Gimbal sensor deviation calibration method and system based on multi-axis motion coupling characteristics

Abstract

The application provides a gimbal sensor deviation calibration method and system based on multi-axis motion coupling characteristics. The method comprises the following steps: S1, obtaining original data of a gimbal sensor and performing filtering processing; S2, selecting one axis of the gimbal as a motion axis and the other two axes as calibration axes, performing open-loop control on the motion axis to reciprocate, and recording maximum and minimum values of first digital output of the calibration axes in a motion period; S3, calculating an average value difference of the extreme values of the first digital output in multiple motion periods, converting the average value difference into an analog quantity to obtain an average angular velocity error quantity, and calculating a deviation angle of the calibration axes according to the average angular velocity error quantity and the motion period; S4, constructing a direction cosine matrix and an error compensation rotation matrix of the calibration axes according to the deviation angle; and S5, obtaining original vectors of three-axis gyroscopes, performing matrix multiplication operation on the original vectors and the error compensation rotation matrix, outputting a calibration angular velocity, and performing calibration. The application improves dynamic stability precision of the gimbal.

Description

Technical Field

[0001] This application relates to the field of gimbal sensor technology, specifically to a gimbal sensor deviation calibration method and system based on multi-axis motion coupling characteristics. Background Technology

[0002] In the gimbal control system, the installation accuracy of the inertial measurement unit (IMU) directly determines the quality of attitude fusion. However, due to the PCB welding stress and bracket assembly tolerance, there is generally a three-axis spatial angle between the IMU coordinate axis and the gimbal mechanical axis, which causes severe cross-axis coupling interference when the gimbal performs a single-axis movement.

[0003] Existing calibration schemes mostly focus on static zero-bias calibration, which not only cannot correct spatial mapping errors during dynamic rotation, but also lack effective cross-axis feature extraction algorithms, making it difficult to identify physical characteristics such as "abnormal yaw axis output generated by pitch motion is actually a manifestation of roll axis tilt angle". This results in insufficient fitting ability of the calibration logic in regions with drastic error fluctuations (such as large stroke motion or near poles).

[0004] To meet the requirements for high-precision stabilization, an automatic calibration method is needed that can perform dynamic scanning using the motion characteristics of the gimbal itself and achieve precise decoupling of spatial coupling through axial mapping logic. Summary of the Invention

[0005] To address the aforementioned technical issues, this application proposes a gimbal sensor deviation calibration method and system based on multi-axis motion coupling characteristics.

[0006] According to a first aspect of this application, a gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics is proposed, the method comprising the following steps: S1. Obtain the raw data of the current gimbal sensor and filter the raw data, wherein the raw data includes the first digital output of the gyroscope about the angular velocity and the second digital output of the magnetic encoder; S2. Select one axis of the gimbal as the motion axis and the other two axes as calibration axes. Control the motion axis to reciprocate along a preset trajectory in an open loop and record the maximum and minimum values ​​of the first digital output of the calibration axis within one reciprocating motion cycle. S3. Calculate the average extreme value of the first digital output of the calibration axis over multiple motion cycles, and calculate the difference between the extreme values. Convert the difference between the extreme values ​​into an analog quantity to obtain the average angular velocity error. Calculate the deviation angle of the calibration axis based on the average angular velocity error and the motion cycle. S4. Based on the deviation angle, construct the direction cosine matrix of the calibration axis, and based on the spatial three-dimensional rotation formula of the direction cosine matrix, construct the error compensation rotation matrix; S5. Obtain the original vector of the three-axis gyroscope, perform matrix multiplication between the error compensation rotation matrix and the original vector of the three-axis gyroscope, output the spatially decoupled calibration angular velocity, and calibrate the calibration axis.

[0007] Preferably, the method further includes the step of: S6. Re-control the motion axis to perform the reciprocating motion of step S2, and monitor the change amplitude of the magnetic encoder of the calibration axis. When it is determined that the change amplitude is greater than a preset threshold, change the compensation direction of the deviation angle in step S3.

[0008] Preferably, the filtering process on the original data specifically includes: Construct a data sliding window; The acquired raw data is enqueued, and the raw data in the data sliding window is sorted using the bubble sort method to obtain multiple arrays; The median value of each array is taken as the effective output value after filtering.

