Method for diagnosing failure of mobile device wheel speed sensor and device thereof, mobile device

Abstract

The application discloses a fault diagnosis method of a wheel speed sensor of a mobile device, and comprises the following steps: collecting left wheel speed and right wheel speed output by the wheel speed sensor and IMU heading angle rate output by an inertial measurement unit, and performing low-pass filtering processing; calculating wheel speed-based heading angle rate ω odom odom and the IMU heading angle rate ω imu after the low-pass filtering processing according to timestamps, and calculating the difference Δω between the two heading angle rates; counting a plurality of latest obtained difference values Δω, and diagnosing whether the wheel speed sensor is abnormal or not. The application further discloses a fault diagnosis device of a wheel speed sensor of a mobile device and a mobile device. By adopting the mutual checking mode of the heading angle rate output by the inertial measurement unit (IMU), the application can accurately judge the fault condition of the wheel speed sensor, and ensure the safe and stable operation of the vehicle.​

Description

Technical Field

[0001] This invention relates to the field of autonomous vehicle technology, and in particular to a fault diagnosis method for a wheel speed sensor of a mobile device, a fault diagnosis device for a wheel speed sensor of a mobile device, and a mobile device. Background Technology

[0002] In recent years, with the continuous development and popularization of autonomous driving technology, small, low-speed unmanned sweeping vehicles applied in specific environments have become the first commercially available products for autonomous driving. For sanitation cleaning operations in scenarios such as parks, campuses, large shopping malls, and industrial parks, the replacement of manual cleaning by intelligent unmanned sweeping vehicles has become a trend.

[0003] For small, low-speed unmanned sweeping vehicles, they are equipped with high-precision differential GNSS (Global Navigation Satellite System) boards, lidar, IMU (Inertial Measurement Unit), and wheel speed sensors. The wheel speed sensors installed on the unmanned sweeping vehicles determine the accurate vehicle speed by measuring the rotational angular rate of the vehicle's wheels. However, in actual driving, vehicles often encounter extreme conditions, which can lead to inaccurate speed information measured by the wheel speed sensors, such as when the vehicle experiences extreme conditions like "slipping" or "jumping".

[0004] If the vehicle speed information measured by the wheel speed sensor is inaccurate, it can easily lead to a decrease in vehicle positioning accuracy and loss of vehicle control, ultimately causing the failure of the vehicle's autonomous driving function and potentially triggering a vehicle safety accident.

[0005] Currently, the method for diagnosing wheel speed sensor faults in autonomous cleaning vehicles typically relies on the fault flags of the wheel speed sensors themselves. While this method can diagnose wheel speed sensor faults, it has several drawbacks. In actual autonomous driving, positioning and control require vehicle speed information, not wheel speed information. In most cases, the wheel speed sensor reflects the vehicle speed. However, when the vehicle experiences extreme conditions such as skidding or jumping, the wheel speed sensor may not reflect the vehicle speed. Yet, in these situations, the fault flags of the wheel speed sensor may still indicate that it is functioning normally. Therefore, this method sometimes fails to reflect the fault status of the wheel speed sensor in a timely manner, and sometimes it cannot diagnose or identify the vehicle speed information measured by the wheel speed sensor. Summary of the Invention

[0006] The purpose of this invention is to address the technical deficiencies of existing technologies by providing a fault diagnosis method for a wheel speed sensor in a mobile device, a fault diagnosis device for a wheel speed sensor in a mobile device, and a mobile device.

[0007] Therefore, in a first aspect, the present invention provides a method for diagnosing faults in a wheel speed sensor of a mobile device, comprising the following steps:

[0008] The left wheel speed and right wheel speed output from the wheel speed sensor, as well as the heading angular rate output from the inertial measurement unit, are collected respectively, and the collected data are processed by low-pass filtering.

