Fault detection method and device of self-moving device and self-moving device

Abstract

The application discloses a fault detection method and device of a self-moving device and the self-moving device. The method comprises the following steps: during the movement of the self-moving device, performing an abnormality detection on a mileage counter every first preset time to obtain a detection result; updating state information of the mileage counter based on the detection result; and determining that a walking wheel of the self-moving device is faulty if the updated state information meets a preset fault condition. The technical scheme provided by the application can determine whether the walking wheel is faulty based on the mileage counter, so that the self-moving device can be controlled correspondingly.

Description

Technical Field

[0001] This application belongs to the field of equipment control technology, and in particular relates to a fault detection method, device and self-moving equipment. Background Technology

[0002] Self-moving devices are intelligent devices that can move autonomously. As a typical indoor self-moving device, a robotic vacuum cleaner needs to use wheels to move on a flat surface.

[0003] When the control circuit of the traveling wheels malfunctions, such as a short circuit, the actual direction of movement of the wheels may differ from the required direction of movement indicated by the electrical level, posing a risk of falls to the self-moving device. Therefore, it is necessary to provide a method for fault detection of the traveling wheels on the navigation side in order to control the self-moving device accordingly. Summary of the Invention

[0004] The embodiments of this application provide a fault detection method, device, and self-moving device for a self-moving device, which can determine whether there is a fault in the walking wheels based on the odometer, thereby controlling the self-moving device accordingly.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to a first aspect of the embodiments of this application, a fault detection method for a self-moving device is provided. The self-moving device includes a device body, and wheels and an odometer disposed on the device body. The method includes:

[0007] During the movement of the self-moving device, the odometer is checked for anomalies every first preset time interval, and the detection results are obtained. Update the odometer status information based on the detection results; If the updated status information meets the preset fault conditions, then the self-moving device's walking wheels are determined to be faulty.

[0008] In some embodiments of this application, based on the foregoing scheme, anomaly detection includes the following steps: Acquire at least one of first detection information and second detection information; wherein, the first detection information includes the first change direction of the yaw angle of the odometer and the second change direction of the yaw angle of the gyroscope, and the second detection information includes the required movement direction and the actual movement direction of the wheels; If the first direction of change is inconsistent with the second direction of change, or if the required direction of motion is inconsistent with the actual direction of motion, then the detection result of the current anomaly detection is determined to be that the odometer is abnormal.

[0009] In some embodiments of this application, based on the aforementioned scheme, the status information includes cumulative anomaly information, which includes cumulative anomaly time or cumulative anomaly count; Based on the detection results, update the odometer's status information, including: If the test result indicates that the odometer is abnormal, then add cumulative abnormal information; If the test result shows that the odometer is not abnormal, then the accumulated abnormal information will be reduced.

[0010] In some embodiments of this application, based on the aforementioned scheme, accumulated anomaly information is added, including: Add the second preset time to the cumulative abnormal time to obtain the updated cumulative abnormal time; Reduce accumulated anomalies, including: Subtract the second preset time from the cumulative abnormal time to obtain the updated cumulative abnormal time; the minimum value of the cumulative abnormal time is 0ms.

[0011] In some embodiments of this application, based on the aforementioned scheme, the updated status information satisfies the preset fault conditions, including the updated cumulative abnormal time reaching a third preset time.

[0012] In some embodiments of this application, based on the aforementioned scheme, accumulated anomaly information is added, including: Add the cumulative number of anomalies once to get the updated cumulative number of anomalies; Reduce accumulated anomalies, including: Subtract one from the cumulative number of anomalies to get the updated cumulative number of anomalies; the minimum value of the cumulative number of anomalies is 0.

[0013] In some embodiments of this application, based on the aforementioned scheme, the updated status information satisfies preset fault conditions, including the updated cumulative number of anomalies reaching a preset number.

[0014] In some embodiments of this application, based on the foregoing scheme, obtaining first detection information includes: Get the current frame yaw angle and the previous frame yaw angle of the odometry, and get the current frame yaw angle and the previous frame yaw angle of the gyroscope. The first direction of change is determined based on the yaw angle of the current frame and the yaw angle of the previous frame from the odometry, and the second direction of change is determined based on the yaw angle of the current frame and the yaw angle of the previous frame from the gyroscope.

[0015] In some embodiments of this application, based on the foregoing scheme, obtaining second detection information includes: Acquire pulse signals for controlling the walking wheels; wherein the walking wheels include a left wheel and a right wheel; The desired directions of motion for the left wheel and the right wheel are determined based on pulse signals. Get the current frame data and the previous frame data of the odometer counter; The actual movement directions of the left wheel and the right wheel are determined based on the current frame data and the previous frame data, respectively.

[0016] In some embodiments of this application, based on the foregoing scheme, the method further includes: Obtain the attitude angles of the mobile device; wherein the attitude angles include at least one of pitch angle and roll angle; An anomaly detection is performed on the self-moving device's odometer every first preset time interval, including: If the attitude angle is not greater than the preset angle, the odometer of the self-moving device will be checked for anomalies every first preset time interval.

[0017] In some embodiments of this application, after determining that the self-moving device's wheels are faulty based on the foregoing scheme, the method further includes: Control the self-moving device to stop moving and handle fault alarms; The fault alarm is handled in at least one of the following ways: Fault alarm processing is performed via the lighting and / or voice devices of self-moving equipment; Send fault alarm information to the associated terminal of the self-moving device so that the fault alarm information can be displayed on the associated terminal.

[0018] According to a second aspect of the embodiments of this application, a fault detection device for a self-moving device is provided. The self-moving device includes a device body, and wheels and an odometer disposed on the device body. The device includes: An anomaly detection module is used to perform anomaly detection on the odometer every first preset time interval during the movement of the self-moving device and obtain the detection result; The information update module is used to update the status information of the odometer based on the detection results; The fault diagnosis module is used to determine that the self-moving device's walking wheels are faulty if the updated status information meets the preset fault conditions.

