Four-wheel robot time synchronization method and system based on Ackerman drive-by-wire chassis

Abstract

The invention relates to a four-wheel robot time synchronization method and system based on an Ackerman drive-by-wire chassis. The method comprises the steps that S1, pre-calibration parameter information is acquired, and then S2 and S3 are executed; s2, obtaining inertial measurement data by using an IMU measurement module to obtain a first data set; s3, wheel speed data and wheel angle data after reverse clearance correction are obtained and serve as clearance correction collection data; s4, acquiring an acceleration vector and an angular velocity vector of a control point coordinate system based on two adjacent nearest groups of gap correction acquisition data and vehicle motion model parameters, and taking the acceleration vector and the angular velocity vector as a second data set; and S5, carrying out time error analysis on the first data set and the second data set in the sliding window time, calculating the time deviation of the first data set and the second data set, and carrying out time synchronization. Through the minimization constraint of the target equation, the time synchronization of the IMU measurement module and the chassis sensor data is realized.

Description

Technical Field

[0001] The present application relates to the technical field of time synchronization, and in particular to a time synchronization method and system for a four-wheeled robot based on an Ackerman-controlled-by-wire chassis. Background Art

[0002] The encoder discs on the UGV or AGV wire-controlled chassis currently used in engineering practice are not directly mounted on the vehicle's wheels. Instead, to improve encoder accuracy, they are directly connected to the drive motor module. The drive motor module consists of a drive motor and a directly connected reducer. Due to machining precision, the reducer has backlash. It can be assumed that in an Ackerman chassis, the drive motor reducer and the steering reducer have different backlashes due to different reduction ratios and machining precision. The presence of these two backlashes causes the wheel speed / wheel angle to be out of sync with the inertial measurement unit (IMU).

[0003] Therefore, there is an urgent need for a four-wheel robot time synchronization method and system based on an Ackerman wire-controlled chassis to solve the above technical problems. Summary of the Invention

[0004] The present application provides a time synchronization method and system for a four-wheel robot based on an Ackerman wire-controlled chassis, which are used to solve the problems in the related art.

[0005] To achieve the above objectives, this application is implemented through the following technical solutions:

[0006] This application provides a time synchronization method for a four-wheeled robot based on an Ackerman wire-controlled chassis, the method comprising:

[0007] S1, obtaining pre-calibration parameter information, which includes the transformation relationship from the control point coordinate system to the IMU coordinate system and the vehicle motion model parameters, and then executing S2 and S3; the vehicle motion model parameters include the front and rear axle lengths, drive shaft backlash, and steering shaft backlash of the four-wheeled robot;

[0008] S2, using an IMU measurement module to acquire inertial measurement data, and converting the acceleration vector and angular velocity vector in the inertial measurement data into a control point coordinate system based on the pre-calibrated parameter information, and using the data as a first data set;

[0009] S3, processing the chassis data acquired by the chassis sensor using a backlash correction strategy, obtaining wheel speed data and wheel angle data after backlash correction and using them as backlash correction data;

[0010] S4, based on the two most recent adjacent sets of gap correction collected data and vehicle motion model parameters, obtaining the acceleration vector and angular velocity vector of the control point coordinate system as the second data set;

[0011] S5: Perform time error analysis on the first data set and the second data set within a sliding window time, calculate the time deviation between the two, and perform time synchronization so that the data obtained by the IMU measurement module and the chassis sensor are consistent in time.

[0012] In one embodiment, the IMU measurement module includes an IMU accelerometer and an IMU angular velocity meter, and the control point refers to the center position of the rear wheel axle of the Ackerman chassis; S2 includes:

[0013] Obtaining a first angular velocity vector using the IMU angular velocity meter, and obtaining a first acceleration vector after removing a gravity component using the IMU accelerometer and a specific force equation; and using the first acceleration vector after removing the gravity component and the first angular velocity vector as inertial measurement data;

[0014] According to the pre-calibrated parameter information, the first acceleration vector in the inertial measurement data is converted to the control point coordinate system to obtain a second acceleration vector; the first angular velocity vector in the inertial measurement data is converted to the control point coordinate system to obtain a second angular velocity vector;

[0015] The second acceleration vector and the second angular velocity vector are taken as a first data set.

[0016] In one embodiment, the transformation relationship includes a translation vector transformation relationship and a conversion matrix transformation relationship;

[0017] The converting the first angular velocity vector in the inertial measurement data into the control point coordinate system to obtain the second angular velocity vector includes:

[0018] According to the transformation relationship of the conversion matrix, the second angular velocity vector corresponding to the first angular velocity vector is obtained by formula (1), which is w2=R×w1; wherein w1 is the first angular velocity vector, w2 is the second angular velocity vector, and R is the transformation relationship of the conversion matrix;

[0019] The converting the first acceleration vector in the inertial measurement data into a control point coordinate system according to the pre-calibrated parameter information to obtain a second acceleration vector includes:

[0020] According to the conversion relationship and the first angular velocity vector, the second acceleration vector corresponding to the first acceleration vector is obtained by formula (2). Formula (2) is a2=R×a1-(a1T+w1×w1×T), where a1 is the first acceleration vector, a2 is the second acceleration vector, and T is the translation vector transformation relationship.

[0021] In one embodiment, the Ackerman-by-wire chassis is provided with a steering mechanism and a driving mechanism, as well as a first encoder provided on the steering mechanism and a second encoder provided on the driving mechanism;

[0022] The chassis sensor includes a wheel angle meter for obtaining the output signal of the first encoder, and a wheel speed meter for obtaining the output signal of the second encoder; S3 includes:

[0023] The wheel speed meter is used to obtain the rotation speed of the reducer and used as wheel speed data, the wheel angle is used to obtain the rotation angle of the wheel and used as wheel angle data, and the wheel speed data and the wheel angle data are used as chassis data;

[0024] When there is no backlash on the drive shaft and the steering shaft, the chassis collected data is used as the clearance correction collected data;

[0025] When backlash occurs on the drive shaft and / or steering shaft, backlash correction data is acquired using a backlash correction strategy, which includes:

[0026] The integral amount is calculated according to the sampling interval. If the integral amount does not exceed the remaining gap amount, the wheel speed data and / or wheel angle data corresponding to the reverse gap are set to null values, and the current remaining reverse gap value is recorded until the reverse gap is removed; if the integral amount exceeds the remaining gap amount, it is considered that the reverse gap is completely removed, and the wheel speed data and wheel angle data are output as gap correction collection data.

