Stacker cargo platform positioning method and system based on multi-sensor fusion

Abstract

The application discloses a kind of based on multi-sensor fusion's stacker cargo platform positioning method and system.The method obtains original distance data by laser distance sensor arranged on the column of stacker and cargo platform, and data fusion is carried out using complementary relationship, and acceleration information is collected using inertial sensor;The laser data before and after fusion is respectively subjected to Kalman filtering, and the data reliability is evaluated based on the variance of filtered value and original value, and the abnormal data section at the end is identified under instantaneous interference;Speed is calculated based on fused laser data, and speed is obtained by integrating inertial acceleration, and more reliable fused speed is obtained by dynamically weighting fusion according to acceleration change trend;When detecting laser data anomaly, the positioning in abnormal period is predicted and corrected using fused speed, and the final positioning result is output.The application effectively fuses the advantages of laser high precision and inertial anti-shielding, and improves the robustness and reliability of positioning in complex industrial environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automated warehouse logistics, in particular to a stacker load table positioning method and system based on multi-sensor fusion. BACKGROUND

[0002] In an automated warehouse, a stacker is the core equipment for realizing automatic storage and retrieval of goods. In order to accurately complete the positioning of the goods, it is usually necessary to obtain the three-dimensional position coordinates of the load table of the stacker in real time and accurately. At present, the mainstream scheme relies on real-time communication with the stacker control system to obtain the internal positioning data calculated by the stacker encoder or servo system. However, this scheme has significant limitations: first, a stable and low-latency data link needs to be established between the moving load table and the stacker main controller, and in a complex industrial wireless environment, the signal is easily disturbed and the reliability is difficult to guarantee; second, not all stacker manufacturers open their internal positioning data interface or provide standard communication protocols; third, if the external system requests positioning data through high-frequency polling, it may bring additional load to the stacker main controller, which was not considered in the design, and there is a risk of affecting the stability and real-time performance of the stacker core control.

[0003] In order to get rid of the dependence on the internal data of the stacker, independent sensors are used for autonomous positioning as an alternative. Common technical means include laser distance sensors and inertial sensors. Laser distance sensors have high precision and fast response, and realize absolute positioning by measuring the distance to a fixed reflection point, but their measurement depends on the optical path and is easily affected by environmental dust, temporary obstruction and uneven reflection surface material, resulting in irregular fluctuations or instantaneous misalignment of data. Inertial sensors (such as MEMS gyroscopes and accelerometers) obtain displacement by integrating acceleration and do not rely on external references, but their measurement noise is large and the integration error will quickly accumulate over time. For cost-sensitive applications, the positioning drift of low-precision inertial sensors within a few seconds may exceed the allowed range, while high-precision fiber or laser gyroscopes are difficult to popularize due to high cost.

[0004] Therefore, in complex industrial scenarios such as warehouse logistics, the existing technical solutions are difficult to achieve independent positioning of the stacker load table with high precision and strong robustness to environmental interference while controlling costs. Developing a technical solution that can effectively fuse the advantages of different sensors and compensate for their respective defects to achieve stable and reliable positioning without stacker communication support has become an urgent technical problem in the field. SUMMARY

[0005] The technical problem to be solved by the present application is how to achieve high-precision and high-robustness real-time positioning of the stacker load table in a complex industrial environment through low-cost sensor fusion technology without relying on the internal communication of the stacker.

[0006] To solve the above technical problems, the technical scheme adopted by the present application is as follows: a stacker cargo platform positioning method based on multi-sensor fusion, comprising:

[0007] At least one pair of laser distance sensors installed on the stacker cargo platform and the column are used to obtain original distance measurement data of the cargo platform relative to the preset fixed reflection point; at the same time, an inertial sensor installed on the cargo platform is used to obtain the motion acceleration data of the cargo platform;

[0008] The two original distance measurement data from the same pair of laser distance sensors are subjected to complementary checking and fusion processing to obtain a set of preferred distance data;

[0009] The original distance measurement data and the intermediate data obtained through fusion processing are processed based on Kalman filtering, and the credibility of the data is evaluated by calculating the variance of the filtered data and the original data to identify the abnormal data segment caused by instantaneous interference;

[0010] The first speed information is calculated based on the preferred distance data, and the second speed information is obtained by integrating the acceleration data of the inertial sensor; the first speed information and the second speed information are fused according to a preset weight rule to obtain fused speed information;

[0011] When the abnormal data segment is detected, the preferred distance data located in the abnormal data segment is predicted and corrected using the fused speed information to obtain the corrected real-time positioning data of the cargo platform.