[0019] In some embodiments of this application, based on the foregoing scheme, the anomaly detection module includes: An information acquisition unit is used to acquire at least one of first detection information and second detection information; wherein, the first detection information includes the first change direction of the yaw angle of the odometer and the second change direction of the yaw angle of the gyroscope, and the second detection information includes the required movement direction and the actual movement direction of the wheels; The anomaly detection unit is used to determine that the odometer is abnormal if the first change direction is inconsistent with the second change direction, or if the required movement direction is inconsistent with the actual movement direction.

[0020] In some embodiments of this application, based on the aforementioned scheme, the status information includes cumulative anomaly information, which includes cumulative anomaly time or cumulative anomaly count; The information update module includes: The first update unit is used to add cumulative abnormal information if the detection result indicates that the odometer is abnormal; The second update unit is used to reduce the accumulated abnormal information if the detection result shows that the odometer is not abnormal.

[0021] In some embodiments of this application, based on the aforementioned scheme, the first update unit is specifically used to add the cumulative abnormal time to the second preset time to obtain the updated cumulative abnormal time; The second update unit is specifically used to subtract the second preset time from the cumulative abnormal time to obtain the updated cumulative abnormal time; the minimum value of the cumulative abnormal time is 0ms.

[0022] In some embodiments of this application, based on the aforementioned scheme, the updated status information satisfies the preset fault conditions, including the updated cumulative abnormal time reaching a third preset time.

[0023] In some embodiments of this application, based on the aforementioned scheme, the first update unit is specifically used to add the cumulative number of anomalies once to obtain the updated cumulative number of anomalies; The second update unit is specifically used to subtract one from the cumulative number of anomalies to obtain the updated cumulative number of anomalies; the minimum value of the cumulative number of anomalies is 0.

[0024] In some embodiments of this application, based on the aforementioned scheme, the updated status information satisfies preset fault conditions, including the updated cumulative number of anomalies reaching a preset number.

[0025] In some embodiments of this application, based on the foregoing scheme, the information acquisition unit includes: The yaw angle acquisition subunit is used to acquire the yaw angle of the current frame and the yaw angle of the previous frame from the odometer, and to acquire the yaw angle of the current frame and the yaw angle of the previous frame from the gyroscope. The first information acquisition subunit is used to determine a first change direction based on the yaw angle of the current frame and the yaw angle of the previous frame from the odometer, and to determine a second change direction based on the yaw angle of the current frame and the yaw angle of the previous frame from the gyroscope.

[0026] In some embodiments of this application, based on the foregoing scheme, the information acquisition unit includes: The signal acquisition subunit is used to acquire pulse signals for controlling the walking wheels; wherein the walking wheels include a left wheel and a right wheel; The demand direction determination subunit is used to determine the demand motion direction of the left wheel and the right wheel respectively based on pulse signals; The counter reading subunit is used to obtain the current frame data and the previous frame data of the odometer's counter; The second information acquisition subunit is used to determine the actual movement direction of the left wheel and the actual movement direction of the right wheel based on the current frame data and the previous frame data, respectively.

[0027] In some embodiments of this application, based on the foregoing scheme, the apparatus further includes: An angle acquisition module is used to acquire the attitude angles of the self-moving device; wherein the attitude angles include at least one of pitch angle and roll angle; The anomaly detection module is specifically used to perform anomaly detection on the odometer of the self-moving device every first preset time interval if the attitude angle is not greater than a preset angle.

[0028] In some embodiments of this application, based on the foregoing scheme, the device further includes a device control module, which is used to control the self-moving device to stop moving and to perform fault alarm processing; The fault alarm is handled in at least one of the following ways: Fault alarm processing is performed via the lighting and / or voice devices of self-moving equipment; Send fault alarm information to the associated terminal of the self-moving device so that the fault alarm information can be displayed on the associated terminal.

[0029] According to a third aspect of the embodiments of this application, a self-moving device is provided, the device including a device body, and a walking wheel, an odometer and a controller disposed on the device body, the controller being used to perform the steps of the fault detection method of the self-moving device as described in any of the first aspects.

[0030] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, which stores computer program instructions that, when loaded and executed by a processor, implement the steps of the fault detection method for a self-moving device as described in any of the first aspects.

[0031] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of a fault detection method for a self-moving device as described in any of the first aspects.

[0032] In this application, the self-moving device includes a main body, wheels and an odometer mounted on the main body. During the movement of the self-moving device, the odometer is checked for abnormalities at first preset intervals to obtain the detection results. Based on the detection results, the status information of the odometer is updated. By periodically updating the status information of the odometer and determining whether the wheels of the self-moving device are malfunctioning based on the updated status information, it is possible to detect abnormalities in the wheels in a timely manner. That is, if the updated status information meets the preset fault conditions, it is determined that the wheels of the self-moving device are malfunctioning, and the self-moving device is controlled to stop moving. This allows for timely detection of wheel malfunctions and prevents the self-moving device from falling or being bumped.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 The diagram illustrates an application scenario of the fault detection method for a self-moving device according to an embodiment of this application. Figure 2 A flowchart of a fault detection method for a self-moving device according to an embodiment of this application is shown; Figure 3 A flowchart of anomaly detection in an embodiment of this application is shown; Figure 4 A flowchart illustrating the updating of status information in an embodiment of this application is shown; Figure 5 A flowchart illustrating the acquisition of first detection information in an embodiment of this application is shown; Figure 6 A flowchart illustrating the acquisition of second detection information in an embodiment of this application is shown; Figure 7 Another flowchart of the fault detection method for a self-moving device according to an embodiment of this application is shown; Figure 8 A block diagram of a fault detection device for a self-moving device according to an embodiment of this application is shown; Figure 9 A schematic diagram of the structure of an electronic device in an embodiment of this application is shown. Detailed Implementation