[0009] Preferably, step S2 specifically includes: The motion axis is controlled to rotate uniformly from 0° to 90° in an open-loop manner, and the one-way motion time t1 is recorded. The motion axis is controlled to return from 90° to 0° at a constant speed using open-loop control, and the one-way motion time t2 is recorded. Repeat the exercise for N cycles, and discard the data from the first cycle.

[0010] Preferably, in step S3, calculating the deviation angle of the calibration axis based on the average angular velocity error and the motion period specifically includes: Calculate the average one-way motion time t3 over N-1 effective motion cycles; The deviation angle of the calibration axis is calculated by integrating the average angular velocity error with the average single-journey motion time t3.

[0011] Preferably, the motion axis is the pitch axis, and the calibration axis includes the roll axis and the yaw axis. Calculating the actual deviation angle of the calibration axis specifically includes: The deviation angle of the roll axis obtained by integral calculation is mapped to the actual deviation angle of the heading axis; The deviation angle of the heading axis obtained by integral calculation is mapped to the actual deviation angle of the roll axis.

[0012] Preferably, step S4 specifically includes: The sine and cosine values ​​of the actual deviation angle are calculated using trigonometric functions, and the direction cosine matrices of the roll axis and the heading axis are constructed as follows: Wherein, α and γ are the actual deviation angles of the roll axis and the yaw axis, respectively. Rx ( α ), Rz ( γ ) are the direction cosine matrices of the roll axis and the heading axis, respectively; Perform matrix multiplication on the direction cosine matrices of the roll axis and the yaw axis to obtain the error-compensated rotation matrix. R ,as follows: .

[0013] According to a second aspect of this application, a gimbal sensor deviation calibration system based on multi-axis motion coupling characteristics is proposed, comprising: The data processing unit is configured to acquire the raw data of the current gimbal sensor and perform filtering processing on the raw data, wherein the raw data includes the first digital output of the gyroscope with respect to angular velocity and the second digital output of the magnetic encoder; The feature extraction unit is configured to select one axis of the gimbal as the motion axis and the other two axes as calibration axes, and to control the motion axis to reciprocate along a preset trajectory in an open loop, and to record the maximum and minimum values ​​of the first digital output of the calibration axis within one reciprocating motion cycle. The deviation angle calculation unit is configured to calculate the extreme average value of the first digital output of the calibration axis over multiple motion cycles, calculate the extreme average value difference, convert the extreme average value difference into an analog quantity to obtain the average angular velocity error, and calculate the deviation angle of the calibration axis based on the average angular velocity error and the motion cycle. An error compensation unit is configured to construct a direction cosine matrix of the calibration axis based on the deviation angle, and to construct an error compensation rotation matrix based on the spatial three-dimensional rotation formula of the direction cosine matrix. An angle calibration unit is configured to acquire the original vector of the three-axis gyroscope, perform matrix multiplication between the error compensation rotation matrix and the original vector of the three-axis gyroscope, output the spatially decoupled calibration angular velocity, and calibrate the calibration axis.

[0014] According to a third aspect of this application, an IMU is proposed that uses a gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics as provided in any of the embodiments of the first aspect above.

[0015] According to a fourth aspect of this application, a gimbal is provided, the gimbal being equipped with a controller, a magnetic encoder, and an IMU as provided in the third aspect above, the controller being used to execute a gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics as provided in any embodiment of the first aspect above.

[0016] This application proposes a gimbal sensor deviation calibration method and system based on multi-axis motion coupling characteristics. The error compensation model is upgraded from the traditional single-axis static linear model to a three-dimensional spatial rotation matrix reconstruction based on dynamic characteristics. By dynamically sweeping and integrating the angular velocity over time on the preset trajectory of the pitch axis motion, and combining the axial mapping interchange logic between the roll axis and the yaw axis, the cross-axis coupling component caused by physical installation tilt (rather than simple electrical signal zero bias) is accurately extracted. This allows the gimbal control system to strictly follow the physical laws of three-dimensional spatial rotation, fundamentally eliminating non-target axis interference (such as roll / yaw drift caused by pitch motion) under large stroke or high-speed motion, and significantly improving the dynamic stabilization accuracy of the gimbal. Attached Figure Description

[0017] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and, together with the description, serve to explain the principles of the invention. Other embodiments and many anticipated advantages of the embodiments will be readily recognized as they become better understood through reference to the following detailed description. Elements in the drawings are not necessarily to scale. The same reference numerals refer to corresponding similar parts.

