard status data, and the deviation between the real-time operating status data and the standard status data is calculated. For quantifiable parameters such as signal transmission power and received signal strength, the difference between the actual value and the standard value is directly calculated. For some range-type parameters, the actual value is determined to be within the standard range. If not, the degree of excess is calculated.

[0120] For example, if the standard signal transmission power of tag 2 is P b , the actual collected transmission power is P s , then the deviation can be expressed as .

[0121] In this embodiment, the calculated deviation is compared with a preset deviation value. If the deviation is greater than the preset deviation value, it indicates that the tag 2, reader 4, or Hall sensor 3 has failed, been interfered with, or is operating abnormally. Continuing to rely on these devices for positioning may result in inaccurate positioning or system failure.

[0122] If the deviation between the operating state data and the standard state data exceeds a preset deviation value, this embodiment can activate the visual positioning mode as a backup positioning method to ensure that the rail-mounted robot 6 can continue to accurately locate and operate. The visual positioning mode uses a camera mounted on the robot to identify and analyze environmental features or specific landmarks around the track 5, and determines the robot's position through image processing algorithms.

[0123] As can be seen from the above, this embodiment can promptly detect component anomalies by acquiring operating status data from the tag 2, reader 4, and Hall effect sensor 3 and comparing it with standard data. When the deviation exceeds a preset value, the visual positioning mode is activated, thus avoiding inaccurate positioning caused by component failure, enhancing the adaptability and reliability of the rail-mounted robot 6 in complex environments, and ensuring its stable operation.

[0124] In one embodiment of the present application, starting the visual positioning mode to obtain target position information of the rail-mounted robot includes:

[0125] In response to the rail-mounted robot operating in a visual positioning mode, determining a degree of recognition of the first image data;

[0126] If the recognition degree of the first image data is greater than or equal to the preset recognition degree, obtaining the target position information of the rail-mounted robot based on the first image data;

[0127] If the recognition degree of the first image data is less than the preset recognition degree, the target position information of the rail-mounted robot is obtained based on the second image data;

[0128] The first image data and the second image data are image data collected based on different reference information; the reference information is identification information used to determine the target position information of the rail-mounted robot in the visual positioning mode.

[0129] In this embodiment, a QR code and other reference information corresponding to the position information of the label 2 can be set at the corresponding label 2 position, wherein the objects collected by the first image data and the second image data are different. The first image data is image data containing the QR code (reference information), and the second image data is data after scanning other reference information (such as the number, graphic mark, etc. on the label 2).

[0130] In this embodiment, when the rail-mounted robot 6 switches to visual positioning mode, the image data captured by the camera must have sufficient resolution to accurately extract information used for positioning. Recognition measures the clarity and identifiability of target information in the image data. For example, factors such as image clarity, the presence of obstructions, and sufficient contrast in the target information can all affect resolution.

[0131] In this embodiment, the preset recognition level is a standard pre-set based on actual application requirements and the capabilities of the image processing algorithm. This embodiment uses the image processing algorithm to extract features from the collected first image data, such as the edge clarity, pattern integrity, and grayscale contrast of the QR code, and compares them with the preset recognition level to determine whether the recognition level meets the requirements.

[0132] In this embodiment, if the recognition level of the first image data is greater than or equal to the preset recognition level, the QR code image quality is good and position information can be reliably extracted from it. The image processing algorithm can decode the QR code. Since the QR code encodes the corresponding position information of tag 2, this position information can be directly obtained after decoding. The target position information of the rail-mounted robot 6 can then be calculated based on the relative positional relationship between the robot and the QR code. For example, the spatial position of the robot relative to the QR code can be determined by using the geometric features of the QR code and an image coordinate transformation algorithm.

[0133] In this embodiment, when the recognition level of the first image data is less than a preset level, it indicates that the QR code may be stained, blurred, or partially obscured, making it difficult to accurately decode and obtain the location information. In this case, the second image data can be used to determine the robot's target location information. The second image data can be a marker with a unique shape, color, or pattern. The image processing algorithm can extract and match features from this reference information. By comparing it with a pre-stored reference information database and combining it with relevant positioning algorithms (such as a positioning algorithm based on feature point matching), the target location information of the rail-mounted robot 6 can be calculated.

[0134] For example, it is assumed that the rail-mounted robot 6 is applied to a cargo handling scenario in a large automated warehouse.

[0135] Along the warehouse's track 5, tags 2 are placed at regular intervals. Each tag 2 is accompanied by a QR code containing location information and other reference information, along with a unique shape and color combination. For example, within a shelf area, tags 2 on track 5 correspond to the location of different shelves.