[0009] Using the left and right wheel speeds of the vehicle after low-pass filtering, the heading angular rate ω based on the wheel speeds is calculated. odom ;

[0010] For the heading angular rate ω based on wheel speed odom and the IMU heading angular rate ω after low-pass filtering imu Alignment is performed according to timestamps, and the heading angular rate ω based on wheel speed is calculated for timestamp alignment. odom and the IMU heading angular rate ω after low-pass filtering imu The difference Δω;

[0011] The latest multi-frame difference Δω is statistically analyzed, and based on the statistical results, the abnormality of the wheel speed sensor is diagnosed.

[0012] Preferably, the acquired data is subjected to low-pass filtering, which includes using a low-pass filter with a cutoff frequency less than half the sampling frequency of the wheel speed sensor and the inertial measurement unit to perform low-pass filtering on the acquired data.

[0013] Preferably, a Butterworth low-pass filter is used to perform low-pass filtering on the acquired data.

[0014] Preferably, the heading angular rate ω based on wheel speed odom and the IMU heading angular rate ω after low-pass filtering imu Alignment is performed according to timestamps, and the heading angular rate ω based on wheel speed is calculated for timestamp alignment. odom and the IMU heading angular rate ω after low-pass filtering imu The difference Δω includes:

[0015] Determine the heading angular rate ω based on each wheel speed. odom The corresponding first timestamp, and the heading angular rate ω of each IMU after low-pass filtering. imu The corresponding second timestamp;

[0016] Find the second timestamp that is closest to each of the first timestamps;

[0017] Calculate the heading angular rate ω based on wheel speed for each close first and second timestamp. odom and the IMU heading angular rate ω after low-pass filtering imu The difference Δω.

[0018] Preferably, finding a second timestamp that is closest to each first timestamp includes:

[0019] For each first timestamp, calculate the time difference between each second timestamp and the current first timestamp. If the time difference is less than a preset time difference threshold, then determine the second timestamp as the second timestamp closest to the current first timestamp.

[0020] Preferably, the latest obtained multi-frame difference Δω is statistically analyzed, including:

[0021] Obtain the heading angular rate deviation Δω of the latest m frames obtained before the current time, and calculate the data deviation according to the following formula:

[0022]

[0023] Where σ is the statistical bias; m is a natural number; Δω m This represents the deviation of the heading angular rate in the m-th frame obtained before the current moment.

[0024] Preferably, diagnosing whether the wheel speed sensor is malfunctioning based on statistical results includes:

[0025] If the statistical deviation σ is greater than the preset deviation threshold, the wheel speed sensor is diagnosed as malfunctioning.

[0026] In a second aspect, the present invention also provides a fault diagnosis device for a wheel speed sensor of a mobile device, comprising:

[0027] The data acquisition unit is used to acquire the left wheel speed and right wheel speed output by the wheel speed sensor, and the heading angular rate output by the inertial measurement unit, and to perform low-pass filtering on the acquired data.

[0028] The rate calculation unit is used to calculate the heading angular rate ω based on the wheel speeds of the vehicle's left and right wheels after low-pass filtering. odom ;

[0029] Alignment unit, used for aligning the heading angular rate ω based on wheel speed. odom and the IMU heading angular rate ω after low-pass filtering imu Alignment is performed according to timestamps;

[0030] The difference calculation unit is used to calculate the heading angular rate ω based on wheel speed for timestamp alignment.odom and the IMU heading angular rate ω after low-pass filtering imu The difference Δω;

[0031] The statistical unit is used to perform statistical analysis on the latest multi-frame difference Δω.

[0032] The diagnostic unit is used to diagnose whether the wheel speed sensor is malfunctioning based on statistical results.

[0033] In a third aspect, the present invention also provides a mobile device comprising a mobile body having at least two sets of symmetrically arranged left and right wheels; wheel speed sensors for measuring the wheel speeds of the left and right wheels of the mobile body; an inertial measurement unit for measuring the heading angular rate of the IMU of the mobile body; and a fault diagnosis device for the aforementioned mobile device wheel speed sensors.

[0034] Preferably, the mobile device is an unmanned vehicle.

[0035] In a fourth aspect, the present invention also provides another mobile device, the mobile device including a memory and a processor, the memory for storing a program, and the processor, when running the program, for executing the aforementioned method.

