Self-moving apparatus, abnormality determination method, device, storage medium, and computer program product

Abstract

Embodiments of the present disclosure disclose an abnormality determination method. The method comprises:, determining, when a self-moving device is in a moving state, depth information of a target region in a sloped region to be detected for the self-moving device to pass through, relative to the self-moving device at different moments, and, on the basis of the depth information at each moment, determining the position of the target region relative to the self-moving device at each moment; and then, on the basis of the position of the target region at each moment, determining whether an idle or slipping phenomenon has occurred in a driving component of the self-moving device. In this way, the problem in the related technology that it is impossible to detect whether wheels of a stair climbing machine are idle or slipping during a stair climbing process is solved, thereby improving the accuracy of an acquired position of the stair climbing machine. The embodiments of the present disclosure further disclose a self-moving apparatus, a device, a computer-readable storage medium, and a computer program product.

Description

Self-moving devices, anomaly detection methods, equipment, storage media, and computer program products Cross-reference to related applications

[0001] This disclosure is based on and claims priority to Chinese Patent Application No. 202510304261.0, filed on March 14, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to anomaly determination technology in the field of intelligent control, and more particularly to a self-moving device, anomaly determination method, equipment, storage medium, and computer program product. Background Technology

[0003] With the development of technology, stair climbers, as a new type of stair climbing tool, have been increasingly widely used in daily life. Currently, the movement status of stair climbers is usually monitored by estimating the distance traveled by the wheels of the stair climber using a wheel-type odometer. However, the wheels of the stair climber are prone to slipping or spinning during the stair climbing process. This results in a large discrepancy between the position estimated by the wheel-type odometer and the actual position of the stair climber. In other words, to obtain the accurate position of the stair climber, it is necessary to monitor its movement status in real time to determine whether it is slipping or spinning. Summary of the Invention

[0004] This disclosure provides a self-moving device, the device comprising:

[0005] Organism;

[0006] A drive component, connected to the machine body, to drive the machine body to move;

[0007] A processor, internally located on the machine body, wherein the processor:

[0008] When the mobile device is in motion, the depth information of the target area in the sloped detection area to be traversed by the mobile device is determined relative to the mobile device at different times.

[0009] Based on multiple depth information parameters, the target region is determined relative to the self-moving device at different times; and

[0010] The abnormal condition of the drive component is determined based on the relationship between the multiple target locations.

[0011] In the above scheme, the device further includes a first imaging component, which is connected to the processor, wherein,

[0012] The first imaging component acquires a first image of the region to be detected relative to the self-moving device at a first moment, and acquires a second image of the region to be detected relative to the self-moving device at a second moment; and

[0013] The processor further determines, based on the first image to be processed and the second image to be processed acquired by the first imaging component, the first depth information of the target region relative to the self-moving device at the first time and the second depth information relative to the self-moving device at the second time.

[0014] In the above scheme, the device further includes a second imaging component, which is connected to the processor, wherein...

[0015] The second imaging component acquires a first image of the region to be detected relative to the self-moving device at a first moment, and acquires a second image of the region to be detected relative to the self-moving device at a second moment; and

[0016] The processor further determines, based on the first image to be processed and the second image to be processed acquired by the second imaging component, first point cloud data of the target region relative to the self-moving device at the first time and second point cloud data relative to the self-moving device at the second time.

[0017] The depth information includes the first point cloud data and the second point cloud data.

[0018] In the above scheme, the processor also:

[0019] Point cloud data of multiple points to be processed in the first image to be processed and the second image to be processed are determined respectively; and

[0020] The first point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the first image to be processed; and

[0021] The second point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the second image to be processed.

[0022] The target area includes the target steps.

[0023] In the above scheme, the processor also:

[0024] Based on the first depth information, determine the first coordinate of the reference edge of the target step on the target coordinate axis; and

[0025] Based on the second depth information, determine the second coordinate of the reference edge on the target coordinate axis.

[0026] The plurality of depth information includes the first depth information and the second depth information, and

[0027] The target location includes the first coordinates and the second coordinates.

[0028] In the above scheme, the device further includes a sensor connected to the processor, wherein,

[0029] The sensor is able to determine the current motion state of the self-moving device;

[0030] The processor can determine abnormal conditions of the drive component based on the current motion state determined by the sensor and the relationship between the first coordinate and the second coordinate.

[0031] In the above scheme, the processor also:

[0032] When the current motion state is a stationary state and the first coordinate is equal to the second coordinate, it is determined that the driving component is not malfunctioning; and

[0033] When the current motion state is the stationary state and the first coordinate is not equal to the second coordinate, it is determined that the driving component is in a slipping state.

[0034] In the above scheme, when the current motion state is a moving state, the processor determines the abnormal situation of the driving component based on the moving direction of the self-moving device and the relationship between the first coordinate and the second coordinate.

[0035] In the above scheme, the processor also:

[0036] When the first coordinate is equal to the second coordinate, it is determined that the driving component is in an idling state;

[0037] When the direction of movement is the first direction and the first coordinate is greater than the second coordinate, it is determined that the driving component is not malfunctioning; and

[0038] When the direction of movement is the first direction and the first coordinate is less than the second coordinate, the driving component is determined to be in the slipping state.

[0039] In the above scheme, the processor also:

[0040] When the direction of movement is the second direction and the first coordinate is greater than the second coordinate, the difference between the first coordinate and the second coordinate is determined;

[0041] When the difference is less than or equal to the target threshold, it is determined that the driving component is not abnormal; and

[0042] When the difference is greater than the target threshold, the driving component is determined to be in the slipping state.

[0043] This disclosure provides a method for determining anomalies, the method comprising:

[0044] When the mobile device is in motion, the depth information of the target area in the sloped detection area to be traversed by the mobile device is determined relative to the mobile device at different times.

[0045] The target location of the target region relative to the self-moving device is determined based on multiple depth information.

[0046] The abnormal condition of the driving component of the self-moving device is determined based on the relationship between the multiple target locations.

[0047] In the above scheme, determining the depth information of the target area relative to the self-moving device at different times within the sloped detection area to be traversed by the self-moving device includes:

[0048] At a first moment, a first image to be processed of the region to be detected relative to the self-moving device is acquired by the first imaging component;

[0049] At the second moment, a second image to be processed of the region to be detected relative to the self-moving device is acquired by the first imaging component;

[0050] Based on the first image to be processed and the second image to be processed, the first depth information of the target region relative to the self-moving device at the first time and the second depth information relative to the self-moving device at the second time are determined.

[0051] In the above scheme, determining the depth information of the target area relative to the self-moving device at different times within the sloped detection area to be traversed by the self-moving device includes:

[0052] At the first moment, the second imaging component acquires a first image of the region to be detected relative to the self-moving device.