[0027] In one embodiment, the S4 includes:

[0028] Based on the wheel speed data in the two most recent adjacent sets of gap correction collected data, the wheel speed meter acceleration value in the control point coordinate system is obtained as a third acceleration vector;

[0029] Based on the wheel speed data, wheel angle data and front and rear axle lengths in the two adjacent and most recent sets of gap correction collected data, the third triangle velocity vector of the control point coordinate system is obtained;

[0030] The third acceleration vector and the third angular velocity vector are taken as a second data set.

[0031] In one embodiment, the S5 includes:

[0032] Performing a time error analysis on the first and second data sets within a sliding window, calculating the time deviation between the two sets by minimizing the objective equation, and performing time synchronization to ensure that the data obtained by the IMU measurement module and the chassis sensor are consistent in time;

[0033] Among them, the target equation is (a3-t)-a2+(w3-t)-w2), a2 is the second acceleration vector, w2 is the second angular velocity vector, a3 is the third acceleration vector, w3 is the third angular velocity vector, and t is the time offset used to indicate the time deviation between the two.

[0034] This application also provides a four-wheel robot time synchronization system based on an Ackerman wire-controlled chassis, comprising:

[0035] IMU measurement module, used to obtain inertial measurement data;

[0036] Chassis sensor, used to obtain chassis data;

[0037] A controller is communicatively connected to the IMU measurement module and the chassis sensor, and is configured to:

[0038] S1, obtaining pre-calibration parameter information, which includes the transformation relationship from the control point coordinate system to the IMU coordinate system and the vehicle motion model parameters, and then executing S2 and S3; the vehicle motion model parameters include the front and rear axle lengths, drive shaft backlash, and steering shaft backlash of the four-wheeled robot;

[0039] S2, using an IMU measurement module to acquire inertial measurement data, and converting the acceleration vector and angular velocity vector in the inertial measurement data into a control point coordinate system based on the pre-calibrated parameter information, and using the data as a first data set;

[0040] S3, processing the chassis data acquired by the chassis sensor using a backlash correction strategy, obtaining wheel speed data and wheel angle data after backlash correction and using them as backlash correction data;

[0041] S4, based on the two most recent adjacent sets of gap correction collected data and vehicle motion model parameters, obtaining the acceleration vector and angular velocity vector of the control point coordinate system as the second data set;

[0042] S5: Perform time error analysis on the first data set and the second data set within a sliding window time, calculate the time deviation between the two, and perform time synchronization so that the data obtained by the IMU measurement module and the chassis sensor are consistent in time.

[0043] In one embodiment, the IMU measurement module includes an IMU accelerometer and an IMU angular velocity meter, and the control point refers to the center position of the rear wheel axle of the Ackerman chassis;

[0044] The controller is configured to obtain a first data set by:

[0045] Obtaining a first angular velocity vector using the IMU angular velocity meter, and obtaining a first acceleration vector after removing a gravity component using the IMU accelerometer and a specific force equation; and using the first acceleration vector after removing the gravity component and the first angular velocity vector as inertial measurement data;

[0046] According to the pre-calibrated parameter information, the first acceleration vector in the inertial measurement data is converted to the control point coordinate system to obtain a second acceleration vector; the first angular velocity vector in the inertial measurement data is converted to the control point coordinate system to obtain a second angular velocity vector;

[0047] The second acceleration vector and the second angular velocity vector are taken as a first data set.

[0048] In one embodiment, the Ackerman-by-wire chassis is provided with a steering mechanism and a drive mechanism, as well as a first encoder provided on the steering mechanism and a second encoder provided on the drive mechanism; the chassis sensor includes a wheel angle meter for obtaining an output signal of the first encoder, and a wheel speed meter for obtaining an output signal of the second encoder;

[0049] The controller is configured to obtain gap correction data in the following manner:

[0050] The wheel speed meter is used to obtain the rotation speed of the reducer and used as wheel speed data, the wheel angle is used to obtain the rotation angle of the wheel and used as wheel angle data, and the wheel speed data and the wheel angle data are used as chassis data;

[0051] When there is no backlash on the drive shaft and the steering shaft, the chassis collected data is used as the clearance correction collected data;

[0052] When backlash occurs on the drive shaft and / or steering shaft, backlash correction data is acquired using a backlash correction strategy, which includes:

[0053] The integral amount is calculated according to the sampling interval. If the integral amount does not exceed the remaining gap amount, the wheel speed data and / or wheel angle data corresponding to the reverse gap are set to null values, and the current remaining reverse gap value is recorded until the reverse gap is removed; if the integral amount exceeds the remaining gap amount, it is considered that the reverse gap is completely removed, and the wheel speed data and wheel angle data are output as gap correction collection data.

[0054] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0055] The present application provides a four-wheel robot time synchronization method and system based on an Ackerman-controlled-by-wire chassis, which is used to achieve data time synchronization between the IMU measurement module and the chassis sensor. Data conversion and synchronization are performed through pre-calibrated parameter information (such as the transformation relationship from the control point coordinate system to the IMU coordinate system and the vehicle motion model parameters). The inertial measurement data is converted to the control point coordinate system after processing to form a first data set, and the chassis acquisition data is corrected for backlash to form a second data set. For the first data set and the second data set, the time error analysis and the minimization constraint of the objective equation are used to calculate the time deviation and synchronize the data to ensure the time consistency of the IMU and chassis sensor data, thereby improving the accuracy and reliability of the robot's positioning and navigation.

[0056] Thus, through the minimization constraint of the objective equation, the time synchronization of the IMU measurement module and the chassis sensor data is achieved, ensuring the consistency and reliability of the data. Through the reverse clearance correction strategy, the reverse clearance problem in the mechanical structure is effectively handled, and the accuracy of the measurement data is improved. By time synchronizing the IMU data and the chassis sensor data, a basis is provided for subsequent data fusion, which improves the positioning and navigation accuracy of the four-wheel robot. At the same time, this method can run stress-free on an embedded platform and is suitable for real-time data processing. This technical solution can be used in complex environments. For example, when the laser degrades during charging and the IMU noise is large, the IMU data can be corrected by the wheel speed meter data, thereby improving the positioning and navigation accuracy of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present application is further described below with reference to the accompanying drawings and examples.