[0018] Figure 1 This is a flowchart of a gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics according to an embodiment of this application; Figure 2 This is a schematic diagram of a gimbal sensor deviation calibration system based on multi-axis motion coupling characteristics according to an embodiment of this application. Detailed Implementation

[0019] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the invention.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0021] In existing technologies, the correction of IMU (Inertial Measurement Unit) installation deviations mainly employs static compensation and linear coefficient mapping methods: Static zero-bias calibration: When the gimbal is stationary, the raw output values ​​of the gyroscope in the three-axis direction are collected, and the average value over a certain period of time is calculated as the static bias, which is then directly subtracted from the raw data in subsequent operations.

[0022] Linear gain and fixed axis mapping: Assuming the installation deviation is a fixed linear ratio, we attempt to align the IMU coordinate system with the machine coordinate system by using a preset fixed coordinate transformation coefficient.

[0023] However, existing solutions still have the following drawbacks: Low accuracy of spatial coupling fitting: Existing technologies use linear compensation with independent channels, which cannot simulate the spatial rotation projection of the IMU under non-ideal installation conditions. In areas with large error gradients, such as long strokes or high-speed turns, it cannot correct attitude drift caused by inter-axis coupling.

[0024] The calibration reference has dynamic errors: most existing solutions are calibrated in a static state, which cannot reflect the mechanical vibration and centrifugal force deviation during operation. If an uncorrected magnetic encoder is used as the position reference to record error points, the nonlinear error of the magnetic encoder itself (such as side-mounted distortion) will be superimposed on the IMU sampling data, resulting in a reference deviation in the compensation calculation.

[0025] The conflict between storage consumption and real-time performance: If accuracy is improved by increasing sampling points (lookup table method), the high-dimensional error point table generated by three-axis coupling will occupy a large amount of storage space. At the same time, the high-frequency lookup and interpolation calculations during real-time operation will significantly increase the CPU load, making it difficult to meet the real-time requirements of the gimbal system for high control frequency.

[0026] Therefore, there is a need to provide a gimbal sensor deviation calibration method that can ensure calibration accuracy while greatly reducing storage footprint and real-time computing load.

[0027] This application proposes a gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics. Figure 1 A flowchart of a gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics according to an embodiment of this application is shown, as follows: Figure 1 As shown, the method includes the following steps: Step S1: Obtain the raw data of the current gimbal sensor and filter the raw data. The raw data includes the first digital output of the gyroscope with respect to angular velocity and the second digital output of the magnetic encoder.

[0028] Specifically, the gimbal sensor includes an IMU and a magnetic encoder. The raw data includes the first digital output about angular velocity directly output by the gyroscope chip of the IMU, and the second digital output about angle directly output by the magnetic encoder chip.

[0029] In some embodiments, the filtering process of the raw data includes the following steps: Construct a data sliding window with a depth of k points, where k is an integer greater than 1; The acquired raw data is enqueued, and the raw data in the data sliding window is sorted using the bubble sort method to obtain multiple arrays; The median value of each array is taken as the effective output value after filtering.

[0030] The above filtering process can remove pulse spike noise generated by high-frequency vibration of the gimbal motor and electromagnetic interference, ensuring the stability and accuracy of the output.

[0031] Step S2: Select one axis of the gimbal as the motion axis and the other two axes as calibration axes. Control the motion axis to reciprocate along a preset trajectory in an open loop, and record the maximum and minimum values ​​of the first digital output of the calibration axis within one reciprocating motion cycle.

[0032] Specifically, this application aims to extract the angular velocity components of non-motion axes and identify the physical nature of spatial deflection by dynamically scanning any one of the three motion axes of the gimbal according to a preset trajectory. Therefore, the motion axis can be any one of the pitch, roll, and yaw axes, while the non-motion axes (i.e., calibration axes) correspond to the other two axes.