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0036] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0037] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0038] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0039] The self-moving device moves on a plane using wheels, which include a left wheel and a right wheel. The control circuit for these wheels contains a switching device, such as a MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) controlling the left wheel. When the MOSFET short-circuits, the actual direction of movement of the left wheel is opposite to the required direction indicated by the voltage level, posing a risk of the self-moving device falling. The MOSFET includes a gate, drain, and source. For example, when the resistance between the gate and drain is abnormally low, a short circuit is detected, the voltage is 5V, the left wheel reverses, the right wheel moves normally, and the self-moving device continues to move backward. When the resistance between the gate and source is abnormally low, a short circuit is detected, the voltage is low, the left wheel can only rotate forward, the right wheel moves normally, and the self-moving device reports a "waiting to move forward" status. When the resistance between the drain and source is abnormally low, a short circuit is detected, the voltage is low, the left wheel can only rotate forward, the right wheel moves normally, and the self-moving device reports a "waiting to move forward" status.

[0040] Currently, it is difficult to quickly and effectively detect short-circuit faults on the hardware and drive sides during the movement of self-moving devices. In order to avoid the actual movement direction of the wheels being inconsistent with the required movement direction, which could lead to the risk of falling of the self-moving device, this application provides a method for fault detection of the wheels on the navigation side. This method can determine whether there is a fault in the wheels based on the odometer, thereby enabling corresponding control of the self-moving device.

[0041] To enable those skilled in the art to better understand this application, firstly, in conjunction with Figure 1 A brief description of the application scenarios involved in this application is provided.

[0042] See Figure 1 The diagram illustrates an application scenario of the fault detection method for a self-moving device according to an embodiment of this application.

[0043] The self-moving device includes a main body, wheels, an odometer, and a controller mounted on the main body. During movement, the controller performs an anomaly detection on the odometer at preset time intervals, obtains the detection results, and updates the odometer's status information based on these results. If the updated status information meets preset fault conditions, a wheel malfunction is determined, and the self-moving device is stopped. The controller controls the self-moving device's lighting and / or voice functions, or sends fault alarm messages to associated terminals to handle fault alarms. Associated terminals can be, but are not limited to, various smartphones, tablets, and IoT devices.

[0044] In one exemplary embodiment, the self-moving device includes a device body, and wheels and an odometer disposed on the device body, referring to... Figure 2 The flowchart of the fault detection method for self-moving devices in the embodiments of this application is shown below, and is described in detail below: Step 201: During the movement of the self-moving device, an anomaly detection is performed on the odometer every first preset time interval to obtain the detection result.

[0045] An odometer records sensor data from the wheels of a self-moving device, allowing the determination of the device's relative motion information, including displacement and rotation. By detecting anomalies in the odometer, the potential for malfunctions in the self-moving device can be assessed. This allows for rapid fault detection based on the odometer's representation of the device's actual motion, even when timely inspection of the control circuitry is difficult.

[0046] Specifically, when the self-moving device starts working, it performs anomaly detection on the odometer according to a preset cycle. The preset cycle can be set to a first preset time interval, meaning that anomaly detection is performed on the odometer every first preset time interval, and a corresponding detection result is obtained for each anomaly detection. Periodic anomaly detection can avoid frequent alarms from the self-moving device in a short period of time, and can achieve continuous monitoring of the self-moving device.

[0047] Anomaly detection operations may include, but are not limited to, determining whether the motion data corresponding to the odometer and gyroscope match, and determining whether the coordinates of the odometer match the coordinates on the map. The first preset time can be set to 100 seconds; this embodiment does not impose a specific limitation on this.

[0048] Optionally, an anomaly detection operation can be performed by determining whether the motion quantities corresponding to the odometer and gyroscope match. For example, a predicted angular velocity is calculated based on sensor data recorded by the odometer, and an observed angular velocity is calculated based on sensor data recorded by the gyroscope. The predicted angular velocity is then compared with the observed angular velocity. If they match or the error is below a small threshold, the odometer is considered normal in the current anomaly detection. Conversely, if they do not match or the error is above a small threshold, the odometer is considered abnormal in the current anomaly detection.

[0049] Optionally, an anomaly detection operation can be performed by determining whether the coordinates of the odometer match the coordinates on the map. For example, based on the initial coordinates of the mobile device and the sensor data recorded by the odometer, the predicted coordinates of the mobile device are calculated, and then the observed coordinates of the mobile device on the map are obtained. The predicted coordinates are compared with the observed coordinates. If they match or the error is less than a small threshold, the odometer is considered normal in the current anomaly detection. Conversely, if they do not match or the error is greater than a small threshold, the odometer is considered abnormal in the current anomaly detection.

[0050] Step 202: Update the status information of the odometer based on the detection results.

[0051] After each anomaly detection, the odometer status information is updated based on the detection results of the current anomaly detection. The results of multiple anomaly detections are combined to perform fault analysis, thereby avoiding the situation where accidental factors cause odometer anomalies in a single anomaly detection and lead to misjudgment of faults. Accidental factors include situations where the self-moving device's wheels briefly slip when they briefly run over obstacles such as carpet edges or wires, or situations where the self-moving device's wheels briefly lift off the ground when it crosses obstacles such as thresholds.

[0052] Optionally, the status information is a health score. An initial health score, such as 100 points, is set for the odometer. When the detection result is that the odometer is abnormal, a preset abnormal score, such as 10 points, is deducted from the health score. When the detection result is that the odometer is normal, a preset normal score, such as 2 points, is added to the health score. The maximum health score is 100 points. That is, when the health score reaches 100 points, no further points will be added even if the odometer is detected to be normal.