[0058] Figure 1 A flow chart of a four-wheel robot time synchronization method provided in an embodiment of the present application is shown.

[0059] Figure 2 A schematic diagram of a process for obtaining a first data set provided in an embodiment of the present application is shown.

[0060] Figure 3 A schematic diagram of a process for obtaining gap correction acquisition data provided by an embodiment of the present application is shown.

[0061] Figure 4 A schematic diagram of a process for obtaining a second data set provided in an embodiment of the present application is shown.

[0062] Figure 5 The figure shows a structural block diagram of a four-wheel robot time synchronization system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The accompanying drawings illustrate preferred embodiments of the present invention. However, the present invention may be implemented in many other forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present disclosure.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0065] First, some application scenarios (charging scenarios) involved in this application are explained.

[0066] When a four-wheeled robot is charging at a charging station, its laser sensor is susceptible to environmental influences and performance degradation, while the IMU generates high noise at low speeds, resulting in unreliable data. However, the wheel speedometer, due to its flat surface and low speeds, provides relatively stable and accurate data. Therefore, during the charging process, it is important to consider how to rely on the wheel speedometer data and ensure data consistency between the IMU and wheel speedometer through time synchronization.

[0067] The technical solution provided by the present application, when applied to the above-mentioned scenario, takes into account that the robot needs to move back and forth frequently to adjust its position during the charging process, resulting in reverse clearance between the drive shaft and the steering shaft. By monitoring the data changes of the wheel speed meter and the wheel angle meter, the occurrence of reverse clearance is detected in real time. When a reverse clearance is detected, the integral amount is calculated according to the sampling interval. If the integral amount exceeds the remaining gap amount, it is considered that the reverse clearance is completely eliminated, and the corrected wheel speed and wheel angle data are output. Using the corrected wheel speed data and IMU data, time error analysis is performed within the sliding window time, and the deviation is calculated and synchronized by minimizing the target equation to ensure that the data time of the IMU and the wheel speed meter are consistent during the charging process, thereby improving the accuracy of positioning and navigation.

[0068] At the same time, the wheel speed and wheel angle are collected and calculated by the electronic control unit (ECU). However, in order to reduce costs, most ECUs do not support timing, resulting in the inability to stably measure the transmission delay between them and the positioning calculation module as the host computer. This is because most positioning calculation modules used for positioning and navigation are based on non-real-time systems (Linux), and their sampling time and delay vary depending on the system load, resulting in different delays. The ECU is a real-time system, and its sampling interval is a stable period, which is understood to be managed by the real-time system. Through the technical solution provided in this application, data from the IMU and wheel speedometer with consistent data time can be provided to the positioning calculation module. The following will first explain the method, and then explain the system, etc.

[0069] Example 1

[0070] See also Figure 1 , Figure 1 A flow chart of a four-wheel robot time synchronization method provided in an embodiment of the present application is shown.

[0071] The method comprises:

[0072] S101, obtaining pre-calibration parameter information, which includes the transformation relationship between the control point coordinate system and the IMU coordinate system and the vehicle motion model parameters, and then executing S101 and S102; the vehicle motion model parameters include the front and rear axle lengths, drive shaft backlash, and steering shaft backlash of the four-wheeled robot;

[0073] S102, using an IMU measurement module to acquire inertial measurement data, and converting acceleration vectors and angular velocity vectors in the inertial measurement data into a control point coordinate system based on the pre-calibrated parameter information, and using the data as a first data set;

[0074] S103, processing the chassis data acquired by the chassis sensor using a backlash correction strategy to obtain wheel speed data and wheel angle data after backlash correction as backlash correction data;

[0075] S104, based on the two most recent adjacent sets of gap correction collected data and vehicle motion model parameters, obtaining the acceleration vector and angular velocity vector of the control point coordinate system as a second data set;

[0076] S105 , performing time error analysis on the first data set and the second data set within a sliding window time, calculating a time deviation between the two, and performing time synchronization so that the data acquired by the IMU measurement module and the chassis sensor are consistent in time.

[0077] The technical solution provided in this embodiment provides a basis for subsequent data conversion and synchronization by obtaining pre-calibration parameter information, ensuring accurate conversion between different coordinate systems, so that IMU data and chassis sensor data can be compared and fused in the same reference system. The raw data is obtained using the IMU accelerometer and angular velocity meter, and the gravity component is removed by the specific force equation, and the data is converted to the control point coordinate system, ensuring the accuracy and consistency of the IMU data. The chassis sensor data is processed by the reverse gap correction strategy to ensure the accuracy of the wheel speed data and wheel angle data. When the reverse gap is detected, the integral amount is calculated according to the sampling interval, and whether the output of the corrected data is determined based on whether the integral amount exceeds the remaining gap amount, thereby solving the reverse gap problem in the mechanical structure and ensuring the reliability of the chassis sensor data. Based on the corrected chassis sensor data and the vehicle motion model parameters, the acceleration and angular velocity vectors of the control point coordinate system are calculated, and the chassis sensor data is converted to the same reference system as the IMU data, providing a direct comparison basis for time synchronization. By performing time error analysis within the sliding window time and calculating the time deviation using the minimization constraint of the objective equation, the data timestamp is adjusted to ensure that the data time of the IMU measurement module is consistent with that of the chassis sensor, thus achieving high-precision time synchronization.

[0078] See also Figure 2 , Figure 2 A schematic diagram of a process for obtaining a first data set provided in an embodiment of the present application is shown.

[0079] In one embodiment, the IMU measurement module includes an IMU accelerometer and an IMU angular velocity meter, and the control point refers to the center position of the rear wheel axle of the Ackerman chassis; S102 includes:

[0080] S201, using the IMU angular velocity meter to obtain a first angular velocity vector, using the IMU accelerometer and a specific force equation to obtain a first acceleration vector after removing the gravity component; and using the first acceleration vector after removing the gravity component and the first angular velocity vector as inertial measurement data;

[0081] S202, based on the pre-calibrated parameter information, converting a first acceleration vector in the inertial measurement data into a control point coordinate system to obtain a second acceleration vector; converting a first angular velocity vector in the inertial measurement data into a control point coordinate system to obtain a second angular velocity vector;

[0082] S203: Take the second acceleration vector and the second angular velocity vector as a first data set.