[0136] The rail-mounted robot 6 is equipped with a high-definition camera for image acquisition, and the robot's control system has image processing and positioning calculation functions.

[0137] When the robot switches to the visual positioning mode due to abnormal working status of the tag 2, the reader 4 or the Hall sensor 3, the camera first collects the first image data containing the QR code.

[0138] If the QR code is clear and complete (e.g., under normal lighting conditions and not obscured by goods or other objects), its recognition level exceeds the preset level. The image processing algorithm quickly decodes the QR code and obtains the location information of Tag 2 corresponding to the QR code. For example, the location corresponds to a specific shelf location in a warehouse row. Further analysis of the QR code's position and orientation within the image, combined with the camera's installation parameters, calculates the robot's precise position relative to that shelf location. This determines the robot's target location, allowing it to accurately navigate to that shelf location and handle the goods.

[0139] If the QR code is worn out due to long-term use, covered by dust, or partially blocked by goods, the recognition degree of the first image data is less than the preset recognition degree. At this time, the camera collects the second image data containing other reference information (such as a logo with a unique shape and color combination). This embodiment extracts features from these logos, such as identifying the shape outline, color distribution and other features of the logo. The extracted features are matched with the logo features pre-stored in the database to determine the type and location information of the logo. Assuming that the positions of these logos correspond to specific areas in the warehouse, by matching the successfully matched logo information, combined with the relative position relationship between the robot and the logo, and using algorithms such as triangulation positioning, the target position information of the robot in the warehouse is calculated, and the robot is guided to accurately drive to the destination to complete the task.

[0140] As can be seen from the above, this embodiment selects different image data to obtain target position information of the rail-mounted robot 6 by determining the recognition level of the first image data. When the first image data has high recognition level, it is used first to ensure positioning accuracy; when the recognition level is insufficient, the second image data is used to ensure positioning reliability.

[0141] Corresponding to the rail-mounted robot positioning method in the above embodiment, Figure 3 This is a structural block diagram of a rail-mounted robot positioning device provided in one embodiment of the present application. For ease of illustration, only the parts related to the embodiment of the present application are shown. Figure 3 The rail-mounted robot positioning device 20 includes: a power determination module 21 , a first positioning module 22 , a correction module 23 , and a second positioning module 24 .

[0142] The power determination module is configured to determine a target transmission power for transmitting electromagnetic signals from a reader to a plurality of tags based on the electromagnetic interference intensity of the working environment of the rail-mounted robot; the plurality of tags are configured to receive the electromagnetic signals transmitted by the reader based on the target transmission power and to send return information to the reader based on the electromagnetic signals; the reader is disposed on the rail-mounted robot, and the plurality of tags are distributed on the track;

[0143] A first positioning module is used to determine the reference position information of the rail-mounted robot based on the position information corresponding to the multiple tags and the return information sent by each tag;

[0144] a correction module, configured to determine the detection sensitivity of a Hall sensor provided on the rail-mounted robot based on the degree of magnetic field anomaly in the working environment of the rail-mounted robot, and to obtain correction data of the reference position information based on the detection sensitivity;

[0145] The second positioning module is used to fuse the correction data with the reference position information of the rail-mounted robot to obtain the target position information of the rail-mounted robot.

[0146] In one embodiment of the present application, the rail-mounted robot positioning device 20 further includes a target transmission power adjustment module; the target transmission power adjustment module is specifically configured to:

[0147] Determining the attenuation rate of the electromagnetic signal based on the distance between the reader and each tag;

[0148] The target transmission power is adjusted based on the attenuation rate of the electromagnetic signal to obtain a new target transmission power; so that the reader transmits electromagnetic signals to multiple tags based on the new target transmission power.

[0149] In one embodiment of the present application, the magnetic field anomaly of the working environment of the rail-mounted robot is determined based on the ratio of the magnetic field change to the initial magnetic field strength, and the correction module 23 is further configured to:

[0150] The magnetic field variation is adjusted based on the magnetic field gradient within a unit distance of the magnet to obtain a new magnetic field anomaly degree; magnets are set on both sides of each tag along the track direction;

[0151] The detection sensitivity of the Hall sensor is adjusted based on the new magnetic field anomaly and the running time of the rail-mounted robot to obtain a new detection sensitivity;

[0152] The correction data of the reference position information is obtained based on the detection sensitivity, including:

[0153] Corrected data of the reference position information is obtained based on the new detection sensitivity.