[0033] In some embodiments, since the effect of the pitch axis driving the other two axes to move when it rotates is more obvious, the pitch axis is selected as the motion axis in this embodiment, while the roll axis and yaw axis are used as calibration axes.

[0034] In some embodiments, performing dynamic sweep motion on the motion axis includes the following steps: The motion axis is controlled to rotate uniformly from 0° to 90° in an open-loop manner, and the one-way motion time t1 is recorded. The open-loop control axis returns to 0° at a constant speed from 90°, and the one-way motion time t2 is recorded; Repeat the above steps for a total of N motion cycles, and discard the data from the first motion cycle to eliminate motor starting resistance and initial steady-state deviation. The cumulative effective motion cycles are N-1.

[0035] In some embodiments, when performing dynamic sweep motion on the motion axis, feature extremum extraction of the calibration axis includes the following steps: During the aforementioned dynamic sweep motion, the first digital output of the gyroscope on the calibration axes (roll axis and yaw axis) is read in real time after filtering; The maximum and minimum values ​​of the first digital output of the calibration axis gyroscope are continuously compared and recorded during each motion cycle.

[0036] Step S3: Calculate the average extreme values ​​of the first digital output of the calibration axis over multiple motion cycles, and calculate the difference between the extreme values. Convert the difference between the extreme values ​​into an analog quantity to obtain the average angular velocity error. Calculate the deviation angle of the calibration axis based on the average angular velocity error and the motion cycle.

[0037] In some embodiments, the average extreme values ​​of the first digital output of the calibration axis over multiple motion cycles are calculated, and the difference between the extreme values ​​is calculated. The difference between the extreme values ​​is then converted into an analog quantity to obtain the average angular velocity error. Specifically, this includes: The extreme value data of the first digital output within N-1 effective motion cycles are sorted, and the extreme value mean (i.e., the mean of the maximum and the mean of the minimum) is calculated for each extreme value to obtain stable upper and lower limits, thus eliminating random errors from a single acquisition. Then, the extreme value mean difference (i.e., the difference between the mean of the maximum and the mean of the minimum) is calculated. Based on the gyroscope's range and resolution, the extreme value mean difference is converted from a digital quantity to an analog quantity (unit: degrees / second, i.e., angular velocity) to obtain the average angular velocity error. The conversion formula is as follows: Where ω is the average angular velocity error, Range is the difference between extreme values ​​and the mean, LSB is the sensitivity coefficient, FSR is the gyroscope range, and N indicates an N-bit gyroscope. N This indicates the full-scale encoding.

[0038] In this embodiment, taking a 16-bit gyroscope with a range of ±500 dps as an example, the formula for calculating the average angular velocity error is as follows: .

[0039] In some embodiments, the deviation angle of the calibration axis is calculated based on the average angular velocity error and the motion period, specifically including: Calculate the average single-journey time t3 for each exercise cycle, and then calculate the average single-journey time T for N-1 effective exercise cycles; The deviation angle of the calibration axis is calculated by integrating the average angular velocity error with the average single-journey motion time T.

[0040] Therefore, the error extraction in this embodiment is changed from static zero bias to dynamic sweep and time integration, which eliminates the deviation between mechanical vibration and static calibration reference and improves calibration accuracy.

[0041] Furthermore, in the three-dimensional orthogonal system of the gimbal, the "installation angle" of a certain axis of the gimbal sensor is essentially a rotation of the coordinate system around that axis. During pitch axis movement, when the roll axis deflects, the sensor rotates around the roll axis. The roll axis itself remains unchanged in direction, but this causes the yaw axis to tilt and enter the trajectory of the pitch axis. Therefore, the angular velocity of the pitch axis will be incorrectly projected onto the yaw axis. Similarly, when the yaw axis deflects, the sensor rotates around the yaw axis. The yaw axis itself remains unchanged in direction, but this causes the roll axis to tilt and enter the trajectory of the pitch axis. Therefore, the angular velocity of the pitch axis will be incorrectly projected onto the roll axis.

[0042] Therefore, in some embodiments, after calculating the deviation angle of the calibration axis by integrating the average angular velocity error with the average single-journey travel time T, the deviation angle of the roll axis calculated by integration is mapped to the actual deviation angle of the heading axis, and the deviation angle of the heading axis calculated by integration is mapped to the actual deviation angle of the roll axis.