[0083] The technical solution provided in this embodiment ensures accurate conversion of IMU data from the original coordinate system to the control point coordinate system by pre-calibrating parameter information (transformation matrix R and translation vector T), improving data consistency and reliability. Removing the gravity component from the specific force equation makes acceleration data more accurate, avoids gravity interference on measurement results, and improves measurement accuracy.

[0084] In one embodiment, the transformation relationship includes a translation vector transformation relationship and a conversion matrix transformation relationship;

[0085] The converting the first angular velocity vector in the inertial measurement data into the control point coordinate system to obtain the second angular velocity vector includes:

[0086] According to the transformation relationship of the conversion matrix, the second angular velocity vector corresponding to the first angular velocity vector is obtained by formula (1), which is w2=R×w1; wherein w1 is the first angular velocity vector, w2 is the second angular velocity vector, and R is the transformation relationship of the conversion matrix;

[0087] The converting the first acceleration vector in the inertial measurement data into a control point coordinate system according to the pre-calibrated parameter information to obtain a second acceleration vector includes:

[0088] According to the conversion relationship and the first angular velocity vector, the second acceleration vector corresponding to the first acceleration vector is obtained by formula (2). Formula (2) is a2=R×a1-(a1T+w1×w1×T), where a1 is the first acceleration vector, a2 is the second acceleration vector, and T is the translation vector transformation relationship.

[0089] See also Figure 3 , Figure 3 A schematic diagram of a process for obtaining gap correction acquisition data provided by an embodiment of the present application is shown.

[0090] In one embodiment, the Ackerman-by-wire chassis is provided with a steering mechanism and a driving mechanism, as well as a first encoder provided on the steering mechanism and a second encoder provided on the driving mechanism;

[0091] The chassis sensor includes a wheel angle meter for obtaining an output signal of the first encoder, and a wheel speed meter for obtaining an output signal of the second encoder; S103 includes:

[0092] S301, using the wheel speed meter to obtain the rotation speed of the reducer as wheel speed acquisition data, using the wheel angle meter to obtain the rotation angle of the wheel as wheel angle acquisition data, and using the wheel speed acquisition data and the wheel angle acquisition data as chassis acquisition data;

[0093] S302, when there is no backlash on the drive shaft and the steering shaft, the chassis collected data is used as the gap correction collected data;

[0094] S303: When backlash occurs on the drive shaft and / or the steering shaft, backlash correction data is acquired using a backlash correction strategy. The backlash correction strategy includes:

[0095] The integral amount is calculated according to the sampling interval. If the integral amount does not exceed the remaining gap amount, the wheel speed data and / or wheel angle data corresponding to the reverse gap are set to null values, and the current remaining reverse gap value is recorded until the reverse gap is removed; if the integral amount exceeds the remaining gap amount, it is considered that the reverse gap is completely removed, and the wheel speed data and wheel angle data are output as gap correction collection data.

[0096] See also Figure 4 , Figure 4 A schematic diagram of a process for obtaining a second data set provided in an embodiment of the present application is shown.

[0097] In one embodiment, the S104 includes:

[0098] S401, based on the wheel speed data in the two most recent adjacent sets of gap correction collected data, obtaining a wheel speed meter acceleration value in the control point coordinate system as a third acceleration vector;

[0099] S402, obtaining a third triangle velocity vector of the control point coordinate system based on the wheel speed data, wheel angle data, and front and rear axle lengths in the two most recent adjacent sets of gap correction collected data;

[0100] S403: Use the third acceleration vector and the third angular velocity vector as a second data set.

[0101] In one embodiment, the S105 includes:

[0102] Performing a time error analysis on the first and second data sets within a sliding window, calculating the time deviation between the two sets by minimizing the objective equation, and performing time synchronization to ensure that the data obtained by the IMU measurement module and the chassis sensor are consistent in time;

[0103] The target equation is (a3-t)-a2+(w3-t)-w2), where a3 is the third acceleration vector, w3 is the third angular velocity vector, and t is the time offset used to indicate the time deviation between the two.

[0104] As an example, a lower-level description of the technical solution of this embodiment is given. The time synchronization method is applied to a four-wheel robot time synchronization system, and the four-wheel robot time synchronization system includes an IMU measurement module and a chassis sensor. The IMU measurement module includes an IMU accelerometer and an IMU angular velocity meter, and the control point refers to the center position of the rear wheel axle of the Ackerman chassis; the Ackerman wire-controlled chassis is provided with a steering mechanism and a driving mechanism, and the steering mechanism includes a steering motor, a reducer and a first encoder arranged on the reducer; the driving mechanism includes a driving motor and a reducer, and a second encoder arranged on the reducer; the chassis sensor includes a wheel angle meter for obtaining the output signal of the first encoder, and a wheel speed meter for obtaining the output signal of the second encoder. The method includes:

[0105] S1, obtaining pre-calibration parameter information, which includes the transformation relationship from the control point coordinate system to the IMU coordinate system and the vehicle motion model parameters, and then executing S2 and S3; the vehicle motion model parameters include the front and rear axle lengths, drive axis backlash, and steering axis backlash of the four-wheeled robot; the transformation relationship includes the translation vector transformation relationship and the conversion matrix transformation relationship;

[0106] S2, using the IMU angular velocity meter to obtain a first angular velocity vector, using the IMU accelerometer and a specific force equation to obtain a first acceleration vector after removing the gravity component; and using the first acceleration vector after removing the gravity component and the first angular velocity vector as inertial measurement data;

[0107] According to the transformation relationship of the conversion matrix, the second angular velocity vector corresponding to the first angular velocity vector is obtained by formula (1), which is w2=R×w1; wherein w1 is the first angular velocity vector, w2 is the second angular velocity vector, and R is the transformation relationship of the conversion matrix;