[0154] In one embodiment of the present application, the rail-mounted robot positioning 20 further includes: a fusion module; specifically configured to:

[0155] Determining a weight corresponding to the reference position information based on the electromagnetic interference intensity;

[0156] Determining the weight corresponding to the correction data based on the degree of magnetic field anomaly;

[0157] Acquire acceleration data of the rail-mounted robot and determine a weight corresponding to the acceleration data based on the operating state of the rail-mounted robot, wherein the acceleration data is detected by an inertial sensor provided on the rail-mounted robot;

[0158] The correction data and the reference position information of the rail-mounted robot are integrated to obtain the target position information of the rail-mounted robot, including:

[0159] Based on the extended Kalman filter algorithm, the reference position information, correction data, and acceleration data are fused through their corresponding weights to obtain the target position information of the rail-mounted robot.

[0160] In one embodiment of the present application, the rail-mounted robot positioning device further includes an acceleration compensation module, specifically configured to:

[0161] The acceleration data is compensated based on the temperature drift of the inertial sensor and the vibration interference of the rail-mounted robot to obtain the target acceleration data, and the weight corresponding to the target acceleration data is determined based on the operating state of the rail-mounted robot;

[0162] The fusion module is also used to:

[0163] Based on the extended Kalman filter algorithm, the reference position information, correction data, and target acceleration data are fused through their corresponding weights to obtain the target position information of the rail-mounted robot.

[0164] In one embodiment of the present application, the rail-mounted robot positioning device 20 further includes a mode switching module; the mode switching module is specifically configured to:

[0165] Get the current working status data of the tag, reader and Hall sensor respectively:

[0166] In response to a deviation between the working state data and the standard state data being greater than a preset deviation value, the visual positioning mode is started to obtain target position information of the rail-mounted robot.

[0167] In one embodiment of the present application, the mode switching module is further configured to:

[0168] In response to the rail-mounted robot operating in a visual positioning mode, determining a degree of recognition of the first image data;

[0169] If the recognition degree of the first image data is greater than or equal to the preset recognition degree, obtaining the target position information of the rail-mounted robot based on the first image data;

[0170] If the recognition degree of the first image data is less than the preset recognition degree, the target position information of the rail-mounted robot is obtained based on the second image data;

[0171] The first image data and the second image data are image data collected based on different reference information; the reference information is identification information used to determine the target position information of the rail-mounted robot in the visual positioning mode.

[0172] See also Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 4The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 3 The functions of the power determination module 21, the first positioning module 22, the correction module 23 and the second positioning module 24 are shown. In this embodiment, the electronic device can be the rail-hanging robot shown in the above embodiment.

[0173] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0174] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0175] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information such as the original acceleration, the preset deviation value, and the preset recognition level in this embodiment.

[0176] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present application can execute the implementation methods described in the first and second embodiments of the rail-mounted robot positioning method provided in the embodiments of the present application, and can also execute the implementation methods of the electronic device described in the embodiments of the present application, which will not be repeated here.

[0177] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0178] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0179] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0180] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0181] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple devices can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0182] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0183] In addition, the functional modules in the various embodiments of the present application may be integrated into one device, or each module may exist physically separately, or two or more modules may be integrated into one device. The above-mentioned integrated device may be implemented in the form of hardware or in the form of software functional units.

[0184] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A positioning method for a rail-mounted robot, characterized in that: include: Determine the target transmission power of electromagnetic signals transmitted by the reader to multiple tags based on the electromagnetic interference intensity of the working environment of the rail-mounted robot; The plurality of tags are used to receive the electromagnetic signal transmitted by the reader based on the target transmission power, and send return information to the reader in response to the electromagnetic signal; The reader is arranged on the rail-mounted robot, and the multiple tags are distributed on the track; Determine the reference position information of the rail-mounted robot based on the position information corresponding to the multiple tags and the return information sent by each tag; determining a detection sensitivity of a Hall sensor provided on the rail-mounted robot based on a magnetic field anomaly of a working environment of the rail-mounted robot, and obtaining correction data of the reference position information based on the detection sensitivity; The magnetic field anomaly of the working environment of the rail-mounted robot is determined based on the ratio of the magnetic field variation to the initial magnetic field strength, and the method further includes: The magnetic field variation is adjusted based on the magnetic field gradient within a unit distance of the magnet to obtain a new magnetic field anomaly; the magnets are arranged on both sides of each tag along the track direction; Adjusting the detection sensitivity of the Hall sensor based on the new magnetic field anomaly degree and the running time of the rail-mounted robot to obtain a new detection sensitivity; Wherein, the obtaining of correction data of the reference position information based on the detection sensitivity includes: acquiring correction data of the reference position information based on the new detection sensitivity; The correction data is integrated with the reference position information of the rail-mounted robot to obtain the target position information of the rail-mounted robot.