[0036] As can be seen from the technical solutions provided by the present invention above, compared with the prior art, the present invention provides a fault diagnosis method for wheel speed sensors of mobile devices, a fault diagnosis device for wheel speed sensors of mobile devices, and a mobile device. The present invention, by using the heading angular rate output by the same inertial measurement unit (IMU) for mutual verification, can accurately determine the fault condition of the wheel speed sensor, ensuring the safe and stable operation of the vehicle.

[0037] By applying this invention, on the one hand, abnormal vehicle speed information (i.e., wheel speed data) output by the wheel speed sensor can be accurately isolated, and on the other hand, the vehicle safety risks caused by abnormal wheel speed sensors can be reduced. Attached Figure Description

[0038] Figure 1 A flowchart of a fault diagnosis method for a mobile device wheel speed sensor provided by the present invention;

[0039] Figure 2 This is a schematic diagram illustrating the diagnostic principle of wheel speed data from commonly used vehicle-side wheel speed sensors.

[0040] Figure 3 A structural block diagram of a fault diagnosis device for a mobile device wheel speed sensor provided by the present invention;

[0041] Figure 4 This is a schematic diagram of the structure of a mobile device provided by the present invention. Detailed Implementation

[0042] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0043] This invention provides a fault diagnosis method for wheel speed sensors in mobile devices, applicable to any mobile device equipped with a wheel speed sensor and an inertial measurement unit (IMU), such as unmanned sweepers, unmanned sprinkler trucks, unmanned floor scrubbers, unmanned logistics vehicles, and other unmanned vehicles.

[0044] See Figure 1 The fault diagnosis method for the wheel speed sensor of a mobile device provided by the present invention includes the following steps:

[0045] Step S1, Sensor Data Acquisition and Processing: For mobile devices pre-installed with wheel speed sensors and inertial measurement units (IMUs), the left wheel speed, right wheel speed, and heading angular rate (hereinafter referred to as "IMU heading angular rate") output by the wheel speed sensors are acquired respectively, and low-pass filtering is performed to obtain the left wheel speed, right wheel speed, and IMU heading angular rate after low-pass filtering.

[0046] like Figure 1 As shown, step S1 specifically includes the following sub-steps:

[0047] Step S11, Raw data acquisition: Acquire the vehicle's left and right wheel speeds output by the wheel speed sensors, as well as the IMU heading angular rate;

[0048] Step S12, Low-pass filtering: The left wheel speed, right wheel speed and IMU heading angular rate of the vehicle collected in step S11 are subjected to low-pass filtering to remove high-frequency noise that is not related to the actual motion state of the vehicle, and the left wheel speed, right wheel speed and IMU heading angular rate of the vehicle after low-pass filtering are obtained, which helps to improve the accuracy and stability of the data.

[0049] In this invention, the purpose of low-pass filtering is to filter out high-frequency noise data that is irrelevant to the actual motion characteristics of the vehicle.

[0050] In some embodiments, step S12 employs, but is not limited to, a Butterworth low-pass filter to perform low-pass filtering on the data.

[0051] To ensure the effectiveness of low-pass filtering, in specific implementation, when performing low-pass filtering in step S12, the cutoff frequency of the low-pass filter should be less than half of the sampling frequency of the wheel speed sensor and the sampling frequency of the IMU.

[0052] It should be noted that in this invention, in step S12, the characteristics of the low-pass filter are set according to the characteristics of the sweeper's sensors and the vehicle. Taking the commonly used IMU sampling frequency of 100Hz and the wheel speed sensor sampling frequency of 10Hz as an example, the cutoff frequency of the low-pass filter needs to be less than 1 / 2 of the sampling frequency of these two sensors. In combination with the vehicle characteristics, the cutoff frequency of the low-pass filter that filters the vehicle's heading angular velocity rate can be 30Hz, and the cutoff frequency of the low-pass filter that filters the left wheel speed and right wheel speed can be 3Hz.

[0053] Step S2, Calculate the heading angular rate based on wheel speed: Using the left and right wheel speeds of the vehicle after low-pass filtering, calculate the heading angular rate ω based on the wheel speeds. odom .