[0108] According to the conversion relationship and the first angular velocity vector, the second acceleration vector corresponding to the first acceleration vector is obtained by formula (2), where formula (2) is a2=R×a1-(a1T+w1×w1×T), a1 is the first acceleration vector, a2 is the second acceleration vector, and T is the translation vector conversion relationship;

[0109] taking the second acceleration vector and the second angular velocity vector as a first data set;

[0110] S3, using the wheel speed meter to obtain the rotation speed of the reducer and use it as wheel speed data, using the wheel angle meter to obtain the rotation angle of the wheel and use it as wheel angle data, and using the wheel speed data and the wheel angle data as chassis data;

[0111] When there is no backlash on the drive shaft and the steering shaft, the chassis collected data is used as the clearance correction collected data;

[0112] When backlash occurs on the drive shaft and / or steering shaft, backlash correction data is acquired using a backlash correction strategy, which includes:

[0113] The integral amount is calculated based on the sampling interval. If the integral amount does not exceed the remaining gap amount, the wheel speed data and / or wheel angle data corresponding to the reverse gap are set to null values, and the current remaining reverse gap value is recorded until the reverse gap is eliminated. If the integral amount exceeds the remaining gap amount, it is considered that the reverse gap is completely eliminated, and the wheel speed data and wheel angle data are output as the gap correction acquisition data;

[0114] The method for determining whether the drive shaft has reverse clearance is as follows: the drive mechanism receives a control signal from a host computer (such as a motion planning module or a remote control device) and drives the drive shaft to switch its driving direction between forward and reverse directions.

[0115] The method for determining whether the steering shaft has a backlash is as follows: the steering mechanism receives a control signal from a host computer (such as a motion planning module or a remote control device) and drives the steering shaft to switch its steering direction between forward and reverse.

[0116] Among them, the backlash of the mechanical structure is determined by the design parameters (gear meshing clearance).

[0117] The integral is calculated based on the sampling interval. It can be assumed that the ECU sampling time is stable (for example, 10ms). The corresponding integral can be calculated by accumulating the change in wheel speed or wheel angle over time:

[0118]

[0119] Where IV(t) is the integral at the current moment, Δx(k) is the change in wheel speed and angle during the kth sampling period, Δt is the sampling time interval, and n is the index of the current sampling period, which is used to identify the number of the current sampling period.

[0120] The integral IV(t) is the cumulative change in wheel speed or wheel angle over time and is used to determine whether backlash has been overcome. If the integral does not exceed the remaining backlash, it indicates that the mechanical structure has not yet completely overcome the backlash and is therefore set to a null value.

[0121] The remaining clearance can be updated using the following formula:

[0122] RB(t)=RB(t-1)-Δx(t)·Δt

[0123] RB(t) is the remaining clearance at the current moment. RB(t-1) is the remaining clearance at the previous moment. Δx(t) is the change in wheel speed or wheel angle during the current sampling period. Δt is the sampling interval.

[0124] The remaining gap value RB(t) represents the gap that still needs to be overcome. During each sampling cycle, the remaining gap value is updated based on the current change and the sampling interval. The sampling interval is the time difference between two consecutive sensor data acquisitions.

[0125] S4. Based on the wheel speed data in the two most recent adjacent sets of gap correction collected data, obtain the wheel speed meter acceleration value of the control point coordinate system as the third acceleration vector; for example, if at least one of the wheel speed data in the two most recent adjacent sets of gap correction collected data is a null value, it means that during the gap correction process, the wheel speed meter acceleration value is 0.

[0126] A third angular velocity vector of the control point coordinate system is obtained based on the wheel speed data, wheel angle data, and front and rear axle lengths from the two most recent adjacent sets of gap correction data. For example, if at least one of the wheel angle data from the two most recent adjacent sets of gap correction data is null, this indicates that the third angular velocity vector is zero during the gap correction process. The third angular velocity vector can be obtained by table lookup based on the wheel speed data, wheel angle data, and front and rear axle lengths from the two most recent adjacent sets of gap correction data.

[0127] taking the third acceleration vector and the third angular velocity vector as a second data set;

[0128] S5, performing time error analysis on the first data set and the second data set within the sliding window time, calculating the time deviation between the two through the minimization constraint of the objective equation and performing time synchronization, so that the data time obtained by the IMU measurement module is consistent with that obtained by the chassis sensor; at the same time, re-execute S2 to realize the loop of the method.

[0129] Wherein, the target equation is (a3-t)-a2+(w3-t)-w2), where a3 is the third acceleration vector, w3 is the third angular velocity vector, and t is the time offset used to indicate the time deviation between the two. The first data set and the second data set are synchronized based on the obtained time offset to ensure that the data obtained by the IMU measurement module is consistent with the data obtained by the chassis sensor.

[0130] It can be understood that the pre-calibration parameter information includes the transformation relationship from the control point coordinate system to the IMU coordinate system (translation vector T and translation matrix R) and the vehicle motion model parameters (front and rear axle length L, drive shaft backlash DL and steering shaft backlash DR).

[0131] In the inertial measurement data processing step (S2), the IMU accelerometer is used to obtain a raw acceleration vector, and the gravity component is removed using the specific force equation to obtain a first acceleration vector. The IMU angular velocity is used to obtain a first angular velocity vector. The first acceleration vector and the first angular velocity vector are converted to the control point coordinate system to obtain a second acceleration vector and a second angular velocity vector. The converted second acceleration vector and second angular velocity vector are used as the first data set.

[0132] In the chassis data collection processing step (S3), the speedometer is used to obtain the rotation speed of the reducer, and the wheel angle is obtained by using the wheel angle meter. If there is no backlash on the drive shaft and the steering shaft, the chassis data is directly used as the backlash correction data. If there is backlash on the drive shaft and / or the steering shaft, the data is processed using the backlash correction strategy. The integral amount is calculated based on the sampling interval. If the integral amount does not exceed the remaining backlash amount, the corresponding wheel speed data and / or wheel angle data are set to null values (wheel speed data for the drive shaft and wheel angle data for the steering shaft); if the integral amount exceeds the remaining backlash amount, the corrected wheel speed data and wheel angle data are output as the backlash correction data.