[0012] Further, the at least one pair of laser distance sensors installed on the stacker cargo platform and the column are used to obtain the original distance measurement data of the cargo platform relative to the preset fixed reflection point, which specifically comprises:

[0013] A pair of horizontal laser distance sensors are arranged at the bottom end of the stacker column and point to the front and rear fixed reflection points along the forward direction of the stacker to obtain the original distance measurement data in the horizontal direction;

[0014] A pair of vertical laser distance sensors are arranged at the bottom of the stacker cargo platform and point to the top and bottom fixed reflection points along the vertical direction to obtain the original distance measurement data in the vertical direction.

[0015] Further, the complementary checking and fusion processing of the two original distance measurement data from the same pair of laser distance sensors to obtain a set of preferred distance data specifically comprises:

[0016] For each pair of laser distance sensors, a theoretical fixed value is determined according to the sum of the two original distance values measured after installation and stabilization;

[0017] For each sampling, record the original measurement value of one sensor as a first original value, and record the difference between the theoretical fixed value and the original measurement value of another sensor as a second original value;

[0018] Based on the first original value and the second original value, subsequent fusion and processing are performed to obtain a set of preferred distance data.

[0019] Further, the Kalman filtering is used to process the original distance measurement data and the intermediate data obtained through fusion processing, and the reliability of the data is evaluated by calculating the variance of the filtered data and the original data.

[0020] The first original value sequence and the second original value sequence generated by the same pair of sensors are respectively subjected to Kalman filtering to obtain corresponding first filtered value sequence and second filtered value sequence;

[0021] The variance of the first filtered value sequence and its corresponding first original value sequence is calculated as a first deviation value, and the variance of the second filtered value sequence and its corresponding second original value sequence is calculated as a second deviation value;

[0022] Based on the size of the first deviation value and the second deviation value, the final preferred distance value is determined, wherein the smaller the deviation value, the higher the weight of the corresponding data.

[0023] Further, based on the size of the first deviation value and the second deviation value, the final preferred distance value is determined, which specifically includes:

[0024] When the first deviation value is zero, the filtered value corresponding to the first original value is taken as the preferred distance value;

[0025] Otherwise, the reciprocal absolute values of the first deviation value and the second deviation value are respectively taken as the weights of the filtered values corresponding to the first original value and the second original value, and a weighted average value is calculated as the preferred distance value.

[0026] Further, the identification of the data abnormal segment caused by instantaneous interference includes:

[0027] A time window length and a variance threshold are set;

[0028] A data segment at the end of the filtered value sequence with a length equal to the time window length is checked, and the variance of the filtered value and the original value in the data segment is calculated;

[0029] If the variance is greater than the variance threshold, the data segment is determined to be an unreliable data abnormal segment caused by instantaneous interference.

[0030] Further, the first speed information is calculated based on the preferred distance data, and the second speed information is obtained by integrating the acceleration data of the inertial sensor, the first speed information and the second speed information are fused according to a preset weight rule to obtain the fused speed information, and the fused speed information specifically includes:

[0031] The first instantaneous speed is calculated according to the difference between adjacent sampling values of the preferred distance data and the sampling time interval;

[0032] The second instantaneous speed is obtained by integrating the acceleration data collected by the inertial sensor, and the predicted instantaneous speed in a short future time is predicted based on real-time fitting modeling of the acceleration data;

[0033] The first instantaneous speed and the second instantaneous speed or the predicted instantaneous speed are weighted and averaged according to the change trend of the acceleration to obtain the fused speed information, and when the change trend of the acceleration increases, the weight of the first instantaneous speed is increased.