[0053] Step 203: If the updated status information meets the preset fault conditions, then the walking wheel of the self-moving device is determined to be faulty.

[0054] After multiple anomaly detections, the status information of the odometer is continuously updated until the status information after a certain update meets the preset fault conditions. At this point, it is determined that the walking wheel of the self-moving device is faulty, rather than an anomaly caused by accidental factors. The self-moving device can then be controlled to stop moving to prevent it from falling.

[0055] Optionally, the status information is a health score, and the preset fault condition is that the health score is lower than a preset threshold, such as 60 points.

[0056] In this application, the self-moving device includes a main body, wheels and an odometer mounted on the main body. During the movement of the self-moving device, the odometer is checked for abnormalities at first preset intervals to obtain the detection results. Based on the detection results, the status information of the odometer is updated. By periodically updating the status information of the odometer and determining whether the wheels of the self-moving device are malfunctioning based on the updated status information, it is possible to detect abnormalities in the wheels in a timely manner. That is, if the updated status information meets the preset fault conditions, it is determined that the wheels of the self-moving device are malfunctioning, and the self-moving device is controlled to stop moving. This allows for timely detection of wheel malfunctions and prevents the self-moving device from falling or being bumped.

[0057] Based on the above embodiments, in an exemplary embodiment, see [link to example]. Figure 3 This illustrates the anomaly detection method in an embodiment of this application, specifically including: Step 301: Obtain at least one of the first detection information and the second detection information.

[0058] The first detection information includes the first change direction of the yaw angle of the odometer and the second change direction of the yaw angle of the gyroscope. The second detection information includes the required movement direction and the actual movement direction of the walking wheels. The yaw angle refers to the plane orientation angle of the self-moving device during movement.

[0059] Specifically, the driving wheels include a left wheel and a right wheel. When the speed of the left wheel is higher than that of the right wheel, the self-moving device turns left; conversely, when the speed of the left wheel is lower than that of the right wheel, the self-moving device turns right. The speeds of the left and right wheels are read separately, and the yaw angle of the odometer is calculated based on the speed difference between the left and right wheels to determine the first direction of change. The rotational angular velocity of the gyroscope is read, and the yaw angle of the gyroscope is calculated based on the rotational angular velocity to determine the second direction of change.

[0060] The desired direction of movement for the wheels refers to the planned direction of movement, while the actual direction of movement refers to the actual direction of movement. The desired direction of movement for the wheels is determined by recognizing control commands, and the actual direction of movement is determined by reading sensor data recorded by the odometer.

[0061] Step 302: If the first direction of change is inconsistent with the second direction of change, or the required direction of motion is inconsistent with the actual direction of motion, then the detection result of the current anomaly detection is determined to be that the odometer is abnormal.

[0062] Optionally, only the first detection information is obtained to determine whether the first change direction is consistent with the second change direction. If so, the detection result is determined to be that the odometer is normal; otherwise, the detection result is determined to be that the odometer is abnormal.

[0063] Optionally, only the second detection information is obtained to determine whether the required direction of motion is consistent with the actual direction of motion. If so, the detection result is determined to be that the odometer is normal; otherwise, the detection result is determined to be that the odometer is abnormal.

[0064] Optionally, first and second detection information are acquired to determine whether the first and second change directions are consistent, and whether the required motion direction and the actual motion direction are consistent. If either of these is inconsistent, the detection result can be determined as an odometer anomaly. Comprehensive analysis from both aspects can improve the accuracy of the detection results. It should be noted that the first and second change directions can be compared first, and if they are consistent, the required motion direction and the actual motion direction can be compared. Alternatively, the required motion direction and the actual motion direction can be compared first, and if they are consistent, the first and second change directions can be compared. Furthermore, the change direction and the motion direction can be compared simultaneously. This embodiment does not impose specific limitations on the implementation steps of the anomaly detection.

[0065] This application provides three specific methods for anomaly detection: comparing the yaw angle changes of the odometer and gyroscope, comparing the required and actual movement directions of the wheels, and comparing both simultaneously. This allows for anomaly detection of the odometer from the navigation side, identifying abnormal situations such as the left wheel rotating in reverse or only rotating forward when the control circuit is short-circuited, thus ensuring the accuracy of anomaly detection.

[0066] Based on the above embodiments, in an exemplary embodiment, the status information includes cumulative anomaly information, which includes cumulative anomaly time or cumulative anomaly count. See [link to relevant documentation]. Figure 4 This illustrates the method for updating status information in embodiments of this application, specifically including: Step 401: If the detection result indicates that the odometer is abnormal, then add cumulative abnormal information.

[0067] For each anomaly detection, if the detection result indicates that the odometer is abnormal, the corresponding cumulative anomaly information is added to the cumulative anomaly information updated in the previous anomaly detection. If the detection result of the first anomaly detection indicates that the odometer is abnormal, the corresponding cumulative anomaly information is added to the initial cumulative anomaly information.

[0068] Optionally, the cumulative anomaly information is the cumulative anomaly time. The updated cumulative anomaly time is obtained by adding a second preset time. For example, for each anomaly detection, if the detection result indicates an odometer anomaly, the second preset time is added to the cumulative anomaly time updated in the previous anomaly detection. The second preset time can be the same as the first preset time, such as 100 seconds, or it can be different. The initial cumulative anomaly time corresponding to the first anomaly detection can be 0 seconds. When the second preset time is the same as the first preset time, the calculation is more convenient, and the degree of odometer anomaly can be determined more intuitively based on the cumulative anomaly time.