[0133] In the step of obtaining the second data set (S4), the wheel speedometer acceleration value in the control point coordinate system is obtained as a third acceleration vector based on the wheel speed data in the two most recent adjacent sets of gap-corrected collected data. A third angular velocity vector in the control point coordinate system is obtained based on the wheel speed data, wheel angle data, and front and rear axle lengths in the two most recent adjacent sets of gap-corrected collected data. The third acceleration vector and the third angular velocity vector are used as the second data set.

[0134] In the time synchronization step (S5), the first data set and the second data set are subjected to time error analysis within the sliding window time, and the time deviation between the two is calculated by minimizing the objective equation, and time synchronization is performed.

[0135] Thus, through the minimization constraint of the objective equation, the time synchronization of the IMU measurement module and the chassis sensor data is achieved, ensuring the consistency and reliability of the data. Through the reverse clearance correction strategy, the reverse clearance problem in the mechanical structure is effectively handled, and the accuracy of the measurement data is improved. By time synchronizing the IMU data and the chassis sensor data, a basis is provided for subsequent data fusion, which improves the positioning and navigation accuracy of the four-wheel robot. At the same time, this method has relatively low requirements for the computing power of data processing, can run without pressure on the embedded platform, and is suitable for real-time data processing. This technical solution can be used in complex environments. For example, when the laser is degraded during charging and the IMU noise is large, the IMU data is corrected by the wheel speed meter data, thereby improving the positioning and navigation accuracy of the robot.

[0136] Example 2

[0137] See also Figure 5 , Figure 5 The figure shows a structural block diagram of a four-wheel robot time synchronization system provided in an embodiment of the present application.

[0138] This application also provides a four-wheel robot time synchronization system based on an Ackerman wire-controlled chassis, comprising:

[0139] IMU measurement module, used to obtain inertial measurement data;

[0140] Chassis sensor, used to obtain chassis data;

[0141] A controller is communicatively connected to the IMU measurement module and the chassis sensor, and is configured to:

[0142] S1, obtaining pre-calibration parameter information, which includes the transformation relationship from the control point coordinate system to the IMU coordinate system and the vehicle motion model parameters, and then executing S2 and S3; the vehicle motion model parameters include the front and rear axle lengths, drive shaft backlash, and steering shaft backlash of the four-wheeled robot;

[0143] S2, using an IMU measurement module to acquire inertial measurement data, and converting the acceleration vector and angular velocity vector in the inertial measurement data into a control point coordinate system based on the pre-calibrated parameter information, and using the data as a first data set;

[0144] S3, processing the chassis data acquired by the chassis sensor using a backlash correction strategy, obtaining wheel speed data and wheel angle data after backlash correction and using them as backlash correction data;

[0145] S4, based on the two most recent adjacent sets of gap correction collected data and vehicle motion model parameters, obtaining the acceleration vector and angular velocity vector of the control point coordinate system as the second data set;

[0146] S5: Perform time error analysis on the first data set and the second data set within a sliding window time, calculate the time deviation between the two, and perform time synchronization so that the data obtained by the IMU measurement module and the chassis sensor are consistent in time.

[0147] In one embodiment, the IMU measurement module includes an IMU accelerometer and an IMU angular velocity meter, and the control point refers to the center position of the rear wheel axle of the Ackerman chassis; S2 includes:

[0148] Obtaining a first angular velocity vector using the IMU angular velocity meter, and obtaining a first acceleration vector after removing a gravity component using the IMU accelerometer and a specific force equation; and using the first acceleration vector after removing the gravity component and the first angular velocity vector as inertial measurement data;

[0149] According to the pre-calibrated parameter information, the first acceleration vector in the inertial measurement data is converted to the control point coordinate system to obtain a second acceleration vector; the first angular velocity vector in the inertial measurement data is converted to the control point coordinate system to obtain a second angular velocity vector;

[0150] The second acceleration vector and the second angular velocity vector are taken as a first data set.

[0151] In one embodiment, the transformation relationship includes a translation vector transformation relationship and a conversion matrix transformation relationship;

[0152] The converting the first angular velocity vector in the inertial measurement data into the control point coordinate system to obtain the second angular velocity vector includes:

[0153] According to the transformation relationship of the conversion matrix, the second angular velocity vector corresponding to the first angular velocity vector is obtained by formula (1), which is w2=R×w1; wherein w1 is the first angular velocity vector, w2 is the second angular velocity vector, and R is the transformation relationship of the conversion matrix;

[0154] The converting the first acceleration vector in the inertial measurement data into a control point coordinate system according to the pre-calibrated parameter information to obtain a second acceleration vector includes:

[0155] According to the conversion relationship and the first angular velocity vector, the second acceleration vector corresponding to the first acceleration vector is obtained by formula (2). Formula (2) is a2=R×a1-(a1T+w1×w1×T), where a1 is the first acceleration vector, a2 is the second acceleration vector, and T is the translation vector transformation relationship.

[0156] In one embodiment, the Ackerman-by-wire chassis is provided with a steering mechanism and a driving mechanism, as well as a first encoder provided on the steering mechanism and a second encoder provided on the driving mechanism;

[0157] The chassis sensor includes a wheel angle meter for obtaining an output signal of the first encoder, and a wheel speed meter for obtaining an output signal of the second encoder;

[0158] The controller is configured to obtain a first data set by:

[0159] The wheel speed meter is used to obtain the rotation speed of the reducer and used as wheel speed data, the wheel angle is used to obtain the rotation angle of the wheel and used as wheel angle data, and the wheel speed data and the wheel angle data are used as chassis data;

[0160] When there is no backlash on the drive shaft and the steering shaft, the chassis collected data is used as the clearance correction collected data;

[0161] When backlash occurs on the drive shaft and / or steering shaft, backlash correction data is acquired using a backlash correction strategy, which includes:

[0162] The integral amount is calculated according to the sampling interval. If the integral amount does not exceed the remaining gap amount, the wheel speed data and / or wheel angle data corresponding to the reverse gap are set to null values, and the current remaining reverse gap value is recorded until the reverse gap is removed; if the integral amount exceeds the remaining gap amount, it is considered that the reverse gap is completely removed, and the wheel speed data and wheel angle data are output as gap correction collection data.