[0054] In some embodiments, step S2 calculates the heading angular rate based on wheel speed using the following formula:

[0055] ω odom =(V R -V L ) / L wheel_base , formula (1);

[0056] Among them, V L V represents the left wheel speed of the vehicle after low-pass filtering. R L represents the right wheel speed of the vehicle after low-pass filtering. wheel_base The track width between the left and right wheels of the vehicle is determined by the vehicle's structural dimensions and is known data; ω odom The calculated heading angular rate is based on wheel speed.

[0057] Step S3, diagnose whether the wheel speed sensor is malfunctioning: This involves checking the heading angular rate ω based on wheel speed. odom and the IMU heading angular rate ω after low-pass filtering imu Alignment is performed according to timestamps, and the heading angular rate ω based on wheel speed is calculated for timestamp alignment. odom and the IMU heading angular rate ω after low-pass filtering imu The difference Δω is calculated; the calculated difference Δω is statistically analyzed, and based on the statistical results, the abnormality of the wheel speed sensor is diagnosed.

[0058] See Figure 2 As shown, the yaw rate output by commonly used vehicle-mounted IMU sensors is high-frequency data, typically around 100Hz, while the wheel speed data output by wheel speed sensors is low-frequency data, typically around 10Hz. Therefore, simply comparing the low-pass filtered IMU yaw rate ω... imu and the heading angular rate ω based on wheel speed odomThe instantaneous difference between these two factors, and the use of this instantaneous difference to diagnose whether the wheel speed sensor is faulty or whether the output data is accurate, will result in a large error in the diagnostic results.

[0059] Therefore, in this invention, time alignment and statistical data deviation are used to diagnose faults, such as... Figure 1 As shown, step S3 specifically includes steps S31 to S33:

[0060] Step S31, Timestamp Alignment: Determine the heading angular rate ω for each wheel speed. odom The corresponding first timestamp, and the heading angular rate ω of each IMU after low-pass filtering. imu For each corresponding second timestamp, find the second timestamp closest to each first timestamp (the time difference between the two timestamps is less than or equal to a preset time difference threshold), and calculate the heading angular rate ω based on wheel speed for each close first timestamp and second timestamp. odom and the IMU heading angular rate ω after low-pass filtering imu The difference Δω (hereinafter referred to as "heading angular rate deviation Δω").

[0061] In step S31 above, the heading angular rate ω is based on the wheel speed. odom The corresponding first timestamp is used to calculate the heading angular rate ω based on wheel speed. odom The timestamps corresponding to the left and right wheel speeds can be obtained from the output data of the wheel speed sensors; the IMU heading angular rate ω after low-pass filtering. imu The corresponding second timestamp is the IMU heading angular rate ω before low-pass filtering. imu The corresponding timestamp can be obtained from the IMU's output data.

[0062] It should be noted that the time difference threshold should be set according to the accuracy (e.g., frequency) of the wheel speed sensor and IMU. For example, when the accuracy of both the IMU and the wheel speed sensor is relatively high, the time difference threshold should also be set relatively high.

[0063] If the computing device has sufficient cache space, in some embodiments, step S31 is implemented as follows:

[0064] (1) The heading angular rate ω based on wheel speed is calculated by the cache. odom And cache the heading angular rate ω of each IMU after low-pass filtering. imu ;

[0065] (2) Determine the first or second timestamp corresponding to each cached data. For each first timestamp, calculate the time difference between each second timestamp and the current first timestamp. If the time difference is less than the preset time difference threshold, then determine the second timestamp as the second timestamp closest to the current first timestamp.

[0066] (3) Calculate and cache the heading angular rate ω based on wheel speed corresponding to the first and second timestamps that are close to each other. odom and the IMU heading angular rate ω after low-pass filtering imu The difference Δω.

[0067] To save cache space, in some embodiments, step S31 can also be implemented as follows:

[0068] (1) The heading angular rate ω based on wheel speed is calculated in real time. odom And the real-time calculation of the heading angular rate ω of each IMU after low-pass filtering. imu ;

[0069] (2) Determine the first or second timestamp corresponding to each cached data in real time. For each first timestamp, calculate the time difference between each second timestamp and the current first timestamp. If the time difference is less than the preset time difference threshold, then determine the second timestamp as the second timestamp closest to the current first timestamp.