[0034] Further, when the data abnormal segment is detected, the preferred distance data located in the data abnormal segment is predicted and corrected using the fused speed information, and the method specifically includes:

[0035] The last reliable preferred distance data before the start point of the data abnormal segment is taken as a reference value;

[0036] The duration corresponding to the data abnormal segment is integrated using the fused speed information to calculate the predicted displacement in the period;

[0037] The reference value and the predicted displacement are added to generate the corrected positioning data at each time point in the data abnormal segment.

[0038] The application also provides a stack truck loading platform positioning system based on multi-sensor fusion, which is used to realize the positioning method as described above, and the system includes:

[0039] The laser ranging module includes at least one pair of laser distance sensors, and is used to obtain the original distance measurement data of the loading platform;

[0040] The inertial measurement module includes an electronic inertial sensor, and is used to obtain the motion acceleration data of the loading platform;

[0041] The data processing and control module is configured to perform the complementary check and fusion processing, the reliability evaluation and abnormality detection, the speed information fusion and the positioning data correction to output the corrected real-time positioning data.

[0042] Further, the laser ranging module includes a horizontal ranging unit and a vertical ranging unit;

[0043] The horizontal distance measuring unit comprises a pair of horizontal laser distance sensors arranged at the bottom end of the stacker column and fixedly pointing forward and backward along the direction of travel.

[0044] The vertical distance measuring unit comprises a pair of vertical laser distance sensors arranged at the bottom of the stacker loading platform and fixedly pointing to the top and bottom.

[0045] The beneficial effects of the present application are that through the closed-loop technical chain of multi-source data acquisition, complementary verification fusion, credibility evaluation and speed information cooperative correction, the inherent defects of single sensor in complex industrial environment are effectively solved. By using the complementary measurement relationship of the pair of laser sensors, the anti-interference ability and fault tolerance of the original distance data are significantly improved through verification fusion; the dynamic quantitative evaluation of data credibility and the accurate identification of instantaneous interference section are realized by introducing Kalman filter and variance analysis mechanism; the laser data calculated speed and the integrated speed of inertial sensor are weighted and fused, and the weight is dynamically adjusted according to the motion state, forming a high-reliability speed estimation with complementary advantages; finally, the identified abnormal laser positioning data are predicted and corrected by using the fused speed, so that the positioning effect of high precision, high robustness and real-time self-consistency is realized without relying on the communication of the stacker, and the stability and practicability of the system in complex working conditions such as dust shielding and signal fluctuation are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the drawings shown.

[0047] Figure 1 The total flow chart of the stacker loading platform positioning method based on multi-sensor fusion of the embodiment of the present application;

[0048] Figure 2 The specific flow chart of the stacker loading platform positioning method based on multi-sensor fusion of the embodiment of the present application;

[0049] Figure 3 The sensor position diagram of the stacker loading platform in the horizontal direction of the embodiment of the present application;

[0050] Figure 4 The sensor position diagram of the stacker loading platform in the vertical direction of the embodiment of the present application;

[0051] Figure 5The structure diagram of the stacker cargo platform positioning system based on multi-sensor fusion of the embodiment of the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0053] It should be noted that the description of "first", "second" and the like in the present application is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of various embodiments can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it, and when the combination of technical solutions appears to be contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, and is not within the protection scope required by the present application.

[0054] As shown in Figure 1 , the embodiment of the present application is: a stacker cargo platform positioning method based on multi-sensor fusion, the overall steps of the scheme include steps S1-S5, and the specific complete execution steps are as shown in Figure 2 .

[0055] S1, obtaining original distance measurement data of the cargo platform relative to a preset fixed reflection point through at least one pair of laser distance sensors installed on the stacker cargo platform and the column; at the same time, obtaining motion acceleration data of the cargo platform through an inertial sensor installed on the cargo platform.