[0069] Optionally, the cumulative anomaly information is the cumulative number of anomalies. The cumulative number of anomalies is incremented by one to obtain the updated cumulative number of anomalies. For example, for each anomaly detection, if the detection result is that the odometer is abnormal, one more is added to the cumulative number of anomalies updated in the previous anomaly detection. The initial cumulative number of anomalies for the first anomaly detection is 0.

[0070] Step 402: If the detection result shows that the odometer is not abnormal, then reduce the accumulated abnormal information.

[0071] For each anomaly detection, if the detection result is that the odometer is not abnormal, the corresponding cumulative anomaly information is reduced based on the cumulative anomaly information updated in the previous anomaly detection. If the detection result of the first anomaly detection is that the odometer is not abnormal, the corresponding cumulative anomaly information is subtracted based on the initial cumulative anomaly information. The updated cumulative anomaly information must meet specific requirements.

[0072] Optionally, the cumulative anomaly information is the cumulative anomaly time. The updated cumulative anomaly time is obtained by subtracting a second preset time from the cumulative anomaly time. For example, for each anomaly detection, if the detection result indicates that the odometer is not abnormal, the second preset time is subtracted from the cumulative anomaly time updated in the previous anomaly detection. The second preset time can be the same as the first preset time, such as 100 seconds, or it can be different. The initial cumulative anomaly time corresponding to the first anomaly detection can be 0 seconds. When the second preset time is the same as the first preset time, the calculation is more convenient, and the degree of anomaly of the odometer can be determined more intuitively based on the cumulative anomaly time.

[0073] The minimum value of the cumulative abnormal time is 0ms. For example, when subtracting the second preset time from the cumulative abnormal time, if the cumulative abnormal time is less than the second preset time, the cumulative abnormal time is directly set to zero, resulting in an updated cumulative abnormal time of zero. Alternatively, the second preset time can be subtracted from the cumulative abnormal time first, and if the result is negative, the updated cumulative abnormal time can be set to zero.

[0074] Optionally, the cumulative anomaly information is the cumulative number of anomalies. For example, for each anomaly detection, if the detection result is that there is no anomaly in the odometer, one anomaly is subtracted from the cumulative number of anomalies updated in the previous anomaly detection. The initial cumulative number of anomalies for the first anomaly detection is 0.

[0075] The minimum cumulative number of exceptions is 0. For example, if the cumulative number of exceptions is subtracted by one, the cumulative number of exceptions is 0. If the subtraction operation is not performed, the updated cumulative number of exceptions will still be zero. Alternatively, the cumulative number of exceptions can be subtracted by one first, and if the result is negative, the updated cumulative number of exceptions can be set to zero.

[0076] This application provides two specific methods for updating the status information of the odometer: accumulating abnormal time or accumulating abnormal number. By accumulating the detection results of multiple abnormal detections, fault analysis can be performed. This not only avoids the situation where the odometer is abnormal due to accidental factors in a single abnormal detection, resulting in misjudgment of fault, but also provides a reasonable anti-shake strategy to avoid frequent fault alarms from the self-moving device.

[0077] Based on the above embodiments, in an exemplary embodiment, the updated status information satisfies the preset fault conditions, including the updated cumulative abnormal time reaching a third preset time.

[0078] For example, each time an odometer malfunction is detected, a second preset time of 100 seconds is added; each time an odometer is detected to be normal, the second preset time of 100 seconds is subtracted. When the accumulated malfunction time reaches a third preset time of 500 seconds, it can be determined that the odometer malfunction is not caused by accidental factors, the walking wheel malfunction is determined, and the self-moving device is controlled to stop moving.

[0079] The updated status information meets the preset fault conditions, including the updated cumulative number of anomalies reaching the preset number.

[0080] For example, each time an odometer malfunction is detected, one error is added, and each time a normal odometer is detected, one error is subtracted. When the cumulative number of malfunctions reaches a preset number of 5, it can be determined that the odometer malfunction is not caused by accidental factors, the walking wheel is identified as faulty, and the self-moving device is controlled to stop moving.

[0081] In this application, by setting corresponding preset fault conditions based on updating the status information of the odometer, the fault detection logic of the self-moving device can be closed, the fault situation can be accurately measured, and the self-moving device can be controlled accordingly when it reaches a certain level, thereby improving the safety and reliability of device control.

[0082] Based on the above embodiments, in an exemplary embodiment, see [link to example]. Figure 5 This illustrates the method for obtaining the first detection information in an embodiment of this application, specifically including: Step 501: Obtain the yaw angle of the current frame and the yaw angle of the previous frame from the odometer, and obtain the yaw angle of the current frame and the yaw angle of the previous frame from the gyroscope.

[0083] The current frame refers to the current moment, which is the start time of the current anomaly detection cycle. The previous frame refers to the previous moment, which can be a historical moment a certain time before the current moment, such as the historical moment corresponding to the second before the current moment.

[0084] Read the speed of the left wheel and the speed of the right wheel in the current frame, and calculate the yaw angle of the odometer in the current frame based on the speed difference between the left and right wheels in the current frame. Correspondingly, read the speed of the left wheel and the speed of the right wheel in the previous frame, and calculate the yaw angle of the odometer in the previous frame based on the speed difference between the left and right wheels in the previous frame.

[0085] Read the rotational angular velocity of the gyroscope in the current frame and the rotational angular velocity in the previous frame, and calculate the yaw angle of the gyroscope in the current frame and the yaw angle in the previous frame.

[0086] Step 502: Determine the first direction of change based on the yaw angle of the current frame and the yaw angle of the previous frame from the odometer, and determine the second direction of change based on the yaw angle of the current frame and the yaw angle of the previous frame from the gyroscope.