[0070] (3) Calculate and cache the heading angular rate ω based on wheel speed corresponding to the first and second timestamps that are close to each other. odom and the IMU heading angular rate ω after low-pass filtering imu The difference Δω.

[0071] In step S31, due to the limited buffer space, as time goes by, when the latest calculated heading angular rate deviation Δω enters the buffer area, the earliest heading angular rate deviation Δω in the buffer area will be deleted.

[0072] Step S32, Statistical data deviation: Obtain the heading angular rate deviation Δω of the latest m frames obtained before the current time, and calculate the data deviation according to the following formula:

[0073]

[0074] Where σ is the statistical bias; m is a natural number; Δω m This represents the deviation of the heading angular rate in the m-th frame obtained before the current moment.

[0075] It should be noted that, in practice, the specific value of m can be set according to computing power, equipment buffer space, and the normal moving speed of mobile devices. If the value of m is too large, the time span of the statistical time deviation will be long, which may result in missed detections.

[0076] The most recently obtained m-frame heading angular rate deviation Δω before the current moment is the most recently stored m heading angular rate deviations Δω in the buffer.

[0077] Step S33, perform diagnosis based on statistical results: determine whether the statistical deviation σ is greater than the preset deviation threshold. If the statistical deviation σ is greater than the preset deviation threshold, it is determined that the wheel speed sensor is abnormal and the vehicle speed measured by the wheel speed sensor is abnormal. If the statistical deviation σ is less than or equal to the preset deviation threshold, it is determined that the wheel speed sensor is normal and the vehicle speed measured by the wheel speed sensor is normal.

[0078] It should be noted that the preset deviation threshold can be changed according to the change of m in Formula 2. The larger the value of m, the larger the preset deviation threshold will be.

[0079] When the statistical deviation σ is greater than the preset deviation threshold, it indicates that there is a large deviation between the heading angular rate calculated based on the left and right wheel speeds of the wheel speed sensor and the heading angular rate measured by the IMU. In this case, it is considered that the wheel speed sensor has malfunctioned. In order to avoid false alarms caused by noise, a sliding window is added to smooth and denoise the data.

[0080] This invention first uses timestamp alignment to find time-close yaw rate (low-frequency data) and IMU yaw rate (high-frequency data) based on wheel speed, and calculates the difference between these two types of data to obtain the yaw rate deviation Δω at the alignment point. This process fully considers the problem of inconsistent data refresh frequencies between the inertial measurement unit and the wheel speed sensor (generally, the left and right wheel speeds collected by the wheel speed sensor are low-frequency data, and the yaw rate calculated from them is also low-frequency data, while the yaw rate collected by the IMU is high-frequency data. There is a large timestamp difference between the low-frequency data and the high-frequency data. If they are directly compared, the comparability is poor and it cannot reflect the true condition of the wheel speed sensor).

[0081] Furthermore, this invention statistically analyzes the obtained multi-frame statistical heading angular rate deviation Δω, which avoids the errors that easily occur when comparing single-frame data, resulting in more reliable diagnostic results.

[0082] The present invention provides a fault diagnosis method for a wheel speed sensor of a mobile device, which can accurately determine whether the vehicle speed information measured by the wheel speed sensor is accurate, thereby effectively avoiding positioning deviations caused by inaccurate wheel speed sensor measurements and potential safety problems.

[0083] Based on the fault diagnosis method for wheel speed sensors of mobile devices provided in this invention, and based on the same inventive concept, this invention also provides a fault diagnosis device for wheel speed sensors of mobile devices, such as... Figure 3 As shown, the device includes:

[0084] The acquisition unit 31 is used to acquire the vehicle's left wheel speed and right wheel speed output by the wheel speed sensor, and the IMU heading angular rate output by the inertial measurement unit, and to perform low-pass filtering on the acquired data.