[0056] In specific embodiments, the original distance measurement data of the cargo platform relative to the preset fixed reflection point obtained by the at least one pair of laser distance sensors installed on the stacker cargo platform and the column specifically includes:

[0057] Obtaining original distance measurement data in the horizontal direction through a pair of horizontal laser distance sensors arranged at the bottom end of the stacker column and pointing to the front and rear fixed reflection points in the forward direction of the stacker;

[0058] Obtaining original distance measurement data in the vertical direction through a pair of vertical laser distance sensors arranged at the bottom of the stacker cargo platform and pointing to the top and bottom fixed reflection points in the vertical direction.

[0059] In the embodiment, as Figure 3 , Figure 4As shown, at the bottom end of the stacker column, along its horizontal travel direction, two laser distance sensors (horizontal distance sensors) are installed in pairs, respectively pointing to the fixed reflection points at the front and rear ends of the track. At the bottom of the loading platform, along the vertical lifting direction, two laser distance sensors (vertical distance sensors) are installed in pairs, respectively pointing to the fixed reflection points at the top rail and the bottom foundation. In theory, the sum of the real-time measurement values of each pair of sensors should be a fixed installation distance constant. A three-axis electronic inertial sensor (IMU) is installed on the loading platform, with its x-axis parallel to the horizontal travel direction of the stacker and its z-axis perpendicular to the ground. When collecting data, the local gravitational acceleration is subtracted from the z-axis raw reading to zero, so that the x-axis and z-axis readings directly correspond to the horizontal and vertical accelerations, respectively.

[0060] S2, complementary checking and fusion processing of the two original distance measurement data from the same pair of laser distance sensors to obtain a set of preferred distance data.

[0061] In specific embodiments, the complementary checking and fusion processing of the two original distance measurement data from the same pair of laser distance sensors to obtain a set of preferred distance data specifically includes:

[0062] S21, for each pair of laser distance sensors, determining a theoretical constant value according to the sum of the two original distance values measured after installation and stabilization;

[0063] S22, for each sampling, recording the original measurement value of one sensor as the first original value, and recording the difference between the theoretical constant value and the original measurement value of the other sensor as the second original value;

[0064] S23, based on the first original value and the second original value, subsequent fusion and processing are carried out to obtain a set of preferred distance data.

[0065] In this embodiment, for each pair of laser sensors, taking the horizontal direction as an example:

[0066] Let the front sensor reading be x and the rear sensor reading be y, and the theoretical constant value measured after installation be S (S = x + y).

[0067] In each sampling, x is the first original value. According to the complementary relationship, the second original value x' can be calculated from y as x' = S - y.

[0068] Ideally, x and x' should be equal, but due to interference, there is a difference between the two. Subsequent processing is based on the x sequence and the x' sequence.

[0069] S3, performing processing on the original distance measurement data and the intermediate data obtained through fusion processing based on Kalman filtering, and evaluating the reliability of the data by calculating the variance of the filtered data and the original data to identify data abnormal sections caused by instantaneous interference.

[0070] In specific embodiments, the processing on the original distance measurement data and the intermediate data obtained through fusion processing based on Kalman filtering, and the evaluation of the reliability of the data by calculating the variance of the filtered data and the original data specifically include:

[0071] S31, performing Kalman filtering on the first original value sequence and the second original value sequence generated by the same pair of sensors respectively to obtain corresponding first filtered value sequence and second filtered value sequence;

[0072] S32, calculating the variance of the first filtered value sequence and the first original value sequence corresponding thereto as a first deviation value, and calculating the variance of the second filtered value sequence and the second original value sequence corresponding thereto as a second deviation value;

[0073] S33, determining the preferred distance value finally adopted based on the sizes of the first deviation value and the second deviation value, wherein the smaller the deviation value is, the higher the weight of the corresponding data is.

[0074] In specific embodiments, the determination of the preferred distance value finally adopted based on the sizes of the first deviation value and the second deviation value specifically includes:

[0075] When the first deviation value is zero, the filtered value corresponding to the first original value is adopted as the preferred distance value;

[0076] Otherwise, the reciprocal absolute values of the first deviation value and the second deviation value are respectively taken as the weights of the filtered values corresponding to the first original value and the second original value, and a weighted average value is calculated as the preferred distance value.

