Mobile state detection method and self-moving cleaning device

Abstract

In the motion state detection method and self-moving cleaning device disclosed in this invention, when the self-moving cleaning device determines that it has moved a certain distance based on internal parameters, if the distance measurement value detects a small change in the relative distance between the self-moving cleaning device and objects in the environment, and the data state at this time matches the data state when slipping occurs, it can be directly determined that slipping has occurred. By combining the original hardware configuration of the self-moving cleaning device with relevant data statistics, it is possible to determine whether slipping has occurred. The hardware and software costs for slipping detection are extremely low, and the detection results are accurate. Even self-moving cleaning devices with lower hardware configurations can achieve slipping detection through software upgrades; without increasing purchase costs, users of self-moving cleaning devices with lower hardware configurations can also enjoy a good user experience.

Description

Technical Field

[0001] The present invention relates to the field of automatic cleaning technology, and in particular to a method for detecting movement and a self-moving cleaning device. Background Technology

[0002] With the continuous development of electronic technology, various forms of smart homes have begun to appear in people's lives, providing convenience and improving the quality of life for users in many ways. For example, self-cleaning devices can free people up a significant portion of their time from housework, allowing them more time to experience the richness of life.

[0003] Self-propelled cleaning devices require drive wheels to move across the ground during operation, performing tasks such as cleaning designated areas, returning to the base station for recharging, and washing the mop upon returning to the base station. The mobility of these devices varies depending on the surface conditions. For example, on carpeted surfaces, the distance traveled in one rotation of the drive wheels is equal to the circumference of the drive wheels. On tiled or wood-laid surfaces, the distance traveled is close to the circumference of the drive wheels. However, on areas with oil stains on tiled or wood-laid surfaces, the distance traveled in one rotation is significantly less than the circumference of the drive wheels. This slippage is called slippage. Slippage can easily lead to positioning errors, which in turn affects modules coupled with positioning, such as SLAM and navigation, ultimately resulting in a significant reduction in the completion rate and quality of the cleaning task.

[0004] Existing technologies typically achieve slippage detection by adding sensors and corresponding algorithms. Examples include setting up cameras and corresponding visual odometry calculation methods, and detecting drive wheel current to determine the working status of the drive wheels.

[0005] The inventors analyzed existing solutions for slip detection in self-propelled cleaning devices and found that these solutions all require dedicated or high-precision sensors and corresponding complex algorithms to complete slip detection. The hardware and software costs for slip detection are high, and slip detection cannot even be completed on self-propelled cleaning devices with low hardware configurations. Users can only accept the poor completion rate and quality of the cleaning task, resulting in a poor user experience for self-propelled cleaning devices with low hardware configurations. Summary of the Invention

[0006] This invention provides a motion state detection method and a self-moving cleaning device to solve the technical problems of high hardware and software costs for implementing slippage detection, the inability to complete slippage detection on self-moving cleaning devices with low hardware configurations, and users having to accept poor completion and quality of cleaning tasks, resulting in a poor user experience for self-moving cleaning devices with low hardware configurations.

[0007] In a first aspect, embodiments of the present invention provide a motion state detection method for a self-moving cleaning device, the self-moving cleaning device being equipped with a ranging sensor, the motion state detection method comprising:

[0008] Determine the preset mileage value when the self-moving cleaning equipment moves along a stable heading under the control of a stable linear velocity control quantity.

[0009] The changes in the ranging values ​​detected by the ranging sensor within the time period corresponding to the preset mileage value are statistically analyzed.

[0010] If the statistical results of the change meet the preset slippage conditions, it is determined that the self-moving cleaning equipment has slipped.

[0011] The mileage is determined by integrating the linear speed control quantity or by using an odometer set up on the self-moving cleaning equipment.

[0012] The motion state detection method further includes:

[0013] Record the heading angle of the self-moving cleaning equipment during movement;

[0014] Determine the distance a self-propelled cleaning device travels along a stable heading under stable linear velocity control to reach a preset mileage value, including:

[0015] Determine the preset mileage value when the self-moving cleaning equipment moves under the control of a stable linear velocity control quantity.

[0016] The fluctuation statistics of the heading angle detected within the time period corresponding to the preset mileage value are performed;

[0017] If the results of the fluctuation statistics meet the preset stable heading conditions, the distance traveled along the stable heading is determined to reach the preset mileage value.

[0018] This includes statistical analysis of the fluctuations in heading angles detected within the time period corresponding to the preset mileage value, including:

[0019] The sample heading angle is obtained by downsampling the heading angle detected within the time period corresponding to the preset mileage value;

[0020] The fluctuation statistics of the sample heading angle were analyzed.

[0021] After determining that the self-propelled cleaning equipment has moved a preset distance under the control of a stable linear velocity, the process also includes:

[0022] Reset mobile mileage to 0.