[0053] At the second moment, a second image to be processed of the region to be detected relative to the self-moving device is acquired by the second imaging component;

[0054] Based on the first image to be processed and the second image to be processed, the first point cloud data of the target region relative to the self-mobile device at the first time and the second point cloud data relative to the self-mobile device at the second time are determined.

[0055] The depth information includes the first point cloud data and the second point cloud data.

[0056] In the above scheme, determining the first point cloud data of the target region relative to the self-mobile device at the first time and the second point cloud data relative to the self-mobile device at the second time, based on the first image to be processed and the second image to be processed, includes:

[0057] The point cloud data of multiple points to be processed in the first image to be processed and the second image to be processed are determined respectively;

[0058] The first point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the first image to be processed.

[0059] The second point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the second image to be processed.

[0060] The target area includes the target steps.

[0061] In the above scheme, determining the target position of the target region relative to the self-moving device at different times based on multiple depth information includes:

[0062] Based on the first depth information, determine the first coordinate of the reference edge of the target step on the target coordinate axis;

[0063] Based on the second depth information, determine the second coordinate of the reference edge on the target coordinate axis.

[0064] The depth information includes the first depth information and the second depth information, and the target location includes the first coordinates and the second coordinates.

[0065] In the above scheme, determining the abnormal condition of the driving component of the self-moving device based on the relationship between multiple target locations includes:

[0066] Determine the current motion state of the self-moving device;

[0067] Based on the current motion state and the relationship between the first coordinate and the second coordinate, the abnormal condition of the driving component is determined.

[0068] In the above scheme, determining the abnormal condition of the driving component based on the current motion state and the relationship between the first coordinate and the second coordinate includes:

[0069] When the current motion state is a stationary state and the first coordinate is equal to the second coordinate, it is determined that the driving component is not abnormal;

[0070] When the current motion state is the stationary state and the first coordinate is not equal to the second coordinate, it is determined that the driving component is in a slipping state;

[0071] When the current motion state is a moving state, the abnormal situation of the driving component is determined based on the moving direction of the self-moving device and the relationship between the first coordinate and the second coordinate.

[0072] In the above scheme, based on the movement direction of the self-moving device and the relationship between the first coordinate and the second coordinate, the abnormal situation of the driving component is determined, including:

[0073] When the first coordinate is equal to the second coordinate, it is determined that the driving component is in an idling state;

[0074] When the direction of movement is the first direction and the first coordinate is greater than the second coordinate, it is determined that the driving component is not abnormal;

[0075] When the direction of movement is the first direction and the first coordinate is less than the second coordinate, the driving component is determined to be in the slipping state.

[0076] The method in the above scheme further includes:

[0077] When the direction of movement is the second direction and the first coordinate is greater than the second coordinate, the difference between the first coordinate and the second coordinate is determined;

[0078] When the difference is less than or equal to the target threshold, it is determined that the driving component is not abnormal; and

[0079] When the difference is greater than the target threshold, the driving component is determined to be in the slipping state.

[0080] This disclosure provides a self-moving device, the device including: a processor, a memory, and a communication bus;

[0081] The communication bus enables communication between the processor and the memory;

[0082] The processor executes the exception determination program in the memory to implement the steps of the above-described exception determination method.

[0083] This disclosure provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps of the anomaly determination method described above.

[0084] This disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the above-described exception determination method. Attached Figure Description

[0085] Figure 1 is a flowchart illustrating an anomaly determination method provided in an embodiment of this disclosure;

[0086] Figure 2 is a flowchart illustrating another anomaly determination method provided in an embodiment of this disclosure;

[0087] Figure 3 is a schematic diagram of the region to be detected in an anomaly determination method provided in an embodiment of this disclosure;

[0088] Figure 4 is a schematic diagram of a target step in an anomaly determination method provided in an embodiment of this disclosure;

[0089] Figure 5 is a schematic diagram of the coordinates of the reference edge in an anomaly determination method provided in an embodiment of this disclosure;

[0090] Figure 6 is a schematic diagram of the structure of a self-moving device provided in an embodiment of this disclosure;

[0091] Figure 7 is a schematic diagram of the structure of a self-moving device provided in an embodiment of this disclosure;

[0092] Figure 8 is a schematic diagram of a computer program product provided in an embodiment of this disclosure. Detailed Implementation

[0093] The technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0094] It should be understood that the specific embodiments described herein are merely illustrative of this disclosure and are not intended to limit this disclosure.

[0095] Stair climbers, as a new type of stair climbing tool, are increasingly widely used in daily life. Currently, the movement status of stair climbers is usually monitored by estimating the distance traveled by the wheels using a wheel-type odometer. However, the wheels of stair climbers are prone to slipping or spinning during the stair climbing process. This results in a large discrepancy between the position estimated by the wheel-type odometer and the actual position of the stair climber. In other words, to obtain the accurate position of the stair climber, it is necessary to monitor its movement status in real time to determine whether it is slipping or spinning.

[0096] The self-moving device, anomaly determination method, apparatus, storage medium, and computer program product provided in this disclosure can determine the depth information of a target area in a sloped detection area to be traversed by the self-moving device at different times when the self-moving device is in motion. Based on multiple depth information, the target position of the target area relative to the self-moving device at different times can be determined. Furthermore, based on the relationship between multiple target positions, the abnormality of the driving component of the self-moving device can be determined. In this way, the target position of the target area in a sloped detection area to be traversed by the self-moving device at different times can be determined, and the abnormality of the driving component of the self-moving device is determined based on the relationship between the target positions at different times. This solves the problem in related technologies that it is impossible to detect whether the wheels of the stair climber are slipping or spinning, thereby improving the accuracy of determining the position of the stair climber.

[0097] This disclosure provides an anomaly determination method, which can be applied to self-moving devices. Referring to FIG1, the method includes the following steps:

[0098] Step 101: When the self-moving device is in motion, determine the depth information of the target area relative to the self-moving device at different times in the sloped detection area to be traversed by the self-moving device.

[0099] In this embodiment of the disclosure, the self-moving device can refer to a stair-climbing machine, a vehicle, or a stair-climbing robot; the area to be detected can refer to a sloping area that the self-moving device is about to pass through; it should be noted that the area to be detected can be a smooth slope that the self-moving device is about to pass through, or it can be a staircase consisting of multiple steps that the self-moving device is about to pass through; correspondingly, if the area to be detected is a smooth slope, the target area can be any area of ​​the smooth slope, or it can be a certain landmark in the smooth slope; if the area to be detected is a staircase, the target area can be a certain step in the staircase.