[0077] In this embodiment, a fixed-length sliding window is used to perform Kalman filtering on the x sequence and the x' sequence respectively to obtain filtered sequences x_f and x'_f.

[0078] The variance of the x_f sequence and the x original sequence is calculated as a first deviation value D_x, reflecting the degree of continuous interference on the x data.

[0079] Similarly, the variance of the x'_f sequence and the x' original sequence is calculated as a second deviation value D_x'.

[0080] The smaller the deviation value is, the smaller the interference on the road data is, and the more reliable it is.

[0081] If D_x is zero, the end value of x_f is directly taken as the current preferred distance L_opt. Otherwise, the end values of x_f and x'_f are weighted averaged with weights of 1 / |D_x| and 1 / |D_x'|, and the result is taken as L_opt.

[0082] In specific embodiments, the identifying the data abnormal segment caused by the instantaneous interference comprises:

[0083] S34, setting a time window length and a variance threshold;

[0084] S35, checking a data segment at the end of the filtered value sequence and having a length equal to the time window length, and calculating a variance of the filtered value and the original value in the data segment;

[0085] S36, if the variance is greater than the variance threshold, determining that the data segment is an untrusted data abnormal segment caused by the instantaneous interference.

[0086] In the embodiment, since the instantaneous interference (such as a floating particle) can affect the last several measurements, the end untrusted data needs to be identified. The specific process of identification is as follows:

[0087] Setting parameters: end check data length d, and variance threshold dg.

[0088] Checking d data at the end of the x_f sequence, and calculating a variance Var_end of the d x_f values and corresponding x original values.

[0089] If Var_end>dg, determining that the last d data constitute an abnormal segment, and the L_opt value in the last d / p seconds (p is a sampling rate) cannot be directly adopted. At this time, the time point of the last trusted L_opt value lags behind the current time by d / p seconds.

[0090] S4, calculating first speed information based on the preferred distance data, integrating acceleration data of the inertial sensor to obtain second speed information, fusing the first speed information and the second speed information according to a preset weight rule, and obtaining fused speed information.

[0091] In specific embodiments, the calculating first speed information based on the preferred distance data, integrating acceleration data of the inertial sensor to obtain second speed information, fusing the first speed information and the second speed information according to a preset weight rule, and obtaining fused speed information specifically comprises:

[0092] S41, calculating first instantaneous speed according to a difference between adjacent sampling values of the preferred distance data and a sampling time interval;

[0093] S42, integrate the acceleration data collected by the inertial sensor to obtain a second instantaneous speed, and predict a predicted instantaneous speed in a short future time based on real-time fitting modeling of the acceleration data;

[0094] S43, set a dynamic weight according to the change trend of the acceleration, and perform weighted averaging on the first instantaneous speed and the second instantaneous speed or the predicted instantaneous speed to obtain the fused speed information, wherein when the acceleration change trend increases, the weight of the first instantaneous speed is increased.

[0095] In the embodiment, the inertial sensor data is used to compensate for the absence or unavailability of laser data in the abnormal section. Specifically, it includes:

[0096] Laser speed calculation: according to the continuous L_opt value, the displacement difference between adjacent sampling points is calculated and divided by time to obtain the first instantaneous speed V_laser based on laser.

[0097] Inertial speed calculation and prediction: integrate the acceleration data (corresponding direction) collected by the IMU to obtain the second instantaneous speed V_imu. At the same time, real-time curve fitting (such as polynomial fitting) is performed on the acceleration sequence, which can predict the acceleration trend in a short future time, and then integrate to obtain the predicted instantaneous speed V_pred.

[0098] Speed fusion: weighted averaging of V_laser and V_imu (or V_pred) to obtain fused speed V_fused. Dynamic adjustment of weight: when the IMU detects that the acceleration changes smoothly (jerk is small), the weight of V_imu can be appropriately increased; when the acceleration changes sharply, the weight of V_laser should be increased, because the inertial sensor is prone to greater errors when there is a sudden change. The initial weight is set according to the nominal accuracy of the sensor, and the weight of the laser is usually higher.