[0023] The motion state detection method further includes:

[0024] Record the distance values ​​detected by the distance measuring sensor during the movement;

[0025] The changes in the ranging values ​​detected by the ranging sensor within the time period corresponding to the preset mileage value are statistically analyzed, including:

[0026] The measured distance values ​​detected within the time period corresponding to the preset mileage value are downsampled to obtain sample measured distance values;

[0027] Statistical analysis was performed on the changes in the sample distance measurements.

[0028] Among them, the statistical results of change statistics and / or fluctuation statistics are the proportion of the corresponding statistical data within the corresponding median range;

[0029] The slippage condition is that the proportion of the corresponding statistical data within the corresponding median range is higher than the preset first threshold;

[0030] The stable heading condition is that the proportion of the corresponding statistical data within the corresponding median range is higher than the preset second threshold.

[0031] Among these steps, after determining that the self-propelled cleaning equipment has slipped, provided that the statistical changes in the ranging values ​​meet the preset slippage conditions, the process also includes:

[0032] Control the self-moving cleaning device to execute a preset slip escape control strategy, and / or, provide slip reminders.

[0033] The ranging sensor is a single-point laser ranging sensor.

[0034] Secondly, embodiments of this application provide a self-moving cleaning device, which includes:

[0035] One or more processors;

[0036] Memory, used to store one or more computer programs;

[0037] When one or more computer programs are executed by one or more processors, the self-moving cleaning device implements a movement state detection method as described in any of the first aspects.

[0038] In the aforementioned motion state detection method and self-moving cleaning device, the self-moving cleaning device is determined to have moved a preset mileage value while maintaining a stable heading under the control of a stable linear velocity control quantity. The ranging values ​​detected by the ranging sensor within the time period corresponding to the preset mileage value are statistically analyzed. If the statistical results meet preset slippage conditions, it is determined that the self-moving cleaning device has slipped. Based on mileage and heading statistics from operation control, the self-moving cleaning device determines that it has moved a preset mileage value while maintaining a stable heading. Then, it statistically analyzes the changes in ranging values. If the changes in ranging values ​​meet preset slippage conditions, it is determined that the self-moving cleaning device has slipped. Essentially, if the self-moving cleaning device, based on its internal parameters, determines that it has moved a certain distance, and the ranging value detects a small change in the relative distance between the self-moving cleaning device and objects in the environment, the data state at this point matches the data state at the time of slippage, and slippage can be directly determined. By combining the original hardware of the self-moving cleaning device with relevant data statistics, it is possible to determine whether slippage has occurred. This achieves slippage detection with extremely low hardware and software costs and accurate results. Even self-moving cleaning devices with lower hardware configurations can complete slip detection through software upgrades; without increasing purchase costs, users of self-moving cleaning devices with lower hardware configurations can also enjoy a good user experience. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the motion state detection method provided in the embodiments of this application.

[0041] Figure 2 This is a schematic diagram illustrating the movement of a self-propelled cleaning device within an indoor area.

[0042] Figure 3 For the purposes of this application's embodiments Figure 2 A schematic diagram of the data processing procedure for slippage detection during the moving process.

[0043] Figure 4 This is a schematic diagram of the hardware structure of the self-moving cleaning device provided in the embodiments of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are for illustrative purposes only and not for limiting the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the drawings, not all of the structures.

[0045] It should be noted that, due to space limitations, this application specification does not exhaustively list all possible implementation methods. Those skilled in the art should be able to conceive after reading this application specification that, as long as the technical features do not contradict each other, any combination of technical features can constitute an optional implementation method.

[0046] The embodiments are described in detail below.

[0047] Existing self-propelled cleaning equipment can move on its own and complete certain cleaning tasks, thereby freeing people from a large part of housework and allowing them more time to experience other rich aspects of life.

[0048] Self-propelled cleaning devices require drive wheels to move across the ground during operation, performing tasks such as cleaning designated areas, returning to the base station for recharging, and washing the mop upon returning to the base station. The mobility of these devices varies depending on the surface conditions. For example, on carpeted surfaces, the distance traveled in one rotation of the drive wheels is equal to the circumference of the drive wheels. On tiled or wood-laid surfaces, the distance traveled is close to the circumference of the drive wheels. However, on areas with oil stains on tiled or wood-laid surfaces, the distance traveled in one rotation is significantly less than the circumference of the drive wheels. This slippage is called slippage. Slippage can easily lead to positioning errors, which in turn affects modules coupled with positioning, such as SLAM and navigation, ultimately resulting in a significant reduction in the completion rate and quality of the cleaning task.

[0049] Existing technologies typically achieve slippage detection by adding sensors and corresponding algorithms. Examples include setting up cameras and corresponding visual odometry calculation methods, and detecting drive wheel current to determine the working status of the drive wheels.