[0100] It should be noted that the detection area that the mobile device is about to pass through can be different at different times.

[0101] In this embodiment of the disclosure, while the mobile device is in motion, images of the area to be detected at different times can be acquired by the imaging component on the mobile device. Then, the depth information of the target area within the area to be detected at different times can be determined based on multiple images. The depth information is relative to the mobile device; that is, the depth information is the relative distance between the target area and the imaging component of the mobile device.

[0102] Step 102: Determine the target position of the target area relative to the self-moving device at different times based on multiple depth information.

[0103] In this embodiment of the disclosure, the target position of the target area in the coordinate system can be determined based on the depth information of the target area at different times. Here, the target position is the relative position with respect to the self-moving device.

[0104] Specifically, if the target area is a region or landmark on a smooth slope, the distance between that region or landmark and the imaging component of the self-moving device can be determined at different times. Then, the position of the region or landmark in the coordinate system is determined based on these multiple distances. Similarly, if the target area is a step on a staircase, the distance between the step and the imaging component can be determined at different times. Then, the position of the step in the coordinate system is determined based on these multiple distances. It should be noted that the distances between the region, landmark, or step and the imaging component are all relative to the self-moving device.

[0105] Step 103: Determine the abnormal conditions of the driving components of the self-moving device based on the relationship between multiple target locations.

[0106] In this embodiment of the disclosure, if the self-moving device is a stair climber or a stair climbing robot, the driving component may refer to the wheels of the stair climber or the stair climbing robot; if the self-moving device is a vehicle, the driving component may refer to the tires of the vehicle. Specifically, the motion state of the self-moving device at the current moment can be determined, and then, based on the current motion state and the relationship between multiple target positions, the abnormal conditions of the driving component of the self-moving device can be determined.

[0107] It should be noted that the motion state of the self-moving device at the current moment includes a stationary state and a moving state; abnormal conditions of the driving component include whether there is an abnormality in the driving component. If there is an abnormality, the abnormal condition may include an idling state or a slipping state.

[0108] The anomaly determination method provided by the embodiments of this disclosure can determine the target position of the target area in the sloped detection area to be traversed by the self-moving device at different times, and determine whether the driving component of the self-moving device has slipped or idled abnormally based on the relationship between the target positions at different times. This solves the problem in the related technology that it is impossible to detect whether the wheels of the stair climber are slipping or idling, thereby improving the accuracy of determining the position of the stair climber.

[0109] Based on the foregoing embodiments, embodiments of this disclosure provide an anomaly determination method, which can be applied to self-moving devices. Referring to FIG2, the method may include the following steps:

[0110] Step 201: When the self-moving device is in motion, the self-moving device determines the depth information of the target area in the sloped detection area to be traversed by the self-moving device at different times relative to the self-moving device.

[0111] In the embodiments of this disclosure, the detection area to be traversed by the self-moving device may be the same or different at different times.

[0112] In this embodiment of the disclosure, the step 201, "the self-moving device determines the depth information of the target area in the sloped detection area to be traversed by the self-moving device at different times relative to the self-moving device", can be implemented through steps 201a-201c or steps 201d-201f.

[0113] Step 201a: At the first moment, the self-moving device acquires a first image of the area to be detected relative to the self-moving device through the first imaging component.

[0114] In this embodiment of the disclosure, the first imaging component may refer to a visible light imaging sensor. In one possible implementation, the visible light imaging sensor may refer to a red-green-blue camera (hereinafter referred to as: RGB camera), and the area to be detected may refer to the area captured by the RGB camera that the mobile device is about to pass through (to pass through). Specifically, the image corresponding to the area to be detected captured by the RGB camera at a first moment (i.e., the first image to be processed) can be acquired.

[0115] Step 201b: At the second moment, the self-moving device acquires a second image of the area to be detected relative to the self-moving device through the first imaging component.

[0116] In this embodiment of the disclosure, the second time point can be a time point after the first time point. Specifically, the image corresponding to the area to be detected, captured by the RGB camera at the second time point, can be obtained, i.e., the second image to be processed.

[0117] In one feasible approach, if the area to be detected is a staircase, images of the staircase captured by an RGB camera at the first and second time points can be obtained respectively.

[0118] Step 201c: The self-moving device determines the first depth information of the target region relative to the self-moving device at a first time and the second depth information relative to the self-moving device at a second time, based on the first image to be processed and the second image to be processed.

[0119] The depth information includes first depth information and second depth information.

[0120] In this embodiment of the disclosure, after acquiring the first image to be processed and the second image to be processed, they can be processed by a target image recognition algorithm or a rendering algorithm to obtain a first distance (i.e., first depth information) between the target region and the RGB camera at a first time, and a second distance (i.e., second depth information) between the target region and the RGB camera at a second time. Both the first depth information and the second depth information are relative to the self-moving device.

[0121] It should be noted that the target image recognition algorithm can refer to an open-source computer vision library (OpenCV), and the rendering algorithm can refer to a deferred rendering algorithm.

[0122] In one feasible approach, if the area to be detected is a staircase, then the target area can refer to the steps in the staircase. In this case, the first depth information of the steps at the first moment can be determined based on the staircase image at the first moment, and the second depth information of the steps at the second moment can be determined based on the staircase image at the second moment.

[0123] In other embodiments of this disclosure, multiple images to be processed corresponding to the region to be detected at multiple times such as the third time and the fourth time can be acquired, and multiple depth information of the target region relative to the self-moving device at multiple times can be determined based on each image to be processed, instead of acquiring only two images to be processed and then determining only two depth information.

[0124] Step 201d: The self-moving device acquires a first image of the area to be detected relative to the self-moving device through the second imaging component at the first moment.

[0125] In this embodiment of the disclosure, the second imaging component may refer to a time-of-flight (ToF) sensor (hereinafter referred to as: ToF sensor). In one possible implementation, the ToF sensor may refer to a depth imaging camera (e.g.: ToF camera).

[0126] In this embodiment of the disclosure, if the area to be detected is a staircase, an image corresponding to the staircase (i.e., the first image to be processed) can be captured by a ToF camera at the first moment. It should be noted that, as shown in Figure 3, a light pulse can be emitted from the ToF camera in the mobile device to the staircase (i.e., the area to be detected) that the mobile device will pass through, so as to capture the staircase and obtain the first image to be processed corresponding to the area to be detected.

[0127] Step 201e: At the second moment, the self-moving device acquires a second image of the area to be detected relative to the self-moving device through the second imaging component.

[0128] In this embodiment of the disclosure, if the area to be detected is a staircase, an image corresponding to the staircase (i.e., the second image to be processed) can be captured by a ToF camera at a second time.