[0099] S5, when detecting the data abnormal section, using the fused speed information to predict and correct the preferred distance data located in the data abnormal section to obtain the corrected real-time positioning data of the loading platform.

[0100] In specific embodiments, when the data abnormal section is detected, the preferred distance data located in the data abnormal section is predicted and corrected using the fused speed information, specifically including:

[0101] S51, taking the last reliable preferred distance data before the start point of the data abnormal section as a reference value;

[0102] S52, integrating the time corresponding to the data abnormal section using the fused speed information to calculate the predicted displacement in that period;

[0103] S53, adding the reference value and the predicted displacement to generate the corrected positioning data of each time in the data abnormal segment.

[0104] In the embodiment, when the abnormal segment with the length of d is detected, the last credible L_opt value before the start of the abnormal segment is taken as the correction reference L_base, the fusion speed information V_fused in the time period [t_base, t_current] is taken, the fusion speed information V_fused can be a sequence or a function of multiple speed values, the V_fused is integrated in the time period to calculate the predicted displacement ΔL_pred, and finally the corrected real-time positioning value L_corrected = L_base+ ΔL_pred.

[0105] Through the above steps, the corrected real-time two-dimensional coordinates of the loading platform are obtained by calculating the horizontal direction and the vertical direction respectively. Through the ingenious sensor layout and the hierarchical data fusion algorithm, the random interference of the laser sensor and the cumulative error of the inertial sensor are effectively suppressed, and the high-precision and high-reliability autonomous positioning is realized without the communication support of the stacker.

[0106] As shown in Figure 5 The embodiment of the present application also provides a stacker loading platform positioning system based on multi-sensor fusion, which is used to realize the positioning method as described above, and the system comprises a laser ranging module 10, an inertial measurement module 20 and a data processing and control module 30.

[0107] The laser ranging module 10 comprises at least one pair of laser distance sensors, which are used to obtain the original distance measurement data of the loading platform.

[0108] In specific embodiments, the laser ranging module 10 comprises a horizontal ranging unit and a vertical ranging unit.

[0109] The horizontal ranging unit comprises a pair of horizontal laser distance sensors arranged at the bottom end of the stacker column and fixedly pointing to the front and rear reflection points along the running direction.

[0110] The vertical ranging unit comprises a pair of vertical laser distance sensors arranged at the bottom of the loading platform of the stacker and fixedly pointing to the top and bottom reflection points along the vertical direction.

[0111] The inertial measurement module 20 comprises an electronic inertial sensor, which is used to obtain the motion acceleration data of the loading platform.

[0112] The data processing and control module 30 is configured to perform the complementary check and fusion processing, the credibility evaluation and abnormality detection, the speed information fusion and the positioning data correction, so as to output the corrected real-time positioning data.

[0113] In specific embodiments, the data processing and control module 30 is configured to perform complementary check and fusion processing, credibility assessment and anomaly detection, speed information fusion and positioning data correction to output corrected real-time positioning data, in particular as follows:

[0114] In specific embodiments, the data processing and control module 30 is configured to perform complementary check and fusion processing in particular includes:

[0115] For each pair of laser distance sensors, a theoretical fixed value is determined according to the sum of the two original distance values measured after installation and stabilization;

[0116] For each sampling, the original measurement value of one sensor is recorded as a first original value, and the difference between the theoretical fixed value and the original measurement value of the other sensor is recorded as a second original value;

[0117] Based on the first original value and the second original value, subsequent fusion and processing are performed to obtain a set of preferred distance data.

[0118] In specific embodiments, the data processing and control module 30 is configured to perform credibility assessment in particular includes:

[0119] The first original value sequence and the second original value sequence generated by the same pair of sensors are respectively subjected to Kalman filtering to obtain corresponding first filtered value sequences and second filtered value sequences;

[0120] The variance of the first filtered value sequence and its corresponding first original value sequence is calculated as a first deviation value, and the variance of the second filtered value sequence and its corresponding second original value sequence is calculated as a second deviation value;

[0121] Based on the size of the first deviation value and the second deviation value, the final preferred distance value is determined, wherein the smaller the deviation value, the higher the weight of the corresponding data.