[0050] The inventors analyzed existing slip detection solutions for self-propelled cleaning devices and found that these solutions all require dedicated or high-precision sensors and complex algorithms to achieve slip detection, resulting in high hardware and software costs. For example, setting up a camera and a corresponding visual odometry calculation method requires a dedicated image acquisition module for image acquisition, and also necessitates the development of an image processing-based visual odometry calculation method, leading to high hardware and software costs. While methods that detect slippage based on drive wheel current have a certain probability of detection, there are many reasons why drive wheel current might change, such as changes in ground material, battery voltage, and the load of the self-propelled cleaning device (due to water consumption in the tank, increased or emptied waste). Furthermore, these factors causing changes in drive wheel current occur frequently, making slippage detection based on drive wheel current prone to false alarms. In fact, the high frequency of false alarms might render the slippage detection solution based on drive wheel current impractical. On self-moving cleaning devices with low hardware configurations, slip detection is not even possible. Users can only accept poor completion and quality of cleaning tasks, resulting in a poor user experience for these devices.

[0051] To address the above technical problems, this application proposes a motion state detection method. This method determines that a self-propelled cleaning device, under stable linear velocity control, has reached a preset mileage value while moving along a stable heading. It then statistically analyzes the changes in distance measurements detected by the ranging sensor within the time period corresponding to the preset mileage value. If the statistical results satisfy a preset slip condition, the method determines that the self-propelled cleaning device has slipped. Essentially, when the self-propelled cleaning device, based on mileage and heading statistics from operational control, determines that it has reached a preset mileage value while moving along a stable heading, it statistically analyzes the changes in distance measurements. If the changes in distance measurements satisfy a preset slip condition, the method determines that the self-propelled cleaning device has slipped. In other words, if the self-propelled cleaning device, based on internal parameters, determines that it has moved a certain distance, and the distance measurement detects a small change in the relative distance between the self-propelled cleaning device and objects in the environment, the data state at this point matches the data state at the time of slippage, and slippage can be directly determined. By combining the original hardware of the self-propelled cleaning device with relevant data statistics, slippage detection can be determined, resulting in extremely low hardware and software costs and accurate detection results. Even self-moving cleaning devices with lower hardware configurations can complete slip detection through software upgrades; without increasing purchase costs, users of self-moving cleaning devices with lower hardware configurations can also enjoy a good user experience.

[0052] Please refer to Figure 1This is a flowchart illustrating the motion state detection method provided in this application embodiment. The motion state detection method is implemented by a self-moving cleaning device, the specific product forms of which include, but are not limited to: sweeping robots, floor washing robots, sweeping and mopping robots, cleaning robots, lawnmowing robots, snow removal robots, etc. The self-moving cleaning device can clean using either a front-sweeping-then-mopping method or a separate sweeping and mopping method. The front-sweeping-then-mopping method allows sweeping and mopping to occur simultaneously, improving cleaning efficiency. The separate sweeping and mopping method allows sweeping first, followed by mopping, improving cleaning effectiveness.

[0053] Self-propelled cleaning devices may include a body, a processor, one or more cleaning components, one or more sensors, etc. The body can be circular, square, or other shapes. For example, the front part of the body can be circular, and the rear part can be square. The cleaning components can be circular, square, multi-branched, or other shapes (e.g., semi-circular, arc-shaped, triangular, etc.). A circular shape facilitates rotating cleaning, while other shapes facilitate cleaning corner areas. Cleaning components may include side brushes, roller brushes (also known as floor brushes), and mop trays (also known as mop pads). Side brushes gather debris, moving it towards the center of the bottom of the self-propelled cleaning device for collection. Roller brushes sweep debris from the bottom of the device, allowing it to enter the dust collection box through the suction port. The mop tray is used for wiping or mopping, and contains a mop. The self-propelled cleaning device has a water tank; water from the tank flows through holes to the mop, wetting it for mopping. Self-propelled cleaning devices can clean foreign objects including, but not limited to, dust, hair, and pet feces. Sensors can include lidar sensors (e.g., triangulation sensors, TOF sensors), infrared sensors, line laser sensors, edge sensors, vision sensors (e.g., cameras), and pose sensors. Sensors are used to detect various state information about the self-propelled cleaning device itself or its surroundings. For example, a line laser sensor can detect obstacle information indicating one or more obstacles. The processor can control the self-propelled cleaning device based on the state information detected by the sensors. Among the sensors, the sensor used to detect obstacle information is defined as a ranging sensor. Different types of ranging sensors can emit specific signals (e.g., laser signals, infrared signals) and receive reflected signals from obstacles. Then, based on the time difference between emission and reception and the direction of emission, the relative position of the obstacle and the device itself is determined to complete the ranging. The specific number and type of ranging sensors are not limited. It also includes a moving component, which mainly includes multiple wheels, at least one of which is a drive wheel. The drive wheel is driven to rotate by a motor. The rotation of the drive wheel includes rotation about the axial direction and rotation about the vertical direction. Rotation about the axial direction enables the self-moving cleaning device to move horizontally, and rotation about the vertical direction enables the self-moving cleaning device to change its direction of movement in the horizontal direction.