[0129] In this embodiment of the disclosure, by using a ToF sensor to acquire the first and second images of the area to be detected at the first and second times, a clearer image of the area to be detected with a slope can be directly obtained, instead of only being able to obtain an image of the area to be detected without a slope as in related technologies. Furthermore, using a ToF sensor can also directly obtain the depth information of the corresponding pixels of all objects in the area to be detected in the image to be processed, which significantly improves the efficiency of determining the position of the stair climber.

[0130] Step 201f: The self-moving device determines the first point cloud data of the target region relative to the self-moving device at a first time and the second point cloud data relative to the self-moving device at a second time, based on the first image to be processed and the second image to be processed.

[0131] The depth information includes first point cloud data and second point cloud data.

[0132] In this embodiment of the disclosure, after acquiring the first image to be processed corresponding to the region to be detected at a first time and the second image to be processed corresponding to the region at a second time, point cloud data of multiple points to be processed in the first and second images to be processed can be determined respectively. Then, based on the point cloud data of each point, the first point cloud data of the target region at the first time and the second point cloud data at the second time are determined. It should be noted that one image to be processed corresponds to one point cloud data.

[0133] In this embodiment of the disclosure, step 201f can be implemented by steps 201f1 to 201f3.

[0134] Step 201f1: The self-moving device determines the point cloud data of multiple points to be processed in the first image to be processed and the second image to be processed, respectively.

[0135] The point to be processed can refer to a pixel in the image to be processed; the point cloud data of the point to be processed can include the three-dimensional coordinate information of each point to be processed (i.e., pixel).

[0136] In this embodiment of the disclosure, the point cloud data can be determined by measuring the time required for light to travel from emission to the area to be detected and return using a ToF sensor, and based on the measured time. It should be noted that the point cloud data of the points to be processed is directly carried within the image to be processed; that is, the first and second images to be processed can be directly parsed to obtain the point cloud data of the points to be processed in the first and second images to be detected, respectively.

[0137] Step 201f2: The self-moving device determines the first point cloud data of the target step based on the point cloud data of multiple points to be processed in the first image to be processed.

[0138] In this embodiment of the disclosure, if the area to be detected is a staircase, the target area can refer to the target step. Then, for the first image to be processed, the point cloud data of the first plane of the target step in Figure 4 and the point cloud data of the second plane of the target step can be determined based on the point cloud data of each pixel. Then, the point cloud data of the first plane and the point cloud data of the second plane can be processed to obtain the first vector and the second vector. Then, the first vector, the second vector and the point cloud data of multiple pixels can be processed to obtain the first point cloud data of the target step at the first moment.

[0139] Step 201f3: The self-moving device determines the second point cloud data of the target step based on the point cloud data of multiple points to be processed in the second image to be processed.

[0140] The target area includes the target steps. It should be noted that there can be multiple target steps.

[0141] In this embodiment of the disclosure, for the second image to be processed, according to step 201f2, the point cloud data of the first plane and the second plane of the target step can also be processed to obtain the third vector and the fourth vector. Then, the third vector, the fourth vector and the point cloud data of multiple pixels are processed to obtain the second point cloud data of the target step at the second time.

[0142] It should be noted that, as shown in Figure 4, there can be two target steps in the area to be detected, namely the first target step and the second target step; for the first target step, the first plane can refer to the tread of the target step, and the second plane can refer to the riser of the target step.

[0143] Step 202: The self-moving device determines the first coordinate of the reference edge of the target step on the target coordinate axis based on the first depth information.

[0144] In this embodiment of the disclosure, if there are multiple target steps, then as shown in FIG4, there are also multiple reference edges of the target steps.

[0145] In this embodiment of the disclosure, the first depth information may refer to the point cloud data of the target step at a first moment; the first coordinate may refer to the position of the reference edge of the target step at the first moment. Specifically, the point cloud data of the target step can be processed to obtain the position of the reference edge corresponding to the target step, and then the first coordinate of the reference edge on the target coordinate axis can be determined based on the determined position of the reference edge.

[0146] It should be noted that the target coordinate axis can refer to the coordinate axis in the target coordinate system, which can be a two-dimensional coordinate system. In one possible implementation, the two-dimensional coordinate system can refer to a Cartesian coordinate system. In this case, as shown in Figure 5, the target coordinate axis can refer to the Y-axis in the Cartesian coordinate system, and the first coordinate can refer to the coordinate (i.e., the ordinate) of the reference edge of the target step on the Y-axis in the Cartesian coordinate system.

[0147] Step 203: The self-moving device determines the second coordinate of the reference edge on the target coordinate axis based on the second depth information.

[0148] The depth information includes first depth information and second depth information, and the target location includes first coordinates and second coordinates.

[0149] In this embodiment of the disclosure, the second depth information may refer to the point cloud data of the target step at a second time; the second coordinate may refer to the position of the reference edge of the target step at the second time. Specifically, the point cloud data of the target step at the second time can be processed to obtain the position of the reference edge corresponding to the target step. Then, the coordinate (vertical coordinate) of the reference edge on the target coordinate axis (Y-axis), i.e., the second coordinate, can be determined based on the determined position of the reference edge.

[0150] It should be noted that the second coordinate can refer to the ordinate of the reference side of the target step in the Cartesian coordinate system at the second moment.

[0151] Step 204: The self-moving device determines its current motion state.

[0152] In this embodiment of the disclosure, the current motion state can include a stationary state and a moving state. Specifically, the state of the self-moving device, whether it is currently stationary or moving, can be determined by sensors installed on the self-moving device.

[0153] Step 205: The self-moving device determines the abnormal situation of the driving component based on the current motion state and the relationship between the first coordinate and the second coordinate.

[0154] In this embodiment of the disclosure, if it is determined that the current motion state of the self-moving device is stationary, the abnormal condition of the driving component can be determined based on the relationship between the first coordinate and the second coordinate; if it is determined that the current motion state of the self-moving device is moving, the moving direction of the self-moving device can be determined, and the abnormal condition of the driving component can be determined based on the moving direction and the relationship between the first coordinate and the second coordinate.

[0155] In this embodiment of the disclosure, step 205 can be implemented by steps 205a-205b or step 205c.

[0156] Step 205a: When the current motion state is stationary and the first coordinate is equal to the second coordinate, the self-moving device determines that there is no abnormality in the driving component.

[0157] In this embodiment of the disclosure, if it is determined that the current motion state of the self-moving device is stationary and the first coordinate is equal to the second coordinate, it indicates that the position of the reference edge of the target step is the same at different times. That is to say, the position of the self-moving device does not change between the first time and the second time, that is, the self-moving device is stationary during the time from the first time to the second time. At this time, it can be determined that there is no abnormality in the driving component of the self-moving device.