[0122] Specifically, the determination of the final preferred distance value based on the size of the first deviation value and the second deviation value specifically includes:

[0123] When the first deviation value is zero, the filtered value corresponding to the first original value is taken as the preferred distance value;

[0124] Otherwise, the reciprocal absolute values of the first deviation value and the second deviation value are respectively taken as the weights of the filtered values corresponding to the first original value and the second original value, and the weighted average value is calculated as the preferred distance value.

[0125] In specific embodiments, the data processing and control module 30 is configured to perform anomaly detection, which includes:

[0126] A time window length and a variance threshold are set;

[0127] checking a data segment at the end of the filter value sequence and having a length equal to the length of the time window, calculating a variance of the filter values and the original values in the data segment;

[0128] if the variance is greater than the variance threshold, determining that the data segment is an abnormal segment of untrusted data caused by instantaneous interference.

[0129] In specific embodiments, the data processing and control module 30 is configured to perform speed information fusion, specifically comprising:

[0130] calculating a first instantaneous speed according to the difference between adjacent sampling values of the preferred distance data and the sampling time interval;

[0131] integrating acceleration data collected by the inertial sensor to obtain a second instantaneous speed, and predicting a predicted instantaneous speed in a short future time based on real-time fitting modeling of the acceleration data;

[0132] setting a dynamic weight according to the change trend of the acceleration, and performing weighted average on the first instantaneous speed and the second instantaneous speed or the predicted instantaneous speed to obtain the fusion speed information, wherein when the change trend of the acceleration increases, the weight of the first instantaneous speed is increased.

[0133] In specific embodiments, the data processing and control module 30 is configured to perform positioning data correction, specifically comprising:

[0134] taking the preferred distance data before the start point of the data abnormal segment as a reference value;

[0135] integrating the duration corresponding to the data abnormal segment using the fusion speed information to calculate the predicted displacement in the period;

[0136] adding the reference value and the predicted displacement to generate corrected positioning data at each time in the data abnormal segment.

[0137] The above only describes the embodiments of the present application, and does not limit the patent scope of the present application, any equivalent structure or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A stacker crane loading platform positioning method based on multi-sensor fusion, characterized in that, include: At least one pair of laser distance sensors installed on the stacker crane's loading platform and columns are used to obtain the original distance measurement data of the loading platform relative to a preset fixed reflection point; at the same time, the motion acceleration data of the loading platform is obtained by an inertial sensor installed on the loading platform. For each pair of laser distance sensors, a theoretical constant is determined based on the sum of the two original distance values ​​measured after installation and stabilization; For each sampling, the original measurement value of one sensor is recorded as the first original value, and the difference between the theoretical set value and the original measurement value of the other sensor is recorded as the second original value. Based on the first and second original values, subsequent fusion and processing are performed to obtain a set of preferred distance data; Kalman filtering is performed on the first and second raw value sequences generated by the same pair of sensors to obtain the corresponding first and second filtered value sequences. The variance of the first filtered value sequence and its corresponding first original value sequence is calculated as the first deviation value, and the variance of the second filtered value sequence and its corresponding second original value sequence is calculated as the second deviation value. Based on the magnitude of the first deviation value and the second deviation value, the preferred distance value for final acceptance is determined, wherein the smaller the deviation value, the higher the weight of the corresponding data, and abnormal data segments caused by instantaneous interference are identified. First velocity information is calculated based on the preferred distance data, and second velocity information is obtained by integrating the acceleration data of the inertial sensor. The first velocity information and the second velocity information are then fused according to a preset weighting rule to obtain fused velocity information. When an abnormal data segment is detected, the fusion speed information is used to predict and correct the preferred distance data located within the abnormal data segment, so as to obtain the corrected real-time positioning data of the loading platform. The identification of abnormal data segments caused by transient interference includes: Set a time window length and a variance threshold; Examine the data segment at the end of the filtered value sequence, whose length is equal to the length of the time window, and calculate the variance between the filtered value and the original value within this data segment; If the variance is greater than the variance threshold, the data segment is determined to be an unreliable data anomaly segment caused by transient interference.