[0043] Therefore, through the above-mentioned cross-axis interchange mapping logic, the accurate correction of multi-axis spatial coupling error can be achieved.

[0044] Step S4: Based on the deviation angle, construct the direction cosine matrix of the calibration axis, and construct the error compensation rotation matrix based on the spatial three-dimensional rotation formula of the direction cosine matrix.

[0045] In some embodiments, after performing cross-axis interchange mapping on the deviation angles of the roll axis and the yaw axis, the sine and cosine values ​​of the actual deviation angles of the roll axis and the yaw axis are calculated using trigonometric functions to construct the direction cosine matrices of the roll axis and the yaw axis, respectively. The specific formulas are as follows: Where α and γ are the actual deviation angles of the roll axis and yaw axis, respectively. Rx ( α ), Rz ( γ ) are the direction cosine matrices of the roll axis and the heading axis, respectively.

[0046] Perform matrix multiplication on the direction cosine matrices of the roll axis and yaw axis to obtain the error-compensated rotation matrix. R The specific formula is as follows: .

[0047] Step S5: Obtain the original vector of the three-axis gyroscope, perform matrix multiplication between the error compensation rotation matrix and the original vector of the three-axis gyroscope, output the spatially decoupled calibration angular velocity, and calibrate the calibration axis.

[0048] In some embodiments, the original vector of the three-axis gyroscope is obtained: in, ω x , ω y , ω z These are the original vectors for the roll axis, pitch axis, and yaw axis, respectively.

[0049] Error compensation rotation matrix R With the original vector of the three-axis gyroscope Perform matrix multiplication to directly output the calibrated angular velocities of the roll and yaw axes after spatial decoupling. , Then, the angular velocities of the roll axis and yaw axis are calibrated.

[0050] The rotation matrix reconstruction compensation method based on global deflection angles adopted in this embodiment only requires the extraction and storage of a very small number of deflection angle variables (i.e., the two actual global deviation angles of the roll axis and the yaw axis, occupying only a few bytes) to cover the 360° full-range spatial transformation. This saves a significant amount of MCU Flash storage space without sacrificing or even improving compensation accuracy. At the same time, only one basic 3×3 rotation matrix multiplication needs to be performed in real-time control, eliminating the high-frequency lookup and interpolation operations of traditional lookup calibration methods, greatly freeing up the MCU's computing power and ensuring the real-time performance of the system's kilohertz-level control.

[0051] Step S6: Re-control the motion axis to perform the reciprocating motion of step S2, and monitor the change amplitude of the magnetic encoder of the calibration axis. When it is determined that the change amplitude is greater than the preset threshold, change the compensation direction of the deviation angle in step S3.

[0052] In some embodiments, the calibration axis is induced to deflect due to the movement of the motion axis. If the change amplitude of the magnetic encoder of the calibration axis is detected to be relatively large, it indicates that the initial installation angle of the calibration axis has crossed the quadrant (e.g., from 359° to 1°). At this time, by changing the compensation direction of the actual deviation angle of the calibration axis, that is, by modifying the sign of the actual deviation angle of the roll axis and the yaw axis respectively, the secondary calibration of the deflection angle can be achieved.

[0053] Since the calibration axis has a certain allowable deviation after calibration, it is necessary to determine whether the deviation is amplified due to the compensation direction being reversed, thus causing obvious jitter. This requires testing and analyzing the range of preset threshold values. Specifically: Table 1: Offset Test Analysis Table As shown in Table 1 above, the offset test analysis table shows that 15 groups of samples were tested. In groups 3 and 5, where the magnetic encoder variation was 1.75° or greater, the calibration axis exhibited significant offset. In contrast, the calibration axis of samples with a variation of less than 1.75° remained normal. Therefore, the preset threshold value should be less than 1.75°.

[0054] Considering the discrepancies between different gimbals and the existence of blank samples around 1.75°, a trial-and-error method was used to continuously fine-tune the preset threshold to find a relatively ideal critical value. Ultimately, in this embodiment, the preset threshold was set to 1.72°. It is understood that in other embodiments, the preset threshold could also be other values ​​less than 1.75° but close to 1.75°; this is not a limitation here.