[0158] In one feasible approach, if the self-moving device is a stair climber or a stair climber robot, then if the stair climber or stair climber robot is stationary at the current moment and the first coordinate equals the second coordinate, it can be determined that the wheels of the stair climber or stair climber robot are not abnormal.

[0159] Step 205b: When the current motion state is stationary and the first coordinate is not equal to the second coordinate, the self-moving device determines that the driving component is in a slipping state.

[0160] In this embodiment of the disclosure, if it is determined that the current motion state of the self-moving device is stationary, but the first coordinate is not equal to the second coordinate, it indicates that the position of the reference edge of the target step is different at different times. That is to say, the position of the self-moving device changes between the first time and the second time. In other words, the self-moving device is not stationary during the time from the first time to the second time. At this time, it can be determined that there is an abnormality in the driving component of the self-moving device and that it is in a slipping state.

[0161] Step 205c: When the current motion state is a moving state, the self-moving device determines the abnormal situation of the driving component based on the moving direction of the self-moving device and the relationship between the first coordinate and the second coordinate.

[0162] In this embodiment of the disclosure, if it is determined that the current motion state of the self-moving device is a moving state, the moving direction of the self-moving device can be determined, that is, whether the self-moving device is in an uphill state or a downhill state. Then, based on the moving direction of the self-moving device and the relationship between the first coordinate and the second coordinate, it is determined whether there is an abnormality in the driving component.

[0163] In this embodiment of the disclosure, the step 205c, "the self-moving device determines the abnormal condition of the driving component based on the moving direction of the self-moving device and the relationship between the first coordinate and the second coordinate", can be implemented through steps 205c1, 205c2 to 205c3, or 205c4 to 205c6.

[0164] Step 205c1: When the first coordinate is equal to the second coordinate, the self-moving device determines that the driving component is in an idling state.

[0165] In this embodiment of the disclosure, if it is determined that the current motion state of the self-moving device is a moving state, then regardless of whether the moving direction of the self-moving device is uphill or downhill, if the first coordinate is equal to the second coordinate, it means that the position of the reference edge of the target step at the first moment is the same as the position at the second moment. That is to say, during the time between the first moment and the second moment, the position of the self-moving device has not changed. At this time, it can be determined that there is an abnormality in the driving component of the self-moving device, and it is in an idling state.

[0166] It should be noted that if there are multiple target steps, then as long as the first coordinate and the second coordinate of the reference edge of a certain target step are different, it can be determined that the driving component of the self-moving component is in an idle state.

[0167] Step 205c2: When the movement direction is the first direction and the first coordinate is greater than the second coordinate, the self-moving device determines that there is no abnormality in the driving component.

[0168] In this embodiment of the disclosure, the first direction may refer to the uphill direction. Specifically, if it is determined that the moving direction of the self-moving device is uphill, and the first coordinate is greater than the second coordinate, it indicates that the first distance between the reference edge of the target step and the self-moving device at the first moment is greater than the second distance between the reference edge and the self-moving device at the second moment. In other words, the reference edge of the target step is closer to the self-moving device at the second moment. At this time, it can be determined that the driving component of the self-moving device is also in an uphill state, that is, there is no abnormality in the driving component.

[0169] Step 205c3: When the moving direction is the first direction and the first coordinate is less than the second coordinate, the self-moving device determines that the driving component is in a slipping state.

[0170] In this embodiment of the disclosure, as shown in FIG5, if it is determined that the moving direction of the self-moving device is uphill and the first coordinate is less than the second coordinate, it indicates that the first distance between the reference edge of the target step and the self-moving device is less than the second distance between the reference edge and the self-moving device. That is to say, the reference edge is closer to the self-moving device at the first moment and farther away from the self-moving device at the second moment. That is, although the moving direction of the self-moving device is uphill, in fact, the self-moving device is moving downhill. At this time, it can be determined that there is an abnormality in the driving component and that it is in a slipping state.

[0171] It should be noted that if there are multiple target steps, then as long as the first coordinate of the reference edge of a certain target step is less than the second coordinate, it can be determined that the driving component of the self-moving component is in a slipping state.

[0172] In this embodiment of the disclosure, the relationship between the coordinates of the target step at different times is used to determine whether the drive component is abnormal. This not only solves the problem in the related technology that it is impossible to detect whether the stair climber is spinning or slipping during the stair climbing process, but it is also simple and convenient, without the need for a lot of calculations. This not only saves time, but also improves the efficiency of determining whether the drive component is abnormal.

[0173] Step 205c4: When the movement direction is the second direction and the first coordinate is greater than the second coordinate, the self-moving device determines the difference between the first coordinate and the second coordinate.

[0174] In this embodiment of the disclosure, the second direction may refer to the downhill direction. Specifically, if the movement direction is determined to be downhill and the first coordinate is greater than the second coordinate, it indicates that the first distance between the reference edge of the target step and the self-moving device at the first moment is greater than the second distance between the reference edge and the self-moving device at the second moment. That is to say, at this time, the self-moving device is moving downhill in the downhill direction. However, since the self-moving device also moves downhill in the downhill direction when the driving component slips, it is necessary to further determine the difference between the first coordinate and the second coordinate, and then determine whether there is an abnormality in the driving component of the self-moving device based on the difference.

[0175] Step 205c5: When the difference is less than or equal to the target threshold, the self-moving device determines that there is no abnormality in the driving component.

[0176] In this embodiment of the disclosure, if the difference between the first coordinate and the second coordinate is less than or equal to the target threshold, it indicates that the running distance of the self-moving device is within the preset range during the time period between the first time and the second time. In other words, the movement state of the self-moving device is normal during this period, that is, it is determined that there is no abnormality in the driving component of the self-moving device.

[0177] It should be noted that the target threshold can be determined based on historical data and actual needs.

[0178] Step 205c6: When the difference is greater than the target threshold, the self-moving device determines that the driving component is in a slipping state.

[0179] In this embodiment of the disclosure, if the difference between the first coordinate and the second coordinate is greater than the target threshold, it indicates that the running distance of the self-moving device has exceeded the preset range during the time between the first time and the second time. In other words, the running speed of the self-moving device has exceeded the normal value during this time. That is, the motion state of the self-moving device is abnormal during this time. At this time, it can be determined that the driving component of the self-moving device is in a slipping state.

[0180] In this embodiment of the disclosure, by comparing the difference in coordinates with a target threshold, the state of the drive component in the downhill state can be determined more accurately, thereby significantly improving the accuracy of determining the abnormal state of the drive component.