[0087] For the odometer, calculate the difference between the yaw angle of the current frame and the yaw angle of the previous frame. A positive difference indicates that the first direction of change is positive, and the rotation is counterclockwise. A negative difference indicates that the first direction of change is negative, and the rotation is clockwise.

[0088] For the gyroscope, calculate the difference between the yaw angle of the current frame and the yaw angle of the previous frame. A positive difference indicates that the first change direction is positive, and the rotation is counterclockwise. A negative difference indicates that the first change direction is negative, and the rotation is clockwise.

[0089] In this application, the direction of change is determined by differential calculation based on the yaw angle of the current frame and the yaw angle of the previous frame. This allows for focusing on the direction change in each frame and accurately determining the first detection information.

[0090] Based on the above embodiments, in an exemplary embodiment, see [link to example]. Figure 6 This illustrates a method for obtaining the second detection information in an embodiment of this application, specifically including: Step 601: Obtain the pulse signal used to control the walking wheels.

[0091] The walking wheel includes a left wheel and a right wheel. The pulse signals controlling the walking wheel include a first signal controlling the left wheel and a second signal controlling the right wheel, and the first signal and the second signal are acquired at the same time.

[0092] Step 602: Determine the required movement direction of the left wheel and the required movement direction of the right wheel based on the pulse signal.

[0093] The desired direction of movement for the left wheel is determined based on the first signal, and the desired direction of movement for the right wheel is determined based on the second signal. A positive pulse signal indicates a forward movement, while a negative pulse signal indicates a backward movement.

[0094] Step 603: Obtain the current frame data and the previous frame data of the odometer counter.

[0095] The current frame refers to the current moment, which is the start time of the current anomaly detection cycle. The previous frame refers to the previous moment, which can be a historical moment a certain time before the current moment, such as the historical moment corresponding to the second before the current moment.

[0096] The odometer's counter records the number of pulses that rotate the wheels, including data on the left and right wheels, which helps determine the actual direction of the wheels' movement.

[0097] Step 604: Determine the actual movement direction of the left wheel and the actual movement direction of the right wheel based on the current frame data and the previous frame data, respectively.

[0098] For the left wheel, the counter is read to obtain the current frame data and the previous frame data of the left wheel, and the actual movement direction of the left wheel is determined based on the data difference. For the right wheel, the counter is read to obtain the current frame data and the previous frame data of the right wheel, and the actual movement direction of the right wheel is determined based on the data difference.

[0099] A positive data difference indicates that the corresponding actual direction of motion is forward, while a negative data difference indicates that the corresponding actual direction of motion is negative.

[0100] Correspondingly, when comparing the required direction of motion with the actual direction of motion, the direction of motion of the left wheel and the direction of motion of the right wheel are compared separately. If either of them is inconsistent, it can be determined that the required direction of motion and the actual direction of motion are inconsistent.

[0101] In this application, the required movement direction and actual movement direction of the left and right wheels are determined separately, which can simultaneously detect abnormal movement of the left and right wheels and accurately determine the second detection information.

[0102] Based on the above embodiments, in an exemplary embodiment, the attitude angle of the self-moving device is obtained; the attitude angle includes at least one of pitch angle and roll angle, and correspondingly, if the attitude angle is not greater than a preset angle, the odometer of the self-moving device is checked for an anomaly once every first preset time.

[0103] Optionally, when the self-moving device starts working, the pitch and roll angles of the self-moving device are obtained through an accelerometer or gyroscope. Based on the pitch and roll angles, it is determined whether the self-moving device is moving on a plane. Only when the self-moving device is moving on a horizontal plane will the abnormal detection operation of the odometer be performed according to a preset cycle, thereby avoiding misjudgment of faults caused by special working environments.

[0104] For example, the preset angle is 3 degrees. The pitch angle and roll angle are obtained at the same time. When both the pitch angle and roll angle are not greater than 3 degrees, it is determined that the self-moving device is moving on the plane. An anomaly detection is performed on the odometer every 100 seconds. If either the pitch angle or the roll angle is greater than 3 degrees, the anomaly detection operation is exited until the pitch angle and roll angle are both not greater than 3 degrees, and then the anomaly detection operation is restarted.

[0105] Optionally, when the self-moving device starts working, the pitch angle of the self-moving device is obtained, and if the pitch angle is not greater than a preset angle of 3 degrees, the odometer of the self-moving device is checked for an anomaly every first preset time interval.

[0106] Optionally, when the self-moving device starts working, the roll angle of the self-moving device is obtained, and if the roll angle is not greater than a preset angle of 3 degrees, the odometer of the self-moving device is checked for an anomaly every first preset time interval.

[0107] In this application, corresponding preconditions are set for the periodic abnormality detection of the odometer, namely, the pitch angle is not greater than a preset angle, or the roll angle is not greater than a preset angle, or both the pitch angle and the roll angle are not greater than preset angles, so as to ensure that the self-moving device moves on the plane and avoids misjudgment of faults caused by slippage or body tilt, and avoids false alarms of the self-moving device in special working environments.

[0108] Based on the above embodiments, in an exemplary embodiment, after determining that the self-moving device's walking wheels are faulty, the self-moving device is controlled to stop moving and a fault alarm is performed; wherein, the fault alarm is performed in at least one of the following ways: by performing fault alarm processing through the self-moving device's lighting device and / or voice device; by sending fault alarm information to the associated terminal of the self-moving device so that the fault alarm information can be displayed through the associated terminal.

[0109] The self-moving device is equipped with a lighting device and / or a voice device. When a malfunction is detected in the walking wheel, the lighting device can be controlled to flash or emit a light of a specified color to trigger a malfunction alarm. The voice device can also be controlled to provide a voice prompt to the user that the walking wheel is malfunctioning.