[0054] like Figure 1 As shown, the motion state detection method includes, but is not limited to, steps S110-S130:

[0055] Step S110: Determine the preset mileage value when the self-moving cleaning equipment moves along a stable heading under the control of a stable linear speed control quantity.

[0056] During operation, the self-propelled cleaning device internally plans a movement path based on the target and then moves towards the target at an appropriate speed and direction. The speed is controlled by a linear velocity control quantity, which characterizes the instantaneous state. During the acceleration phase, the linear velocity control quantity continuously increases; during the constant-speed phase, it remains stable; and during the deceleration phase, it continuously decreases. In this embodiment, during the acceleration and deceleration phases, the device's speed can be considered controllable, and slippage is considered to be absent. Slippage is only possible during the constant-speed phase, and slippage detection is performed accordingly.

[0057] In this embodiment, the inventors analyzed data relationships during the movement of the self-propelled cleaning device and found that, at the control level and in terms of internal operating status detection, the rotation speed of the drive wheel remains stable when slippage occurs. However, regarding the actual distance traveled by the self-propelled cleaning device, because there is sliding between the drive wheel and the ground when slippage occurs—sometimes with very little or no rolling—the actual distance traveled is less than the total circumference corresponding to the number of rotations of the drive wheel during this period. In other words, the change in travel distance reflected by the distance measured by the ranging sensor at the same point is less than the change in the expected travel distance according to the control command.

[0058] During the turning process of the self-propelled cleaning equipment, the distance sensor detects the distance corresponding to different positions. These distance changes are not necessarily related to the changes in the rotation distance of the drive wheels controlled by the control commands. Therefore, in this scheme, subsequent judgments are only made when the heading remains relatively stable. That is, after determining that the self-propelled cleaning equipment has moved a preset distance along a stable heading under the control of a stable linear velocity, subsequent state judgments are made.

[0059] Slip detection is performed continuously during the movement of the self-propelled cleaning equipment. To ensure accurate detection results and timely control of the equipment based on these results, a preset mileage value is used to determine the specific time for slip detection. The interval between slip detections is constrained by the preset mileage value, which is set based on the equipment's movement capability or specific target. A suitable value should be chosen based on actual test results. If the preset mileage value is too small, the actual distance detected during slip detection may be close to or equal to the distance detected during normal movement (close to or equal to the preset mileage value), making it impossible to accurately determine whether slip has occurred. If the preset mileage value is too large, the time interval between slip detections is too long, resulting in a low detection frequency. During slip detection, the actual distance detected may deviate significantly from the distance detected during normal movement (close to or equal to the preset mileage value), indicating that slippage may have already occurred for some time. Movement or cleaning during this period is unnecessary. An appropriately sized preset mileage value can trigger slip detection quickly, allowing for appropriate action and enabling the self-propelled cleaning equipment to return to normal movement as soon as possible.

[0060] After the self-moving cleaning device reaches the preset mileage value under the control of a stable linear speed, the mileage is reset to 0 for the next slip detection. Of course, if the current movement at the stable linear speed control value has not yet reached the mileage, and the linear speed control value fluctuates, the current basic data acquisition process is interrupted, and the mileage is also reset to 0 for the next slip detection.

[0061] In a specific implementation, the mileage is determined either by integrating the linear velocity control quantity or by using an odometer installed on the self-moving cleaning device. The linear velocity control quantity is generated by the processor of the self-moving cleaning device during its movement. This quantity characterizes instantaneous speed. Integrating the linear velocity control quantity over a period of time, if there is no slippage, the integrated result is the mileage traveled by the self-moving cleaning device during that period. Alternatively, the mileage can be directly calculated using an odometer installed on the drive wheel. This odometer contains a code disk that detects the rotation angle of the drive wheel. Based on the radius of the drive wheel, the rotation distance of the drive wheel surface can be determined. If there is no slippage, this rotation distance is the actual mileage traveled. The mileage, determined by integrating the linear velocity control quantity or by using an odometer installed on the self-moving cleaning device, is the mileage traveled and also the expected mileage during the movement of the self-moving cleaning device.

[0062] The self-moving cleaning device in this embodiment can be equipped with a gyroscope. The gyroscope can output the machine's attitude in Euler angles (i.e., the angles of rotation around the x, y, and z axes in three-dimensional Euclidean space, corresponding to roll, yaw, and pitch angles; typically, the y-axis is a coordinate axis perpendicular to the machine's plane, and the yaw angle can represent the machine's orientation). The yaw angle is the heading angle of the self-moving cleaning device. The gyroscope continuously detects the heading angle at different times during the movement of the self-moving cleaning device. Whether the heading is stable can be obtained by statistically analyzing the heading angles at multiple times.