[0085] Rate calculation unit 32 is used to calculate the heading angular rate ω based on the wheel speeds of the vehicle's left and right wheels after low-pass filtering. odom ;

[0086] Alignment unit 33 is used for aligning the heading angular rate ω based on wheel speed. odom and the IMU heading angular rate ω after low-pass filtering imu Alignment is performed according to timestamps;

[0087] The difference calculation unit 34 is used to calculate the heading angular rate ω based on wheel speed for timestamp alignment. odom and the IMU heading angular rate ω after low-pass filtering imu The difference Δω;

[0088] Statistical unit 35 is used to perform statistics on the latest multi-frame difference Δω;

[0089] The diagnostic unit 36 ​​is used to diagnose whether the wheel speed sensor is abnormal based on statistical results.

[0090] Figure 3 The fault diagnosis device shown works on the same principle as the fault diagnosis method for the wheel speed sensor of the mobile device provided by the present invention. For details, please refer to the foregoing description, which will not be repeated here.

[0091] Based on the fault diagnosis method for wheel speed sensors in mobile devices provided by this invention, and based on the same inventive concept, this invention also provides a mobile device, such as... Figure 4 As shown, the mobile device includes: a mobile body 41, a wheel speed sensor 42, an inertial measurement unit 43, and a fault diagnosis device 44.

[0092] Among them, the mobile body 41 is a structure that moves using at least two sets of symmetrically arranged wheels (left wheel and right wheel); the mobile device can be an unmanned sweeper, an unmanned water sprinkler, an unmanned floor scrubber, an unmanned logistics vehicle, or other unmanned vehicles.

[0093] Figure 4In the mobile device shown, the wheel speed sensor 42 is responsible for measuring information such as the speed of the wheels, and the inertial measurement unit 43 is responsible for measuring information such as the three-axis attitude angle, yaw rate, and acceleration of the mobile body 41. The working principle of the fault diagnosis device 44 is the same as the fault diagnosis method of the wheel speed sensor 42 of the mobile device provided by the present invention. For details, please refer to the foregoing description, and it will not be repeated here.

[0094] Figure 4 When the fault diagnosis device 44 diagnoses the mobile device as having an abnormal wheel speed sensor 42 and / or an abnormal vehicle speed measurement, it can promptly issue an alarm and / or take safety measures to ensure the safe and stable operation of the mobile device.

[0095] In practice, after diagnosing a fault in the wheel speed sensor, the fault diagnosis device can send a fault signal to other functional modules of the mobile device (such as modules that perform functions such as obstacle perception, path planning, peripheral device control, and service dispatch), so that the other functional modules can take corresponding safety measures, such as stopping the movement.

[0096] The present invention employs a method of mutual verification with other sensors to diagnose wheel speed sensor faults. It can comprehensively utilize information from other sensors to accurately determine the fault status of the wheel speed sensor, thereby ensuring the safe and stable operation of the vehicle.

[0097] In summary, compared with existing technologies, this invention provides a fault diagnosis method, a fault diagnosis device, and a mobile device for wheel speed sensors. By employing a method of cross-verification using the heading angular rate output by the same inertial measurement unit (IMU), this invention can accurately determine the fault condition of the wheel speed sensor, ensuring the safe and stable operation of the vehicle.

[0098] By applying this invention, on the one hand, abnormal vehicle speed information (i.e., wheel speed data) output by the wheel speed sensor can be accurately isolated, and on the other hand, the vehicle safety risks caused by abnormal wheel speed sensors can be reduced.