[0110] The associated terminal of the self-moving device refers to the terminal held by the user. The user's terminal has a management client installed on the self-moving device. The self-moving device can be bound in the management client. When a fault is found in the walking wheel, the self-moving device can send fault alarm information to the associated terminal based on the communication connection, so as to display the fault alarm to the user on the associated terminal.

[0111] In this application, when a walking wheel malfunction is detected, various methods can be used to handle the fault alarm, promptly alerting the user to the abnormal situation, which helps to improve the intelligence of the self-moving equipment.

[0112] Based on the above embodiments, in an exemplary embodiment, see [link to example]. Figure 7 Another flowchart of the fault detection method for the self-moving device in this application embodiment is shown below, and is described in detail below: Step 701: Obtain the pitch and roll angles of the mobile device.

[0113] Step 702: Determine whether both the pitch angle and roll angle are not greater than the preset angle.

[0114] If yes, proceed to step 703; otherwise, return to step 701.

[0115] Step 703: Obtain at least one of the first detection information and the second detection information.

[0116] Step 704: Determine whether there is an abnormality in the odometer based on the first detection information and / or the second detection information.

[0117] If yes, proceed to step 705; otherwise, proceed to step 706.

[0118] Step 705: Add the first preset time to the cumulative abnormal time to obtain the updated cumulative abnormal time.

[0119] Step 706: Subtract the first preset time from the cumulative abnormal time to obtain the updated cumulative abnormal time. If the updated cumulative abnormal time is negative, then reset the updated cumulative abnormal time to zero.

[0120] Step 707: Determine whether the updated cumulative abnormal time has reached the second preset time.

[0121] If yes, proceed to step 708; otherwise, wait for the first preset time and then return to step 703.

[0122] Step 708: Determine that the self-moving device's wheels are faulty, and control the self-moving device to stop moving.

[0123] Step 709: Perform fault alarm processing.

[0124] In this application, the presence of a fault in the traveling wheel can be determined based on the odometer, thereby enabling corresponding control of the self-moving device.

[0125] The following describes an embodiment of the apparatus described in this application, which can be used to execute the fault detection method for the self-moving device described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the fault detection method for the self-moving device described above in this application.

[0126] See Figure 8 A block diagram of a fault detection device 800 for a self-moving device according to an embodiment of this application is shown, specifically including: Anomaly detection module 801 is used to perform anomaly detection on the odometer every first preset time interval during the movement of the self-moving device and obtain the detection result; The information update module 802 is used to update the status information of the odometer based on the detection results; The fault diagnosis module 803 is used to determine that the walking wheel of the self-moving device is faulty if the updated status information meets the preset fault conditions.

[0127] In an exemplary embodiment, based on the above embodiments, the anomaly detection module 801 includes: An information acquisition unit is used to acquire at least one of first detection information and second detection information; wherein, the first detection information includes the first change direction of the yaw angle of the odometer and the second change direction of the yaw angle of the gyroscope, and the second detection information includes the required movement direction and the actual movement direction of the wheels; The anomaly detection unit is used to determine that the odometer is abnormal if the first change direction is inconsistent with the second change direction, or if the required movement direction is inconsistent with the actual movement direction.

[0128] In an exemplary embodiment, based on the above embodiments, the status information includes cumulative anomaly information, which includes cumulative anomaly time or cumulative anomaly count. The information update module 802 includes: The first update unit is used to add cumulative abnormal information if the detection result indicates that the odometer is abnormal; The second update unit is used to reduce the accumulated abnormal information if the detection result shows that the odometer is not abnormal.

[0129] In an exemplary embodiment, based on the above embodiment, the first update unit is specifically used to add the second preset time to the cumulative abnormal time to obtain the updated cumulative abnormal time; the second update unit is specifically used to subtract the second preset time from the cumulative abnormal time to obtain the updated cumulative abnormal time; the minimum value of the cumulative abnormal time is 0ms.

[0130] In an exemplary embodiment, based on the above embodiments, the updated status information satisfies the preset fault conditions, including the updated cumulative abnormal time reaching a third preset time.

[0131] In an exemplary embodiment, based on the above embodiment, the first update unit is specifically used to add one to the cumulative number of anomalies to obtain the updated cumulative number of anomalies; the second update unit is specifically used to subtract one from the cumulative number of anomalies to obtain the updated cumulative number of anomalies; the minimum value of the cumulative number of anomalies is 0.

[0132] In an exemplary embodiment, based on the above embodiments, the updated status information satisfies the preset fault conditions, including the updated cumulative number of anomalies reaching a preset number.

[0133] In one exemplary embodiment, based on the above embodiments, the information acquisition unit includes: The yaw angle acquisition subunit is used to acquire the yaw angle of the current frame and the yaw angle of the previous frame from the odometer, and to acquire the yaw angle of the current frame and the yaw angle of the previous frame from the gyroscope. The first information acquisition subunit is used to determine a first change direction based on the yaw angle of the current frame and the yaw angle of the previous frame from the odometer, and to determine a second change direction based on the yaw angle of the current frame and the yaw angle of the previous frame from the gyroscope.

[0134] In one exemplary embodiment, based on the above embodiments, the information acquisition unit includes: The signal acquisition subunit is used to acquire pulse signals for controlling the walking wheels; wherein the walking wheels include a left wheel and a right wheel; The demand direction determination subunit is used to determine the demand motion direction of the left wheel and the right wheel respectively based on pulse signals; The counter reading subunit is used to obtain the current frame data and the previous frame data of the odometer's counter; The second information acquisition subunit is used to determine the actual movement direction of the left wheel and the actual movement direction of the right wheel based on the current frame data and the previous frame data, respectively.