[0063] To achieve statistical analysis of heading angles at multiple time points, the heading angle of the self-moving cleaning device is recorded during movement. In determining whether the self-moving cleaning device, under the control of a stable linear velocity control quantity, has reached a preset mileage value in terms of the distance traveled along a stable heading, the process first determines that the distance traveled under the control of the stable linear velocity control quantity has reached the preset mileage value. Then, the fluctuation of the heading angle detected within the time period corresponding to the preset mileage value is statistically analyzed. If the result of the fluctuation statistics meets the preset stable heading conditions, it is determined that the distance traveled along the stable heading has reached the preset mileage value. As described above, the linear velocity control quantity is data generated internally by the processor. It is sufficient to directly determine whether the linear velocity control quantity is stable internally. A stable linear velocity control quantity can be data fluctuating within a preset, relatively small range; that is, continuous linear velocity control quantities can have different specific values, but they are considered stable only if they are relatively close overall. If the self-moving cleaning equipment moves under the control of a stable linear velocity and reaches the preset mileage value, then the course angle fluctuation during this period is statistically analyzed. If the result of the fluctuation statistics meets the preset stable course condition, it indicates that the course of the self-moving cleaning equipment is stable during this period, and it is determined that the moving mileage along the stable course has reached the preset mileage value.

[0064] In one specific implementation, fluctuation statistics are performed on the heading angles detected within a time period corresponding to a preset mileage value. This includes: firstly, downsampling the heading angles detected within the time period corresponding to the preset mileage value to obtain sample heading angles; then, fluctuation statistics are performed on the sample heading angles. In other words, the result of fluctuation statistics on a portion of the heading angles within a time period is used as the result of the overall fluctuation statistics for that time period, thereby reducing the amount of data processing and improving the speed of skid detection.

[0065] In the specific statistical process, regardless of whether the total heading angles or the sample heading angles are used as the corresponding statistical data, the statistics are the proportion of the corresponding statistical data within the corresponding median range. That is, when conducting the statistics, the data is first sorted to determine the median (which can be the median, mean, etc.). Then, a smaller range is determined as the median range with the median as a reference. Then, the heading angles within the median range of the corresponding statistical data are counted, as well as the proportion of these heading angles in the corresponding statistical data. The pre-set stable heading condition is that the proportion of the corresponding statistical data within the corresponding median range is higher than a preset second threshold. If the counted proportion is higher than the second threshold, it is determined that the movement is in a stable heading manner during this time period.

[0066] Step S120: Perform statistical analysis on the changes in the ranging values ​​detected by the ranging sensor within the time period corresponding to the preset mileage value.

[0067] Distance sensors include point laser rangefinders or other types of distance sensors. Point laser sensors emit laser light and receive the reflected light, determining the distance to obstacles based on the time difference between emission and reception. Point laser rangefinders may be fixedly mounted on the top or side of a self-propelled cleaning device. The device needs to rotate one full circle to detect the distance to obstacles in its surrounding area. To ensure normal movement, the device typically rotates at intervals for detection. This solution effectively utilizes the distance values ​​collected during these intervals for slip detection, achieving accurate slip detection at low cost without affecting existing detection requirements.

[0068] To achieve statistical analysis of distance measurements at multiple time points, the distance measurements of the self-propelled cleaning device are recorded during its movement. In one specific implementation, the changes in distance measurements detected by the distance sensor within a time period corresponding to a preset mileage value are statistically analyzed. This includes: firstly, downsampling the distance measurements detected within the time period corresponding to the preset mileage value to obtain sample distance measurements; then, performing statistical analysis on the changes in the sample distance measurements. In other words, the result of statistical analysis on the changes in a subset of distance measurements within a time period is used as the result of the statistical analysis for the entire time period, thereby reducing the amount of data processing and improving the speed of slip detection.

[0069] In the specific implementation process, after a slip detection is completed or it is determined that a slip detection is no longer needed, the previously recorded distance and heading angle can be directly deleted. That is, only the distance and heading angle within a small segment corresponding to the movement distance from 0 to the preset distance value are recorded, thereby reducing the data storage burden of the self-moving cleaning equipment.

[0070] In the specific statistical process, regardless of whether all distance measurements or sample distance measurements are used as the corresponding statistical data, the statistics are based on the proportion of the corresponding statistical data within the corresponding median range. That is, during the statistics, the data is first sorted to determine the median (which can be the median, mean, etc.). Then, a smaller range is determined as the median range based on the median value. Next, the distance measurements within the median range and the proportion of these distance measurements in the corresponding statistical data are counted. The pre-set slip condition is that the proportion of the corresponding statistical data within the corresponding median range is higher than a preset first threshold. If the counted proportion is higher than the first threshold, it is determined that the actual distance moved within this time period changes little. In other words, during the control of the self-moving cleaning equipment, the actual position of the self-moving cleaning equipment changes very little, which corresponds to the movement state of the self-moving cleaning equipment being slipped.