[0181] The anomaly determination method provided by the embodiments of this disclosure can determine the target position of the target area in the sloped detection area to be traversed by the self-moving device at different times, and determine whether the driving component of the self-moving device has slipped or idled abnormally based on the relationship between the target positions at different times. This solves the problem in the related technology that it is impossible to detect whether the wheels of the stair climber are slipping or idling, thereby improving the accuracy of determining the position of the stair climber.

[0182] Based on the foregoing embodiments, this disclosure provides a self-moving device that can be applied to the anomaly determination method provided in the embodiments corresponding to FIG1 and 2. Referring to FIG6, the self-moving device 3 may include: a body 31 (not shown in FIG6), a driving component 32, and a processor 33, wherein:

[0183] The drive component 32 is connected to the body 31 to drive the body 31 to move;

[0184] Processor 33, built into the chassis 31, processor 33:

[0185] When the mobile device is in motion, determine the depth information of the target area relative to the mobile device at different times within the sloped detection area to be traversed by the mobile device.

[0186] Determine the target region's position relative to the mobile device at different times based on multiple depth information; and

[0187] The abnormal conditions of the drive component 32 are determined based on the relationship between multiple target locations.

[0188] In other embodiments of this disclosure, referring to FIG6, the device further includes: a first imaging component 34, the first imaging component 34 being connected to the processor 33, wherein,

[0189] The first imaging component 34 acquires a first image of the region to be detected relative to the mobile device at a first moment, and acquires a second image of the region to be detected relative to the mobile device at a second moment; and

[0190] The processor 33 further determines, based on the first image to be processed and the second image to be processed acquired by the first imaging component, the first depth information of the target region relative to the self-moving device at a first time and the second depth information relative to the self-moving device at a second time.

[0191] In other embodiments of this disclosure, referring to FIG6, the device further includes: a second imaging component 35, the second imaging component 35 being connected to the processor 33, wherein,

[0192] The second imaging component 35 acquires a first image of the region to be detected relative to the mobile device at a first moment; and acquires a second image of the region to be detected relative to the mobile device at a second moment; and

[0193] The processor 33 further determines, based on the first and second images to be processed acquired by the second imaging component, the first point cloud data of the target region relative to the self-moving device at a first time and the second point cloud data relative to the self-moving device at a second time.

[0194] The depth information includes first point cloud data and second point cloud data.

[0195] In other embodiments of this disclosure, processor 33 further includes:

[0196] Point cloud data of multiple points to be processed in the first and second images to be processed, respectively; and

[0197] The first point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the first image to be processed; and

[0198] The second point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the second image to be processed.

[0199] The target area includes the target steps.

[0200] In other embodiments of this disclosure, processor 33 further includes:

[0201] Based on the first depth information, determine the first coordinate of the reference edge of the target step on the target coordinate axis; and

[0202] The second coordinate of the reference edge on the target coordinate axis is determined based on the second depth information.

[0203] This includes multiple depth information components, such as first depth information, second depth information, and...

[0204] The target location includes the first coordinate and the second coordinate.

[0205] In other embodiments of this disclosure, referring to FIG6, the device further includes a sensor 36, wherein the sensor 36 is connected to the processor 33.

[0206] Sensor 36 is able to determine the current motion state of the self-moving device;

[0207] The processor 33 is able to determine abnormal conditions of the drive component based on the current motion state determined by the sensor 36 and the relationship between the first coordinate and the second coordinate.

[0208] In other embodiments of this disclosure, processor 33 further includes:

[0209] When the current motion state is stationary and the first coordinate equals the second coordinate, it is determined that there is no abnormality in the driving component; and

[0210] When the current motion state is stationary and the first coordinate is not equal to the second coordinate, it is determined that the driving component is in a slipping state.

[0211] In other embodiments of this disclosure, the processor 33 also determines abnormal conditions of the driving component based on the moving direction of the self-moving device and the relationship between the first coordinate and the second coordinate when the current motion state is a moving state.

[0212] In other embodiments of this disclosure, processor 33 further includes:

[0213] When the first coordinate equals the second coordinate, it is determined that the drive component is in an idling state;

[0214] When the direction of movement is the first direction and the first coordinate is greater than the second coordinate, it is determined that there is no abnormality in the driving component; and

[0215] When the direction of movement is the first direction and the first coordinate is less than the second coordinate, it is determined that the driving component is in a slipping state.

[0216] In other embodiments of this disclosure, processor 33 further includes:

[0217] When the direction of movement is the second direction and the first coordinate is greater than the second coordinate, determine the difference between the first coordinate and the second coordinate;

[0218] When the difference is less than or equal to the target threshold, it is determined that there is no abnormality in the drive component; and

[0219] When the difference is greater than the target threshold, the drive component is determined to be in a slipping state.

[0220] It should be noted that the specific implementation process of the steps performed by each device in the embodiments of this disclosure can be referred to the implementation process of the anomaly determination method provided in the above embodiments, and will not be repeated here.

[0221] The self-moving device provided in the embodiments of this disclosure can determine the target position of the target area in the sloped detection area to be traversed by the self-moving device at different times, and determine whether the driving component of the self-moving device has slipped or idled abnormally based on the relationship between the target positions at different times. This solves the problem in the related art that it is impossible to detect whether the wheels of the stair climber are slipping or idling, thereby improving the accuracy of determining the position of the stair climber.

[0222] Based on the foregoing embodiments, the present disclosure provides a self-moving device that can be applied to the anomaly determination method provided in the embodiments corresponding to FIG1 and 2. Referring to FIG7, the self-moving device 4 may include: a processor 41 (the processor 41 may be the same as or different from the processor 33 in the above embodiments), a memory 42, and a communication bus 43, wherein:

[0223] Communication bus 43 enables communication between processor 41 and memory 42;

[0224] Processor 41 executes the exception determination program in memory 42 to perform the following steps:

[0225] When the mobile device is in motion, determine the depth information of the target area relative to the mobile device at different times within the sloped detection area to be traversed by the mobile device.

[0226] Determine the target location of the target area relative to the self-moving device at different times based on multiple depth information;

[0227] Anomalies in the driving components of a self-moving device are determined based on the relationships between multiple target locations.

[0228] In other embodiments of this disclosure, the processor 41 executes the anomaly determination program in the memory 42 to determine the depth information of the target area in the sloped detection area to be traversed by the self-moving device at different times relative to the self-moving device, in order to perform the following steps:

[0229] At the first moment, the first image to be processed of the area to be detected relative to the self-moving device is acquired by the first imaging component;

[0230] At the second moment, a second image of the region to be detected relative to the self-moving device is acquired by the first imaging component;

[0231] Based on the first image to be processed and the second image to be processed, the first depth information of the target region relative to the self-moving device at the first time and the second depth information relative to the self-moving device at the second time are determined.