2. The rail-mounted robot positioning method according to claim 1, wherein: Also includes: determining an attenuation rate of the electromagnetic signal based on the distance between the reader and each tag; Adjusting the target transmit power based on the attenuation rate of the electromagnetic signal to obtain a new target transmit power; So that the reader transmits the electromagnetic signal to the plurality of tags based on the new target transmission power.

3. The rail-mounted robot positioning method according to claim 1, wherein: The method further comprises: determining a weight corresponding to the reference position information based on the electromagnetic interference intensity; determining a weight corresponding to the correction data based on the degree of magnetic field anomaly; Acquiring acceleration data of the rail-mounted robot and determining a weight corresponding to the acceleration data based on an operating state of the rail-mounted robot, wherein the acceleration data is detected by an inertial sensor provided on the rail-mounted robot; The step of fusing the correction data with the reference position information of the rail-mounted robot to obtain the target position information of the rail-mounted robot includes: Based on the extended Kalman filter algorithm, the reference position information, the correction data, and the acceleration data are fused through their respective corresponding weights to obtain the target position information of the rail-mounted robot.

4. The rail-mounted robot positioning method according to claim 3, wherein: Also includes: Compensating the acceleration data based on the temperature drift of the inertial sensor and the vibration interference of the rail-mounted robot to obtain target acceleration data, and determining a weight corresponding to the target acceleration data based on the operating state of the rail-mounted robot; The method of obtaining the target position information of the rail-mounted robot by fusing the reference position information, the correction data, and the acceleration data based on the extended Kalman filter algorithm through their corresponding weights includes: Based on the extended Kalman filter algorithm, the reference position information, the correction data, and the target acceleration data are fused through their respective corresponding weights to obtain the target position information of the rail-mounted robot.

5. The rail-mounted robot positioning method according to claim 1, wherein: Also includes: Obtain the current working status data corresponding to the tag, the reader, and the Hall sensor respectively: In response to a deviation between the working state data and the standard state data being greater than a preset deviation value, a visual positioning mode is started to obtain the target position information of the rail-mounted robot.

6. The rail-mounted robot positioning method according to claim 5, wherein: The starting of the visual positioning mode to obtain the target position information of the rail-mounted robot includes: In response to the rail-mounted robot operating in a visual positioning mode, determining a degree of recognition of the first image data; If the recognition degree of the first image data is greater than or equal to a preset recognition degree, obtaining target position information of the rail-mounted robot based on the first image data; If the recognition degree of the first image data is less than the preset recognition degree, obtaining the target position information of the rail-mounted robot based on the second image data; The first image data and the second image data are image data collected based on different reference information; the reference information is identification information used to determine the target position information of the rail-mounted robot in a visual positioning mode.

7. A rail-mounted robot positioning device, characterized in that: include: A power determination module is used to determine a target transmission power of electromagnetic signals transmitted by a reader to a plurality of tags based on the electromagnetic interference intensity of the working environment of the rail-mounted robot; The plurality of tags are used to receive the electromagnetic signal transmitted by the reader based on the target transmission power, and send return information to the reader in response to the electromagnetic signal; The reader is arranged on the rail-mounted robot, and the multiple tags are distributed on the track; A first positioning module is used to determine the reference position information of the rail-mounted robot based on the position information corresponding to the multiple tags and the return information sent by each tag; a correction module, configured to determine a detection sensitivity of a Hall sensor provided on the rail-mounted robot based on a degree of magnetic field anomaly in a working environment of the rail-mounted robot, and to obtain correction data for the reference position information based on the detection sensitivity; The magnetic field anomaly of the working environment of the rail-mounted robot is determined based on the ratio of the magnetic field change to the initial magnetic field strength. The correction module 23 is further configured to: The magnetic field variation is adjusted based on the magnetic field gradient within a unit distance of the magnet to obtain a new magnetic field anomaly degree; magnets are set on both sides of each tag along the track direction; The detection sensitivity of the Hall sensor is adjusted based on the new magnetic field anomaly and the running time of the rail-mounted robot to obtain a new detection sensitivity; The correction data of the reference position information is obtained based on the detection sensitivity, including: obtaining correction data of the reference position information based on the new detection sensitivity; The second positioning module is used to fuse the correction data with the reference position information of the rail-mounted robot to obtain the target position information of the rail-mounted robot.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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