[0071] Step S130: If the results of the change statistics meet the preset slip conditions, it is determined that the self-moving cleaning equipment has slipped.

[0072] When it is determined that the self-propelled cleaning device is slipping, targeted measures need to be taken to address the current situation. Targeted measures could include controlling the self-propelled cleaning device to execute a preset slip escape control strategy, such as cleaning oil stains on the ground, increasing the speed of the drive wheels, or oscillating the drive wheels left and right to clean the ground or increase traction, thereby enabling it to escape from the slipping position. Targeted measures could also include issuing a slip reminder, allowing the user to assist in resolving the slipping problem. Slip reminders could include voice alarms or sending reminder information to the user's control terminal via the self-propelled cleaning device's management application. Targeted measures could also involve issuing a slip reminder simultaneously with or after executing the slip escape control strategy for a period of time, if the movement state detection method in this embodiment determines that the device is still in a slipping state.

[0073] For a more detailed implementation of the slip detection scheme in this paper, please refer to [the relevant documentation / reference]. Figure 2 and Figure 3 .exist Figure 2 In the indoor area 10, a base station 20 is set in the upper left corner. The self-moving cleaning device 21 usually stays in the base station 20. When there is a cleaning task, it starts from the base station 20 and moves according to the cleaning route generated by the area map corresponding to the indoor area 10. Figure 2 The diagram illustrates three sub-stages of the self-moving cleaning device 21 during its movement. The linear velocity control values ​​in the three sub-stages are essentially the same, and can be considered as movement under the control of a stable linear velocity control value. The first sub-stage involves rotating 270° clockwise from point A, the second sub-stage involves moving from point A to point B, and the third sub-stage involves moving from point B to point C.

[0074] exist Figure 3 The diagram shows the range and heading angles collected in three sub-stages. The first sub-stage corresponds to time t0 to time t1, the second sub-stage to time t1 to time t2, and the third sub-stage to time t2 to time t3. The data recorded in the first sub-stage is shown in... Figure 3 The dataset is represented as ranging value dataset D11 and heading angle dataset D21. The data recorded in the second sub-stage is... Figure 3 The dataset is represented as ranging value dataset D12 and heading angle dataset D22. The data recorded in the third sub-stage is... Figure 3 The data is represented as the distance measurement dataset D13 and the heading angle dataset D23.

[0075] Based on the integral of the linear velocity control quantity or the odometer readings, it can be determined that the movement mileage in the first, second, and third sub-stages has reached the preset mileage value. That is, if slippage does not occur according to the corresponding movement control of the first, second, and third sub-stages, the self-moving cleaning device 21 should move to the preset mileage value. When the self-moving cleaning device 21 moves to the preset mileage value under stable linear velocity control, whether slippage needs to be determined can be determined by first statistically analyzing the heading angle. If the heading angle statistical results indicate a stable heading, then the distance measurement value can be statistically analyzed to determine whether slippage has occurred.

[0076] For the first small stage, at time t1, it is determined that the travel distance reaches the preset mileage value under the control of a stable linear velocity control quantity. At this time, the travel distance can be reset to 0, and multiple heading angles in the heading angle dataset D21 are directly sorted. The median is determined based on the sorting result. Then, the number of heading angles within the median range centered on the median is counted, thereby determining the proportion of heading angles within the median range in the heading angle dataset D21. Obviously, the proportion of heading angles within the median range is very low, and slippage detection is not required. The ranging values ​​in the ranging value dataset D11 are not processed directly. In the specific implementation, the heading angles in the heading angle dataset D21 can also be downsampled, for example... Figure 3 As shown, a heading angle is sampled every other heading angle in time sequence to obtain a sample heading angle dataset D21ˋ. The heading angles in the sample heading angle dataset D21ˋ are statistically analyzed in the same way to obtain the proportion of heading angles within the median range in the sample heading angle dataset D21ˋ, which serves as the result of the fluctuation statistics for the first small stage.