[0232] In other embodiments of this disclosure, the processor 41 executes the anomaly determination program in the memory 42 to determine the depth information of the target area in the sloped detection area to be traversed by the self-moving device at different times relative to the self-moving device, in order to perform the following steps:

[0233] At the first moment, the second imaging component acquires a first image of the area to be detected relative to the self-moving device;

[0234] At the second moment, a second image of the region to be detected relative to the self-moving device is acquired by the second imaging component;

[0235] Based on the first image to be processed and the second image to be processed, the target region is determined using first point cloud data relative to the mobile device at a first time and second point cloud data relative to the mobile device at a second time.

[0236] The depth information includes first point cloud data and second point cloud data.

[0237] In other embodiments of this disclosure, the processor 41 executes an anomaly determination program in the memory 42 to determine, based on the first image to be processed and the second image to be processed, the target region's first point cloud data relative to the self-moving device at a first time and its second point cloud data relative to the self-moving device at a second time, in order to perform the following steps:

[0238] The point cloud data of multiple points to be processed in the first image to be processed and the second image to be processed are determined respectively;

[0239] The first point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the first image to be processed.

[0240] The second point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the second image to be processed.

[0241] The target area includes the target steps.

[0242] In other embodiments of this disclosure, the processor 41 executes an anomaly determination program in the memory 42 to determine the target position of the target region relative to the self-moving device at different times based on multiple depth information, in order to perform the following steps:

[0243] Based on the first depth information, determine the first coordinate of the reference edge of the target step on the target coordinate axis;

[0244] The second coordinate of the reference edge on the target coordinate axis is determined based on the second depth information.

[0245] The depth information includes first depth information and second depth information, and the target location includes first coordinates and second coordinates.

[0246] In other embodiments of this disclosure, the processor 41 executes an anomaly determination program in the memory 42 to determine anomalies in the driving components of the self-moving device based on the relationship between multiple target locations, in order to perform the following steps:

[0247] Determine the current motion state of the self-moving device;

[0248] Based on the current motion state and the relationship between the first and second coordinates, the abnormal conditions of the drive component are determined.

[0249] In other embodiments of this disclosure, the processor 41 executes an anomaly determination program in the memory 42 to determine an anomaly of the driving component based on the current motion state and the relationship between the first and second coordinates, in order to perform the following steps:

[0250] When the current motion state is stationary and the first coordinate is equal to the second coordinate, it is determined that there is no abnormality in the driving component;

[0251] When the current motion state is stationary and the first coordinate is not equal to the second coordinate, it is determined that the driving component is in a slipping state;

[0252] When the current motion state is a moving state, the abnormal situation of the driving component is determined based on the moving direction of the self-moving device and the relationship between the first coordinate and the second coordinate.

[0253] In other embodiments of this disclosure, the processor 41 executes an anomaly determination program in the memory 42 to determine anomalies in the drive unit based on the movement direction of the self-moving device and the relationship between the first and second coordinates, in order to perform the following steps:

[0254] When the first coordinate equals the second coordinate, it is determined that the drive component is in an idling state;

[0255] When the direction of movement is the first direction and the first coordinate is greater than the second coordinate, it is determined that there is no abnormality in the driving component;

[0256] When the direction of movement is the first direction and the first coordinate is less than the second coordinate, it is determined that the driving component is in a slipping state.

[0257] In other embodiments of this disclosure, processor 41 executes an exception determination program in memory 42 to perform the following steps:

[0258] When the direction of movement is the second direction and the first coordinate is greater than the second coordinate, determine the difference between the first coordinate and the second coordinate;

[0259] When the difference is less than or equal to the target threshold, it is determined that there is no abnormality in the drive component; and

[0260] When the difference is greater than the target threshold, the drive component is determined to be in a slipping state.

[0261] It should be noted that the specific details of the steps executed by the processor can be found in the exception determination methods provided in the embodiments corresponding to Figures 1 and 2, and will not be repeated here.

[0262] The self-moving device provided by the embodiments of this disclosure can determine the target position of the target area in the sloped detection area to be traversed by the self-moving device at different times, and determine whether the driving component of the self-moving device has slipped or idled abnormally based on the relationship between the target positions at different times. This solves the problem in the related art that it is impossible to detect whether the wheels of the stair climber are slipping or idling, thereby improving the accuracy of determining the position of the stair climber.

[0263] Based on the foregoing embodiments, the present disclosure provides a computer program product. Referring to FIG8, the computer program product 5 may include a computer program stored in a memory 51 (which may be the same as or different from the memory 42 in the above embodiments). The computer program is finally converted into an executable object file that can be executed by the processor 41 after preprocessing, compilation, assembly and linking processes. The computer program can be executed by the processor 41 to complete the steps of the exception determination method provided in the above embodiments.

[0264] Based on the foregoing embodiments, this disclosure provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the anomaly determination method provided in the above embodiments.

[0265] The computer-readable storage medium is, for example, memory 42. Memory 42 can be volatile memory or non-volatile memory, or memory 42 can include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), SynchLink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0266] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0267] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0268] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0269] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0270] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A self-moving device, comprising: Organism; A drive component, connected to the machine body, to drive the machine body to move; A processor, internally located within the machine body, wherein the processor: When the mobile device is in motion, the depth information of the target area in the sloped detection area to be traversed by the mobile device is determined relative to the mobile device at different times. The target location of the target region relative to the self-moving device is determined based on multiple depth information. as well as The abnormal condition of the drive component is determined based on the relationship between the multiple target locations.

2. The apparatus according to claim 1, wherein, The device further includes a first imaging component connected to the processor, wherein... The first imaging component acquires a first image of the region to be detected relative to the self-moving device at a first moment, and acquires a second image of the region to be detected relative to the self-moving device at a second moment; and The processor further determines, based on the first image to be processed and the second image to be processed acquired by the first imaging component, the first depth information of the target region relative to the self-moving device at the first time and the second depth information relative to the self-moving device at the second time.

3. The apparatus according to claim 1, wherein, The device further includes a second imaging component connected to the processor, wherein... The second imaging component acquires a first image of the region to be detected relative to the self-moving device at a first moment, and acquires a second image of the region to be detected relative to the self-moving device at a second moment; and The processor further determines, based on the first image to be processed and the second image to be processed acquired by the second imaging component, first point cloud data of the target region relative to the self-moving device at the first time and second point cloud data relative to the self-moving device at the second time. The depth information includes the first point cloud data and the second point cloud data.