[0055] Based on the above-mentioned gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics, and based on the same inventive concept, this application also proposes a gimbal sensor deviation calibration system based on multi-axis motion coupling characteristics. Figure 2 A schematic diagram of a gimbal sensor deviation calibration system based on multi-axis motion coupling characteristics according to an embodiment of this application is shown, such as... Figure 2 As shown, the system includes: The data processing unit 10 is configured to acquire the raw data of the current gimbal sensor and perform filtering processing on the raw data. The raw data includes the first digital output of the gyroscope with respect to angular velocity and the second digital output of the magnetic encoder.

[0056] The feature extraction unit 20 is configured to select one axis of the gimbal as the motion axis and the other two axes as calibration axes, and to control the motion axis to reciprocate along a preset trajectory in an open loop, and to record the maximum and minimum values ​​of the first digital output of the calibration axis within one reciprocating motion cycle.

[0057] The deviation angle calculation unit 30 is configured to calculate the extreme average value of the first digital output of the calibration axis over multiple motion cycles, calculate the extreme average value difference, convert the extreme average value difference into an analog quantity to obtain the average angular velocity error, and calculate the deviation angle of the calibration axis based on the average angular velocity error and the motion cycle.

[0058] Error compensation unit 40 is configured to construct the direction cosine matrix of the calibration axis based on the deviation angle, and construct the error compensation rotation matrix based on the spatial three-dimensional rotation formula of the direction cosine matrix.

[0059] Angle calibration unit 50 is configured to acquire the original vector of the three-axis gyroscope, perform matrix multiplication between the error compensation rotation matrix and the original vector of the three-axis gyroscope, output the spatially decoupled calibration angular velocity, and calibrate the calibration axis.

[0060] The secondary calibration unit 60 is configured to re-control the motion axis to reciprocate along a preset trajectory and monitor the change amplitude of the magnetic encoder of the calibration axis. When the change amplitude is determined to be greater than a preset threshold, the compensation direction of the deviation angle is changed.

[0061] Based on the aforementioned gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics, and based on the same inventive concept, this application also proposes an IMU. This IMU uses the gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics provided in any of the above embodiments.

[0062] Based on the aforementioned gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics, and based on the same inventive concept, this application also proposes a gimbal. The gimbal is equipped with a controller, a motor, a magnetic encoder, and an IMU as provided in any of the above embodiments. The controller is used to execute the gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics provided in any of the above embodiments, and to control the motor to perform corresponding actions.

[0063] In this embodiment, the controller is the MCU on the gimbal control board.

[0064] In the embodiments of this application, it should be understood that the disclosed technical content can be implemented in other ways. The device / system / method embodiments described above are merely illustrative. For example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0065] The units described as separate components may or may not be physically separate. 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0066] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0067] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0068] It is evident that those skilled in the art can make various modifications and alterations to the embodiments of the present invention without departing from the spirit and scope of the invention. In this way, the invention is also intended to cover such modifications and alterations if they fall within the scope of the claims and their equivalents. The word "comprising" does not exclude the presence of other elements or steps not listed in the claims. The simple fact that certain measures are described in mutually different dependent claims does not indicate that a combination of these measures cannot be used for profit. Any reference numerals in the claims should not be considered as limiting the scope.