2. The stacker crane loading platform positioning method based on multi-sensor fusion according to claim 1, characterized in that, The process of acquiring the original distance measurement data of the loading platform relative to a preset fixed reflection point through at least one pair of laser distance sensors installed on the loading platform and column of the stacker crane specifically includes: The raw distance measurement data in the horizontal direction is obtained by using a pair of horizontal laser distance sensors that are fixed at the bottom of the stacker crane columns and point to the front and rear ends along the direction of the stacker crane's travel. The raw distance measurement data in the vertical direction is obtained by using a pair of vertical laser distance sensors installed at the bottom of the stacker crane's loading platform, pointing vertically to the top and bottom fixed reflection points.

3. The stacker crane loading platform positioning method based on multi-sensor fusion according to claim 1, characterized in that, The process of determining the preferred distance value for final acceptance based on the magnitudes of the first and second deviation values ​​specifically includes: When the first deviation value is zero, the filtered value corresponding to the first original value is adopted as the preferred distance value; Otherwise, the reciprocal absolute values ​​of the first deviation value and the second deviation value are used as the weights of the filter values ​​corresponding to the first original value and the second original value, respectively, and the weighted average value is calculated as the preferred distance value.

4. The stacker crane loading platform positioning method based on multi-sensor fusion according to claim 1, characterized in that, The process of calculating first velocity information based on the preferred distance data, integrating the acceleration data from the inertial sensor to obtain second velocity information, and fusing the first and second velocity information according to a preset weighting rule to obtain fused velocity information specifically includes: The first instantaneous velocity is calculated based on the difference between adjacent sample values ​​of the preferred distance data and the sampling time interval; The acceleration data collected by the inertial sensor is integrated to obtain the second instantaneous velocity, and the predicted instantaneous velocity in the near future is predicted based on the real-time fitting model of the acceleration data. Dynamic weights are set according to the acceleration change trend, and the first instantaneous velocity and the second instantaneous velocity or the predicted instantaneous velocity are weighted and averaged to obtain the fused velocity information. When the acceleration change trend increases, the weight of the first instantaneous velocity is increased.

5. The stacker crane loading platform positioning method based on multi-sensor fusion according to claim 4, characterized in that, The step of using the fusion speed information to predict and correct the preferred distance data located within the abnormal data segment when an abnormal data segment is detected specifically includes: The reliable preferred distance data preceding the starting point of the data anomaly segment is used as the reference value; The predicted displacement within the time period corresponding to the data anomaly segment is calculated by integrating the fusion speed information. The reference value is added to the predicted displacement to generate corrected positioning data for each time point within the data anomaly segment.

6. A stacker crane loading platform positioning system based on multi-sensor fusion, characterized in that, The system for implementing the positioning method according to any one of claims 1-5, the system comprising: The laser ranging module includes at least one pair of laser distance sensors for acquiring raw distance measurement data of the loading platform; An inertial measurement module, including electronic inertial sensors, is used to acquire motion acceleration data of the loading platform; The data processing and control module is configured to perform complementary verification and fusion processing, credibility assessment and anomaly detection, speed information fusion and positioning data correction, so as to output corrected real-time positioning data.

7. The stacker crane loading platform positioning system based on multi-sensor fusion according to claim 6, characterized in that, The laser ranging module includes a horizontal ranging unit and a vertical ranging unit; The horizontal ranging unit includes a pair of horizontal laser distance sensors installed at the bottom of the stacker crane column and pointing towards fixed reflection points along the direction of travel; The vertical ranging unit includes a pair of vertical laser distance sensors installed at the bottom of the stacker crane's loading platform, pointing vertically to fixed reflection points at the top and bottom.

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