[0163] In one embodiment, the transformation relationship includes a translation vector transformation relationship and a conversion matrix transformation relationship;

[0164] The converting the first angular velocity vector in the inertial measurement data into the control point coordinate system to obtain the second angular velocity vector includes:

[0165] According to the transformation relationship of the conversion matrix, the second angular velocity vector corresponding to the first angular velocity vector is obtained by formula (1), which is w2=R×w1; wherein w1 is the first angular velocity vector, w2 is the second angular velocity vector, and R is the transformation relationship of the conversion matrix;

[0166] The converting the first acceleration vector in the inertial measurement data into a control point coordinate system according to the pre-calibrated parameter information to obtain a second acceleration vector includes:

[0167] According to the conversion relationship and the first angular velocity vector, the second acceleration vector corresponding to the first acceleration vector is obtained by formula (2). Formula (2) is a2=R×a1-(a1T+w1×w1×T), where a1 is the first acceleration vector, a2 is the second acceleration vector, and T is the translation vector transformation relationship.

[0168] In one embodiment, the Ackerman-by-wire chassis is provided with a steering mechanism and a drive mechanism, as well as a first encoder provided on the steering mechanism and a second encoder provided on the drive mechanism; the chassis sensor includes a wheel angle meter for acquiring an output signal of the first encoder, and a wheel speed meter for acquiring an output signal of the second encoder; and the controller is configured to acquire gap correction data in the following manner:

[0169] The wheel speed meter is used to obtain the rotation speed of the reducer and used as wheel speed data, the wheel angle is used to obtain the rotation angle of the wheel and used as wheel angle data, and the wheel speed data and the wheel angle data are used as chassis data;

[0170] When there is no backlash on the drive shaft and the steering shaft, the chassis collected data is used as the clearance correction collected data;

[0171] When backlash occurs on the drive shaft and / or steering shaft, backlash correction data is acquired using a backlash correction strategy, which includes:

[0172] The integral amount is calculated according to the sampling interval. If the integral amount does not exceed the remaining gap amount, the wheel speed data and / or wheel angle data corresponding to the reverse gap are set to null values, and the current remaining reverse gap value is recorded until the reverse gap is removed; if the integral amount exceeds the remaining gap amount, it is considered that the reverse gap is completely removed, and the wheel speed data and wheel angle data are output as gap correction collection data.

[0173] In one embodiment, the controller is configured to obtain the second data set by:

[0174] Based on the wheel speed data in the two most recent adjacent sets of gap correction collected data, the wheel speed meter acceleration value in the control point coordinate system is obtained as a third acceleration vector;

[0175] Based on the wheel speed data, wheel angle data and front and rear axle lengths in the two adjacent and most recent sets of gap correction collected data, the third triangle velocity vector of the control point coordinate system is obtained;

[0176] The third acceleration vector and the third angular velocity vector are taken as a second data set.

[0177] In one embodiment, the controller is configured to perform time synchronization by:

[0178] Performing a time error analysis on the first and second data sets within a sliding window, calculating the time deviation between the two sets by minimizing the objective equation, and performing time synchronization to ensure that the data obtained by the IMU measurement module and the chassis sensor are consistent in time;

[0179] The target equation is (a3-t)-a2+(w3-t)-w2), where a3 is the third acceleration vector, w3 is the third angular velocity vector, and t is the time offset used to indicate the time deviation between the two.

[0180] Example 3

[0181] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the steps of any of the above methods are implemented.

[0182] The computer-readable storage medium may be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium has storage space for program code for executing any of the method steps described above. These program codes can be read from or written to one or more computer program products. The program code can be compressed, for example, in an appropriate form.

[0183] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0184] The above-described embodiments merely represent several implementation methods of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A time synchronization method for a four-wheeled robot based on an Ackerman wire-controlled chassis, characterized in that: Methods include: S1, obtaining pre-calibration parameter information, which includes the transformation relationship from the control point coordinate system to the IMU coordinate system and the vehicle motion model parameters, and then executing S2 and S3; the vehicle motion model parameters include the front and rear axle lengths, drive shaft backlash, and steering shaft backlash of the four-wheeled robot; S2, using an IMU measurement module to acquire inertial measurement data, and converting the acceleration vector and angular velocity vector in the inertial measurement data into a control point coordinate system based on the pre-calibrated parameter information, and using the data as a first data set; S3, processing the chassis data acquired by the chassis sensor using a backlash correction strategy, obtaining wheel speed data and wheel angle data after backlash correction and using them as backlash correction data; S4, based on the two most recent adjacent sets of gap correction collected data and vehicle motion model parameters, obtaining the acceleration vector and angular velocity vector of the control point coordinate system as the second data set; S5: Perform time error analysis on the first data set and the second data set within a sliding window time, calculate the time deviation between the two, and perform time synchronization so that the data obtained by the IMU measurement module and the chassis sensor are consistent in time.

2. The four-wheel robot time synchronization method according to claim 1, characterized in that: The IMU measurement module includes an IMU accelerometer and an IMU angular velocity meter. The control point refers to the center position of the rear wheel axle of the Ackerman chassis. The S2 includes: Obtaining a first angular velocity vector using the IMU angular velocity meter, and obtaining a first acceleration vector after removing a gravity component using the IMU accelerometer and a specific force equation; and using the first acceleration vector after removing the gravity component and the first angular velocity vector as inertial measurement data; According to the pre-calibrated parameter information, the first acceleration vector in the inertial measurement data is converted to the control point coordinate system to obtain a second acceleration vector; the first angular velocity vector in the inertial measurement data is converted to the control point coordinate system to obtain a second angular velocity vector; The second acceleration vector and the second angular velocity vector are taken as a first data set.

3. The four-wheel robot time synchronization method according to claim 2, characterized in that: The transformation relationship includes a translation vector transformation relationship and a conversion matrix transformation relationship; The converting the first angular velocity vector in the inertial measurement data into the control point coordinate system to obtain the second angular velocity vector includes: According to the transformation relationship of the conversion matrix, the second angular velocity vector corresponding to the first angular velocity vector is obtained by formula (1), which is w2=R×w1; wherein w1 is the first angular velocity vector, w2 is the second angular velocity vector, and R is the transformation relationship of the conversion matrix; The converting the first acceleration vector in the inertial measurement data into a control point coordinate system according to the pre-calibrated parameter information to obtain a second acceleration vector includes: According to the conversion relationship and the first angular velocity vector, the second acceleration vector corresponding to the first acceleration vector is obtained by formula (2). Formula (2) is a2=R×a1-(a1T+w1×w1×T), where a1 is the first acceleration vector, a2 is the second acceleration vector, and T is the translation vector transformation relationship.