4. The apparatus according to claim 3, wherein, The processor also includes: The point cloud data of multiple points to be processed in the first image to be processed and the second image to be processed are determined respectively; as well as The first point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the first image to be processed. as well as The second point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the second image to be processed. The target area includes the target steps.

5. The apparatus according to claim 2 or 4, wherein, The processor also includes: Based on the first depth information, determine the first coordinate of the reference edge of the target step on the target coordinate axis; and Based on the second depth information, determine the second coordinate of the reference edge on the target coordinate axis. The plurality of depth information includes the first depth information and the second depth information, and The target location includes the first coordinates and the second coordinates.

6. The apparatus according to claim 5, wherein, The device further includes a sensor connected to the processor, wherein... The sensor is able to determine the current motion state of the self-moving device; The processor can determine abnormal conditions of the drive component based on the current motion state determined by the sensor and the relationship between the first coordinate and the second coordinate.

7. The apparatus according to claim 6, wherein, The processor also includes: When the current motion state is a stationary state and the first coordinate is equal to the second coordinate, it is determined that the driving component is not abnormal; as well as When the current motion state is the stationary state and the first coordinate is not equal to the second coordinate, it is determined that the driving component is in a slipping state.

8. The apparatus according to claim 7, wherein, When the current motion state is a moving state, the processor determines the abnormal situation of the driving component based on the moving direction of the self-moving device and the relationship between the first coordinate and the second coordinate.

9. The apparatus according to claim 8, wherein, The processor also includes: When the first coordinate is equal to the second coordinate, it is determined that the driving component is in an idling state; When the direction of movement is the first direction and the first coordinate is greater than the second coordinate, it is determined that the driving component is not abnormal; as well as When the direction of movement is the first direction and the first coordinate is less than the second coordinate, the driving component is determined to be in the slipping state.

10. The apparatus according to claim 9, wherein, The processor also includes: When the direction of movement is the second direction and the first coordinate is greater than the second coordinate, the difference between the first coordinate and the second coordinate is determined; When the difference is less than or equal to the target threshold, it is determined that the driving component is not abnormal; as well as When the difference is greater than the target threshold, the driving component is determined to be in the slipping state.

11. An anomaly determination method, comprising: When the mobile device is in motion, the depth information of the target area in the sloped detection area to be traversed by the mobile device is determined relative to the mobile device at different times. The target location of the target region relative to the self-moving device is determined based on multiple depth information. The abnormal condition of the driving component of the self-moving device is determined based on the relationship between the multiple target locations.

12. The method according to claim 11, wherein, Determining the depth information of the target area relative to the self-moving device at different times within a sloped detection area to be traversed by the self-moving device includes: At a first moment, a first image to be processed of the region to be detected relative to the self-moving device is acquired by the first imaging component; At the second moment, a second image to be processed of the region to be detected relative to the self-moving device is acquired by the first imaging component; Based on the first image to be processed and the second image to be processed, the first depth information of the target region relative to the self-moving device at the first time and the second depth information relative to the self-moving device at the second time are determined.

13. The method according to claim 11, wherein, Determining the depth information of the target area relative to the self-moving device at different times within a sloped detection area to be traversed by the self-moving device includes: At the first moment, the second imaging component acquires a first image of the region to be detected relative to the self-moving device. At the second moment, a second image to be processed of the region to be detected relative to the self-moving device is acquired by the second imaging component; Based on the first image to be processed and the second image to be processed, the first point cloud data of the target region relative to the self-mobile device at the first time and the second point cloud data relative to the self-mobile device at the second time are determined. The depth information includes the first point cloud data and the second point cloud data.

14. The method according to claim 13, wherein, Based on the first image to be processed and the second image to be processed, determining the target region's first point cloud data relative to the self-mobile device at the first time and the second point cloud data relative to the self-mobile device at the second time includes: The point cloud data of multiple points to be processed in the first image to be processed and the second image to be processed are determined respectively; The first point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the first image to be processed. The second point cloud data of the target step is determined based on the point cloud data of multiple points to be processed in the second image to be processed. The target area includes the target steps.

15. The method according to claim 12 or 14, wherein, Determining the target location of the target region relative to the self-moving device at different times based on multiple depth information includes: Based on the first depth information, determine the first coordinate of the reference edge of the target step on the target coordinate axis; Based on the second depth information, determine the second coordinate of the reference edge on the target coordinate axis. The depth information includes the first depth information and the second depth information, and the target location includes the first coordinates and the second coordinates.

16. The method according to claim 15, wherein, Determining the abnormal condition of the driving component of the self-moving device based on the relationship between multiple target locations includes: Determine the current motion state of the self-moving device; Based on the current motion state and the relationship between the first coordinate and the second coordinate, the abnormal condition of the driving component is determined.

17. The method according to claim 16, wherein, The step of determining the abnormal condition of the driving component based on the current motion state and the relationship between the first coordinate and the second coordinate includes: When the current motion state is a stationary state and the first coordinate is equal to the second coordinate, it is determined that the driving component is not abnormal; When the current motion state is the stationary state and the first coordinate is not equal to the second coordinate, it is determined that the driving component is in a slipping state; When the current motion state is a moving state, the abnormal situation of the driving component is determined based on the moving direction of the self-moving device and the relationship between the first coordinate and the second coordinate.

18. The method according to claim 17, wherein, Based on the movement direction of the self-moving device and the relationship between the first coordinate and the second coordinate, the abnormal condition of the driving component is determined, including: When the first coordinate is equal to the second coordinate, it is determined that the driving component is in an idling state; When the direction of movement is the first direction and the first coordinate is greater than the second coordinate, it is determined that the driving component is not abnormal; When the direction of movement is the first direction and the first coordinate is less than the second coordinate, the driving component is determined to be in the slipping state.

19. The method according to claim 18, wherein, The method further includes: When the direction of movement is the second direction and the first coordinate is greater than the second coordinate, the difference between the first coordinate and the second coordinate is determined; When the difference is less than or equal to the target threshold, it is determined that the driving component is not abnormal; and When the difference is greater than the target threshold, the driving component is determined to be in the slipping state.

20. A self-moving device, comprising: Processor, memory, and communication bus; The communication bus enables communication between the processor and the memory; The processor executes an exception determination program in the memory to implement the steps of the exception determination method as described in any one of claims 11 to 19.

21. A computer-readable storage medium storing one or more programs, wherein, The one or more programs may be executed by one or more processors to implement the steps of the anomaly determination method as described in any one of claims 11 to 19.

22. A computer program product comprising a computer program, wherein, When the computer program is executed by the processor, it implements the exception determination method according to any one of claims 11 to 19.

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