[0135] In an exemplary embodiment, based on the above embodiments, the fault detection device 800 for the self-moving device further includes: An angle acquisition module is used to acquire the attitude angles of the self-moving device; wherein the attitude angles include at least one of pitch angle and roll angle; The anomaly detection module is specifically used to perform anomaly detection on the odometer of the self-moving device every first preset time interval if the attitude angle is not greater than a preset angle.

[0136] In an exemplary embodiment, based on the above embodiments, the fault detection device 800 of the self-moving device further includes a device control module, which is used to control the self-moving device to stop moving and to perform fault alarm processing. The fault alarm is handled in at least one of the following ways: Fault alarm processing is performed via the lighting and / or voice devices of self-moving equipment; Send fault alarm information to the associated terminal of the self-moving device so that the fault alarm information can be displayed on the associated terminal.

[0137] Based on the same inventive concept, this application provides a self-moving device, which includes a device body, and a walking wheel, an odometer, and a controller disposed on the device body. The controller is used to execute the steps of the fault detection method for the self-moving device described above.

[0138] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing computer program instructions. When the computer program instructions are loaded and executed by a processor, they implement the steps of the fault detection method for the self-moving device described above.

[0139] Based on the same inventive concept, this application provides an electronic device, see [link to relevant documentation]. Figure 9 The diagram shows a schematic of the structure of an electronic device in an embodiment of this application. The electronic device includes one or more memories 904, one or more processors 902, and at least one computer program stored in the memory 904 and executable on the processor 902. When the processor 902 executes the computer program, it implements the steps of the fault detection method for the self-moving device described above.

[0140] The bus architecture (represented by bus 900) includes any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 902 and memory represented by memory 904. Bus 900 can also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 905 provides an interface between bus 900 and receiver 901 and transmitter 903. Receiver 901 and transmitter 903 can be the same element, a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 902 is responsible for managing bus 900 and general processing, while memory 904 can be used to store data used by processor 902 during operation.

[0141] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0142] Based on the same inventive concept, this application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the fault detection method for self-moving devices described above.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0144] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0145] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program instructions, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0146] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A fault detection method for a self-moving device, characterized in that, The self-moving device includes a main body, and wheels and an odometer disposed on the main body. The method includes: During the movement of the self-moving device, the odometer is checked for anomalies every first preset time interval, and the detection result is obtained. Based on the detection results, update the status information of the odometer; If the updated status information meets the preset fault conditions, then the self-moving device's walking wheels are determined to be faulty.

2. The method according to claim 1, characterized in that, The anomaly detection includes the following steps: Acquire at least one of first detection information and second detection information; wherein, the first detection information includes the first change direction of the yaw angle of the odometer and the second change direction of the yaw angle of the gyroscope, and the second detection information includes the required movement direction and the actual movement direction of the walking wheel; If the first direction of change is inconsistent with the second direction of change, or if the required direction of movement is inconsistent with the actual direction of movement, then the detection result of the current anomaly detection is determined to be that the odometer is abnormal.

3. The method according to claim 2, characterized in that, The status information includes cumulative anomaly information, which includes cumulative anomaly time or cumulative number of anomalies; The step of updating the status information of the odometer based on the detection results includes: If the detection result indicates that the odometer is abnormal, then the accumulated abnormality information is added; If the detection result indicates that the odometer is not abnormal, then the accumulated abnormal information is reduced.

4. The method according to claim 3, characterized in that, The addition of the accumulated anomaly information includes: Add the second preset time to the cumulative abnormal time to obtain the updated cumulative abnormal time; The reduction of the accumulated abnormal information includes: The cumulative abnormal time is subtracted from the second preset time to obtain the updated cumulative abnormal time; the minimum value of the cumulative abnormal time is 0ms.

5. The method according to claim 3 or 4, characterized in that, The updated status information satisfies the preset fault conditions, including the updated cumulative abnormal time reaching a third preset time.

6. The method according to claim 2, characterized in that, Obtain the first detection information, including: Obtain the current frame yaw angle and the previous frame yaw angle of the odometer, and obtain the current frame yaw angle and the previous frame yaw angle of the gyroscope. A first direction of change is determined based on the yaw angle of the current frame and the yaw angle of the previous frame of the odometer, and a second direction of change is determined based on the yaw angle of the current frame and the yaw angle of the previous frame of the gyroscope.

7. The method according to claim 2, characterized in that, Obtain the second detection information, including: Acquire pulse signals for controlling the walking wheels; wherein the walking wheels include a left wheel and a right wheel; The desired direction of motion for the left wheel and the desired direction of motion for the right wheel are determined based on the pulse signals, respectively. Obtain the current frame data and the previous frame data of the odometer's counter; The actual movement direction of the left wheel and the actual movement direction of the right wheel are determined based on the current frame data and the previous frame data, respectively.

8. The method according to claim 1, characterized in that, The method further includes: The attitude angles of the self-moving device are obtained; wherein the attitude angles include at least one of pitch angle and roll angle; The step of performing an anomaly detection on the odometer of the self-moving device every first preset time interval includes: If the attitude angle is not greater than a preset angle, then the odometer of the self-moving device is checked for anomalies every first preset time interval.

9. A fault detection device for a self-moving device, characterized in that, The self-moving device includes a main body, and wheels and an odometer disposed on the main body. The device includes: An anomaly detection module is used to perform anomaly detection on the odometer every first preset time interval during the movement of the self-moving device, and obtain the detection result; The information update module is used to update the status information of the odometer based on the detection results; The fault determination module is used to determine that the walking wheel of the self-moving device is faulty if the updated status information meets the preset fault conditions.

10. A self-moving device, characterized in that, The device includes a main body, and a walking wheel, an odometer, and a controller disposed on the main body, the controller being used to perform the steps of the fault detection method for the self-moving device as described in claims 1 to 8.

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