4. The four-wheel robot time synchronization method according to claim 3, characterized in that: The Ackerman-by-wire chassis is provided with a steering mechanism and a drive mechanism, as well as a first encoder provided on the steering mechanism and a second encoder provided on the drive mechanism; the chassis sensor includes a wheel angle meter for obtaining an output signal of the first encoder, and a wheel speed meter for obtaining an output signal of the second encoder; S3 includes: The wheel speed meter is used to obtain the rotation speed of the reducer and used as wheel speed data, the wheel angle is used to obtain the rotation angle of the wheel and used as wheel angle data, and the wheel speed data and the wheel angle data are used as chassis data; When there is no backlash on the drive shaft and the steering shaft, the chassis collected data is used as the clearance correction collected data; When backlash occurs on the drive shaft and / or steering shaft, backlash correction data is acquired using a backlash correction strategy, which includes: The integral amount is calculated according to the sampling interval. If the integral amount does not exceed the remaining gap amount, the wheel speed data and / or wheel angle data corresponding to the reverse gap are set to null values, and the current remaining reverse gap value is recorded until the reverse gap is removed; if the integral amount exceeds the remaining gap amount, it is considered that the reverse gap is completely removed, and the wheel speed data and wheel angle data are output as gap correction collection data.

5. The four-wheel robot time synchronization method according to claim 4, characterized in that: The S4 includes: Based on the wheel speed data in the two most recent adjacent sets of gap correction collected data, the wheel speed meter acceleration value in the control point coordinate system is obtained as a third acceleration vector; Based on the wheel speed data, wheel angle data and front and rear axle lengths in the two adjacent and most recent sets of gap correction collected data, the third triangle velocity vector of the control point coordinate system is obtained; The third acceleration vector and the third angular velocity vector are taken as a second data set.

6. The four-wheel robot time synchronization method according to claim 1, characterized in that: The S5 includes: Performing a time error analysis on the first and second data sets within a sliding window, calculating the time deviation between the two sets by minimizing the objective equation, and performing time synchronization to ensure that the data obtained by the IMU measurement module and the chassis sensor are consistent in time; Among them, the target equation is (a3-t)-a2+(w3-t)-w2), a2 is the second acceleration vector, w2 is the second angular velocity vector, a3 is the third acceleration vector, w3 is the third angular velocity vector, and t is the time offset used to indicate the time deviation between the two.

7. A four-wheel robot time synchronization system based on an Ackerman wire-controlled chassis, characterized in that: include: IMU measurement module, used to obtain inertial measurement data; Chassis sensor, used to obtain chassis data; A controller is communicatively connected to the IMU measurement module and the chassis sensor, and is configured to: S1, obtaining pre-calibration parameter information, which includes the transformation relationship from the control point coordinate system to the IMU coordinate system and the vehicle motion model parameters, and then executing S2 and S3; the vehicle motion model parameters include the front and rear axle lengths, drive shaft backlash, and steering shaft backlash of the four-wheeled robot; S2, using an IMU measurement module to acquire inertial measurement data, and converting the acceleration vector and angular velocity vector in the inertial measurement data into a control point coordinate system based on the pre-calibrated parameter information, and using the data as a first data set; S3, processing the chassis data acquired by the chassis sensor using a backlash correction strategy, obtaining wheel speed data and wheel angle data after backlash correction and using them as backlash correction data; S4, based on the two most recent adjacent sets of gap correction collected data and vehicle motion model parameters, obtaining the acceleration vector and angular velocity vector of the control point coordinate system as the second data set; S5: Perform time error analysis on the first data set and the second data set within a sliding window time, calculate the time deviation between the two, and perform time synchronization so that the data obtained by the IMU measurement module and the chassis sensor are consistent in time.

8. The four-wheel robot time synchronization system according to claim 7, characterized in that: The IMU measurement module includes an IMU accelerometer and an IMU angular velocity meter, and the control point refers to the center position of the rear wheel axle of the Ackerman chassis; The controller is configured to obtain a first data set by: Obtain a first angular velocity vector using the IMU angular velocity meter, and obtain a first acceleration vector after removing a gravity component using the IMU accelerometer and a specific force equation; The first acceleration vector and the first angular velocity vector after removing the gravity component are used as inertial measurement data; According to the pre-calibrated parameter information, the first acceleration vector in the inertial measurement data is converted to the control point coordinate system to obtain a second acceleration vector; the first angular velocity vector in the inertial measurement data is converted to the control point coordinate system to obtain a second angular velocity vector; The second acceleration vector and the second angular velocity vector are taken as a first data set.

9. The four-wheel robot time synchronization system according to claim 7, characterized in that: The Ackerman-by-wire chassis is provided with a steering mechanism and a drive mechanism, as well as a first encoder provided on the steering mechanism and a second encoder provided on the drive mechanism; the chassis sensor includes a wheel angle meter for obtaining an output signal of the first encoder, and a wheel speed meter for obtaining an output signal of the second encoder; The controller is configured to obtain gap correction data in the following manner: The wheel speed meter is used to obtain the rotation speed of the reducer and used as wheel speed data, the wheel angle is used to obtain the rotation angle of the wheel and used as wheel angle data, and the wheel speed data and the wheel angle data are used as chassis data; When there is no backlash on the drive shaft and the steering shaft, the chassis collected data is used as the clearance correction collected data; When backlash occurs on the drive shaft and / or steering shaft, backlash correction data is acquired using a backlash correction strategy, which includes: The integral amount is calculated according to the sampling interval. If the integral amount does not exceed the remaining gap amount, the wheel speed data and / or wheel angle data corresponding to the reverse gap are set to null values, and the current remaining reverse gap value is recorded until the reverse gap is removed; if the integral amount exceeds the remaining gap amount, it is considered that the reverse gap is completely removed, and the wheel speed data and wheel angle data are output as gap correction collection data.

10. A computer-readable storage medium having a computer program stored thereon, 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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