Claims

1. A gimbal sensor deviation calibration method based on multi-axis motion coupling characteristics, characterized in that, The method includes the following steps: S1. Obtain the raw data of the current gimbal sensor and filter the raw data, wherein the raw data includes the first digital output of the gyroscope about the angular velocity and the second digital output of the magnetic encoder; S2. Select one axis of the gimbal as the motion axis and the other two axes as calibration axes. Control the motion axis to reciprocate along a preset trajectory in an open loop and record the maximum and minimum values ​​of the first digital output of the calibration axis within one reciprocating motion cycle. S3. Calculate the average extreme value of the first digital output of the calibration axis over multiple motion cycles, and calculate the difference between the extreme values. Convert the difference between the extreme values ​​into an analog quantity to obtain the average angular velocity error. Calculate the deviation angle of the calibration axis based on the average angular velocity error and the motion cycle. S4. Based on the deviation angle, construct the direction cosine matrix of the calibration axis, and based on the spatial three-dimensional rotation formula of the direction cosine matrix, construct the error compensation rotation matrix; S5. Obtain the original vector of the three-axis gyroscope, perform matrix multiplication between the error compensation rotation matrix and the original vector of the three-axis gyroscope, output the spatially decoupled calibration angular velocity, and calibrate the calibration axis. S6. Re-control the motion axis to perform the reciprocating motion of step S2, and monitor the change amplitude of the magnetic encoder of the calibration axis. When it is determined that the change amplitude is greater than a preset threshold, change the compensation direction of the deviation angle in step S3. In step S3, the step of calculating the deviation angle of the calibration axis based on the average angular velocity error and the motion period specifically includes: calculating the average single-journey motion time T within N-1 effective motion periods; integrating the average angular velocity error with the average single-journey motion time T to calculate the deviation angle of the calibration axis. The motion axis is the pitch axis, and the calibration axis includes the roll axis and the yaw axis. The calculation of the deviation angle of the calibration axis specifically includes: mapping the deviation angle of the roll axis obtained by integration to the actual deviation angle of the yaw axis; and mapping the deviation angle of the yaw axis obtained by integration to the actual deviation angle of the roll axis. Step S4 specifically includes: The sine and cosine values ​​of the actual deviation angle are calculated using trigonometric functions, and the direction cosine matrices of the roll axis and the heading axis are constructed as follows: Wherein, α and γ are the actual deviation angles of the roll axis and the yaw axis, respectively. Rx ( α ), Rz ( γ ) are the direction cosine matrices of the roll axis and the heading axis, respectively; Perform matrix multiplication on the direction cosine matrices of the roll axis and the yaw axis to obtain the error compensation rotation matrix. R ,as follows: 。 2. The method according to claim 1, characterized in that, The filtering process for the original data specifically includes: Construct a data sliding window; The acquired raw data is enqueued, and the raw data in the data sliding window is sorted using the bubble sort method to obtain multiple arrays; The median value of each array is taken as the effective output value after filtering.

3. The method according to claim 1, characterized in that, Step S2 specifically includes: The motion axis is controlled to rotate uniformly from 0° to 90° in an open-loop manner, and the one-way motion time t1 is recorded. The motion axis is controlled to return from 90° to 0° at a constant speed using open-loop control, and the one-way motion time t2 is recorded. Repeat the exercise for N cycles, and discard the data from the first cycle.

4. A gimbal sensor deviation calibration system based on multi-axis motion coupling characteristics, characterized in that, The system, using the method as described in any one of claims 1 to 3, comprises: The data processing unit is configured to acquire the raw data of the current gimbal sensor and perform filtering processing on the raw data, wherein the raw data includes the first digital output of the gyroscope with respect to angular velocity and the second digital output of the magnetic encoder; The feature extraction unit is configured to select one axis of the gimbal as the motion axis and the other two axes as calibration axes, and to control the motion axis to reciprocate along a preset trajectory in an open loop, and to record the maximum and minimum values ​​of the first digital output of the calibration axis within one reciprocating motion cycle. The deviation angle calculation unit is configured to calculate the extreme average value of the first digital output of the calibration axis over multiple motion cycles, calculate the extreme average value difference, convert the extreme average value difference into an analog quantity to obtain the average angular velocity error, and calculate the deviation angle of the calibration axis based on the average angular velocity error and the motion cycle. An error compensation unit is configured to construct a direction cosine matrix of the calibration axis based on the deviation angle, and to construct an error compensation rotation matrix based on the spatial three-dimensional rotation formula of the direction cosine matrix. An angle calibration unit is configured to acquire the original vector of the three-axis gyroscope, perform matrix multiplication between the error compensation rotation matrix and the original vector of the three-axis gyroscope, output the spatially decoupled calibration angular velocity, and calibrate the calibration axis.

5. An IMU, characterized in that, Use the method as described in any one of claims 1 to 3.

6. A gimbal, characterized in that, The gimbal is equipped with a controller, a magnetic encoder, and an IMU as described in claim 5, wherein the controller is used to perform the method as described in any one of claims 1 to 3.

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