[0077] For the second sub-stage, at time t2, it is determined that the distance traveled reaches the preset distance value under the control of a stable linear velocity control quantity. At this time, the distance traveled can be reset to 0, and multiple heading angles in the heading angle dataset D22 can be sorted directly. The median is determined based on the sorting result. Then, the number of heading angles within the median range centered on the median is counted, thereby determining the proportion of heading angles within the median range in the heading angle dataset D22. Obviously, the heading in the second sub-stage is relatively stable, and the proportion of heading angles within the median range reaches the second threshold, satisfying the preset stable heading condition. At this time, slippage detection is required. During slip detection, multiple distance values ​​in the distance measurement dataset D12 are sorted, and the median is determined based on the sorting result. Then, the number of distance values ​​within the median range centered on the median is counted, thus determining the proportion of distance values ​​within the median range in the distance measurement dataset D12. In the second sub-stage, there is no slip. The distance values ​​detected by the distance sensor each time show a significant change compared to the previous detection. This is reflected in the statistical results of the distance measurement dataset D12, where the proportion of distance values ​​within the median range is lower than the first threshold, failing to meet the preset slip condition. Therefore, it is determined that there is no slip in the second sub-stage. In a specific implementation, the heading angle in the heading angle dataset D22 can also be downsampled, for example... Figure 3 As shown, a heading angle is sampled sequentially at every other heading angle to obtain a sample heading angle dataset D22ˋ. The heading angles in the sample heading angle dataset D22ˋ are then statistically analyzed in the same way to obtain the proportion of heading angles within the median range in the sample heading angle dataset D22ˋ, which serves as the result of the fluctuation statistics for the second small stage. Alternatively, the ranging values ​​in the ranging value dataset D12 can be downsampled, for example... Figure 3 As shown, a distance measurement value is sampled every other distance measurement value in time sequence to obtain a sample distance measurement value dataset D12ˋ. The distance measurement values ​​in the sample distance measurement value dataset D12ˋ are statistically analyzed in the same way to obtain the proportion of distance measurement values ​​within the median range in the sample distance measurement value dataset D12ˋ, which is used as the result of the change statistics in the second small stage.

[0078] For the third sub-stage, at time t3, it is determined that the distance traveled reaches the preset distance value under the control of a stable linear velocity control quantity. At this time, the distance traveled can be reset to 0, and multiple heading angles in the heading angle dataset D23 can be sorted directly. The median is determined based on the sorting result. Then, the number of heading angles within the median range centered on the median is counted, and the proportion of heading angles within the median range in the heading angle dataset D23 is determined. Obviously, the heading in the third sub-stage is relatively stable, and the proportion of heading angles within the median range reaches the second threshold, satisfying the preset stable heading condition. At this time, slippage detection is required. During slippage detection, multiple distance values ​​in the distance measurement dataset D13 are sorted, and the median is determined based on the sorting result. Then, the number of distance values ​​within the median range centered on the median is counted, thus determining the proportion of distance values ​​within the median range in the distance measurement dataset D13. In the third sub-stage, slippage occurs, and some distance values ​​detected by the distance sensor may not have changed significantly compared to the previous detection. This is reflected in the statistical result of distance measurement dataset D13 where the proportion of distance values ​​within the median range exceeds the first threshold, satisfying the preset slippage condition. At this point, slippage is determined to have occurred in the third sub-stage. In practical implementation, the heading angle in the heading angle dataset D23 can also be downsampled, for example... Figure 3 As shown, a heading angle is sampled every other heading angle in time sequence to obtain a sample heading angle dataset D23ˋ. The heading angles in the sample heading angle dataset D23ˋ are then statistically analyzed in the same way to obtain the proportion of heading angles within the median range in the sample heading angle dataset D23ˋ, which serves as the result of the fluctuation statistics for the third sub-stage. Alternatively, the ranging values ​​in the ranging value dataset D13 can be downsampled, for example... Figure 3 As shown, a distance measurement value is sampled every other distance measurement value in time sequence to obtain a sample distance measurement value dataset D13ˋ. The distance measurement values ​​in the sample distance measurement value dataset D13ˋ are statistically analyzed in the same way to obtain the proportion of distance measurement values ​​within the median range in the sample distance measurement value dataset D13ˋ, which is used as the result of the change statistics in the third small stage.

[0079] In the aforementioned motion state detection method, it is determined that the self-moving cleaning device, under the control of a stable linear velocity control quantity, has moved a preset mileage value along a stable heading. The changes in the distance measured by the ranging sensor within the time period corresponding to the preset mileage value are statistically analyzed. If the statistical results meet preset slippage conditions, it is determined that the self-moving cleaning device has slipped. Based on mileage and heading statistics from operational control, the self-moving cleaning device determines that it has moved a preset mileage value along a stable heading. Then, the changes in the distance measured are statistically analyzed. If the changes in the distance measured meet preset slippage conditions, it is determined that the self-moving cleaning device has slipped. Essentially, if the self-moving cleaning device, based on its internal parameters, determines that it has moved a certain distance, and the distance measured shows a small change in the relative distance between the self-moving cleaning device and objects in the environment, the data state at this point matches the data state at the time of slippage, and slippage can be directly determined. By combining the original hardware of the self-moving cleaning device with relevant data statistics, it is possible to determine whether slippage has occurred. The hardware and software costs for slippage detection are extremely low, and the detection results are accurate. Even self-moving cleaning devices with lower hardware configurations can complete slip detection through software upgrades; without increasing purchase costs, users of self-moving cleaning devices with lower hardware configurations can also enjoy a good user experience.