[0099] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A fault diagnosis method for a wheel speed sensor of a mobile device, characterized in that, Includes the following steps: The left wheel speed and right wheel speed output from the wheel speed sensor, as well as the heading angular rate output from the inertial measurement unit, are collected respectively, and the collected data are processed by low-pass filtering. Using the left and right wheel speeds of the vehicle after low-pass filtering, the heading angular rate based on the wheel speeds is calculated. ; For heading angular rate based on wheel speed and the IMU heading angular rate after low-pass filtering Alignment is performed based on timestamps, and the heading angular rate based on wheel speed is calculated for timestamp alignment. and the IMU heading angular rate after low-pass filtering The difference ; The latest obtained multi-frame interpolation Perform statistical analysis and, based on the results, diagnose whether the wheel speed sensor is malfunctioning. For heading angular rate based on wheel speed and the IMU heading angular rate after low-pass filtering Alignment is performed based on timestamps, and the heading angular rate based on wheel speed is calculated for timestamp alignment. and the IMU heading angular rate after low-pass filtering The difference ,include: Determine the heading angular rate based on each wheel speed. The corresponding first timestamp, and the heading angular rate of each IMU after low-pass filtering. The corresponding second timestamp; Find the second timestamp that is closest to each of the first timestamps; Calculate the heading angular rate based on wheel speed for each close first and second timestamp. and the IMU heading angular rate after low-pass filtering The difference .

2. The method as described in claim 1, characterized in that, The collected data is subjected to low-pass filtering, including using a low-pass filter with a cutoff frequency less than half the sampling frequency of the wheel speed sensor and the inertial measurement unit.

3. The method as described in claim 2, characterized in that, A Butterworth low-pass filter is used to perform low-pass filtering on the acquired data.

4. The method as described in claim 1, characterized in that, Find the second timestamp that is closest to each first timestamp, including: For each first timestamp, calculate the time difference between each second timestamp and the current first timestamp. If the time difference is less than a preset time difference threshold, then determine the second timestamp as the second timestamp closest to the current first timestamp.

5. The method as described in claim 1, characterized in that, The latest obtained multi-frame interpolation Statistical analysis was conducted, including: Get the latest m-frame heading angular rate deviation up to the current time. The data deviation is calculated using the following formula: ； in, Statistical bias; m is a natural number; This represents the deviation of the heading angular rate in the m-th frame obtained before the current moment.

6. The method as described in claim 5, characterized in that, Based on the statistical results, diagnosing whether the wheel speed sensor is malfunctioning includes: If statistical bias If the deviation exceeds the preset threshold, the wheel speed sensor is diagnosed as malfunctioning.

7. A fault diagnosis device for a wheel speed sensor of a mobile device, characterized in that, include: The data acquisition unit is used to acquire the left wheel speed and right wheel speed output by the wheel speed sensor, and the heading angular rate output by the inertial measurement unit, and to perform low-pass filtering on the acquired data. The rate calculation unit is used to calculate the heading angular rate based on the wheel speeds of the vehicle's left and right wheels after low-pass filtering. ; Alignment unit for azimuth rate based on wheel speed and the IMU heading angular rate after low-pass filtering Alignment is performed according to timestamps; The difference calculation unit is used to calculate the heading angular rate based on wheel speed for timestamp alignment. and the IMU heading angular rate after low-pass filtering The difference Specifically used for the heading angular rate based on wheel speed. and the IMU heading angular rate after low-pass filtering Alignment is performed based on timestamps, and the heading angular rate based on wheel speed is calculated for timestamp alignment. and the IMU heading angular rate after low-pass filtering The difference This includes: determining each heading angular rate based on wheel speed. The corresponding first timestamp, and the heading angular rate of each IMU after low-pass filtering. The corresponding second timestamp; find the second timestamp closest to each first timestamp; calculate the heading angular rate based on wheel speed for each close first timestamp and second timestamp. and the IMU heading angular rate after low-pass filtering The difference ; Statistical unit, used to analyze the latest multi-frame differences To conduct statistics; The diagnostic unit is used to diagnose whether the wheel speed sensor is malfunctioning based on statistical results.

8. A mobile device, characterized in that, include: A moving body having at least two sets of symmetrically arranged left and right wheels; Wheel speed sensors used to measure the speed of the left and right wheels of the moving body; An inertial measurement unit used to measure the heading angular rate of the IMU of the moving body; And, the fault diagnosis device for the wheel speed sensor of a mobile device as described in claim 7.

9. The mobile device as described in claim 8, characterized in that, The mobile device is an unmanned vehicle.

10. A mobile device, characterized in that, The mobile device includes a memory and a processor, the memory being used to store a program, and the processor, when running the program, being used to perform the method according to any one of claims 1-6.

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