[0080] Figure 4 This is a schematic diagram of the structure of a self-moving cleaning device provided in an embodiment of this application. Figure 4 As shown, the self-propelled cleaning device includes a processor 310 and a memory 320. The self-propelled cleaning device may also include an input device 330, an output device 340, and a communication device 350. The number of processors 310 in the self-propelled cleaning device can be one or more. Figure 4 Taking a processor 310 as an example; the processor 310, memory 320, input device 330, output device 340, and communication device 350 in the self-propelled cleaning device can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0081] The memory 320, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the mobility state detection method in the embodiments of this application. The processor 310 executes various functional applications and data processing of the self-moving cleaning device by running the software programs, instructions, and modules stored in the memory 320, thereby realizing the aforementioned mobility state detection method.

[0082] The memory 320 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on the use of the self-propelled cleaning device. Furthermore, the memory 320 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 320 may further include memory remotely located relative to the processor 310, which can be connected to the self-propelled cleaning device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0083] Input device 330 can be used to receive network configuration information. Output device 340 may include a display device such as a screen.

[0084] The aforementioned self-moving cleaning equipment can be used to perform any movement state detection method, and has the corresponding functions and beneficial effects.

[0085] This invention also provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to perform relevant operations in the motion state detection method provided in any embodiment of this application, and have corresponding functions and beneficial effects.

[0086] Those skilled in the art will understand that embodiments of this application may be provided as methods, systems, or computer program products.

[0087] Therefore, this application may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should 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, produce implementations of the flowchart... Figure 1 One or more processes and / or boxes Figure 1The 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 operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. 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, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0088] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0089] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0090] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0091] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A movement status detection method for self-moving cleaning equipment, characterized in that, The self-moving cleaning device is equipped with a distance sensor, and the movement state detection method includes: The self-moving cleaning device, under the control of a stable linear velocity control quantity, moves along a stable heading until the preset mileage value is reached. The changes in the distance values ​​detected by the distance measuring sensor within the time period corresponding to the preset mileage value are statistically analyzed. If the statistical results of the changes meet the preset slippage conditions, it is determined that the self-moving cleaning device has slipped.

2. The movement state detection method according to claim 1, characterized in that, The travel distance is determined by integrating the linear speed control quantity, or by the odometer installed on the self-moving cleaning device.

3. The movement state detection method according to claim 1, characterized in that, Also includes: Record the heading angle of the self-moving cleaning equipment during movement; The determination that the self-moving cleaning device moves along a stable heading under the control of a stable linear velocity control quantity until the preset mileage value is reached includes: The self-moving cleaning device moves to a preset mileage value under the control of a stable linear velocity control quantity. The fluctuation statistics of the heading angle detected within the time period corresponding to the preset mileage value are performed. If the results of the fluctuation statistics meet the preset stable heading conditions, the distance traveled along the stable heading is determined to reach the preset mileage value.

4. The movement state detection method according to claim 3, characterized in that, The step of statistically analyzing the fluctuations in the heading angle detected within the time period corresponding to the preset mileage value includes: The sample heading angle is obtained by downsampling the heading angle detected within the time period corresponding to the preset mileage value; The fluctuation statistics of the heading angle of the sample are performed.

5. The movement state detection method according to claim 3, characterized in that, After determining that the self-moving cleaning device has moved a preset distance under the control of a stable linear velocity control quantity, the process further includes: Reset the mobile mileage to 0.

6. The motion state detection method according to any one of claims 1-5, characterized in that, Also includes: Record the distance values ​​detected by the distance measuring sensor during the movement; The step of statistically analyzing the changes in the ranging values ​​detected by the ranging sensor within the time period corresponding to the preset mileage value includes: The distance values ​​detected within the time period corresponding to the preset mileage value are downsampled to obtain sample distance values; The variation statistics of the sample distance values ​​are analyzed.

7. The movement state detection method according to claim 6, characterized in that, The statistical results of the change statistics and / or fluctuation statistics are the proportion of the corresponding statistical data within the corresponding median range; The slippage condition is that the proportion of the corresponding statistical data within the corresponding median range is higher than a preset first threshold. The stable heading condition is that the proportion of the corresponding statistical data within the corresponding median range is higher than a preset second threshold.

8. The motion state detection method according to any one of claims 1-5, characterized in that, After determining that the self-moving cleaning device has slipped when the statistical change in the ranging value meets the preset slipping conditions, the process further includes: The self-moving cleaning device is controlled to execute a preset slip escape control strategy, and / or to provide a slip reminder.

9. The motion state detection method according to any one of claims 1-5, characterized in that, The ranging sensor is a single-point laser ranging sensor.

10. A self-propelled cleaning device, characterized in that, include: One or more processors; Memory, used to store one or more computer programs; When the one or more computer programs are executed by the one or more processors, the self-moving cleaning device implements the movement state detection method as described in any one of claims 1-9.

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