Slippage detection method, device, and storage medium

Abstract

The present invention provides a slippage detection method, device, and storage medium. [Solution] In this invention, while the automatic cleaning device is running, measured distance values ​​and multiple frame point clouds are acquired within a predetermined detection time period, and the measured distance values ​​and multiple frame point clouds can be used to quickly and accurately identify whether the automatic cleaning device is in a slipping state, thereby improving the system's sensing efficiency and detection sensitivity and improving the user experience.

Description

Detailed Description of the Invention

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims priority based on a Chinese patent application filed in China on December 30, 2022, bearing application number 202211729463.2, the entire contents of which are incorporated herein by reference. [Technical Field]

[0002] The present invention relates to the technical field of automatic cleaning devices, and in particular to a slippage detection method, device and storage medium. [Background technology]

[0003] With the development of smart technology, automatic cleaning devices, such as cleaning robots, are becoming more and more widely used. When cleaning robots encounter situations in user environments, such as thresholds, fan bases, etc., they are prone to slippage, which can affect user experience. Summary of the Invention

[0004] The present invention aims to provide a slippage detection method, device and storage medium. Specific solutions are as follows:

[0005] According to a specific embodiment of the present invention, in a first aspect, the present invention provides a slippage detection method, the slippage detection method comprising: While the automatic cleaning device is running, a measured distance value representing a rotation distance value of a drive wheel assembly is acquired by odometry within a predetermined detection time period, and a plurality of frame point clouds are acquired by a laser radar; and identifying the automated cleaning device as being in a slipping state based on the distance measurements and the plurality of frame point clouds.

[0006] Optionally, identifying the automated cleaning device as being in a slipping state based on the distance measurements and the plurality of frame point clouds includes: When the measured distance value is greater than a predetermined measured distance threshold, determining whether a position change has occurred in the automatic cleaning device based on the plurality of frame point clouds; and determining that the automatic cleaning device is in a slip state when it is determined that no position change has occurred in the automatic cleaning device.

[0007] Optionally, identifying the automated cleaning device as being in a slipping state based on the distance measurements and the plurality of frame point clouds includes: determining whether a position change has occurred in the automated cleaning device based on the plurality of frame point clouds; When it is determined that no position change has occurred in the automatic cleaning device based on the plurality of frame point clouds, determining whether the measured distance value is greater than a predetermined measured distance threshold value; and identifying the automatic cleaning device as being in a slip state when the measured distance value is greater than a predetermined measured distance threshold.

[0008] Optionally, each frame point cloud includes a plurality of point data; Accordingly, determining whether a position change has occurred in the automated cleaning device based on the plurality of frame point clouds may include: assigning the plurality of frame point clouds to a first point cloud set including a plurality of first frame point clouds or a second point cloud set including a plurality of second frame point clouds; performing similarity matching based on the plurality of first frame point clouds and the plurality of second frame point clouds to obtain a similarity matching result; and determining whether a position change has occurred to the automated cleaning device based on the similarity matching results.

[0009] Optionally, assigning the plurality of frame point clouds to a first point cloud set or a second point cloud set, respectively, comprises: Dividing a predetermined detection time period into a first detection time period and a second detection time period; The method includes assigning a plurality of frame point clouds within the first detection time period to a first point cloud set, and assigning a plurality of frame point clouds within the second detection time period to a second point cloud set.

[0010] Optionally, the processor is configured to assign the plurality of frame point clouds to a first point cloud set or a second point cloud set, respectively, by: Dividing a predetermined detection time period into a first detection time period and a second detection time period; The method includes assigning a plurality of frame point clouds within the first detection time period to a first point cloud set, and assigning a plurality of frame point clouds within the second detection time period to a second point cloud set.

[0011] Optionally, performing similarity matching based on the plurality of first frame point clouds and the plurality of second frame point clouds to obtain a similarity matching result includes: projecting the two-dimensional position of each point data in each first frame point cloud onto a predetermined plane to obtain a first planar map including a plurality of first projection positions, and projecting the two-dimensional position of each point data in each second frame point cloud onto a predetermined plane to obtain a second planar map including a plurality of second projection positions; and performing similarity matching based on a plurality of first projection positions in the first planar map and a plurality of second projection positions in the second planar map to obtain a similarity matching result.

[0012] Optionally, the pre-formed plane comprises a pre-formed grating plane; the first planar map includes a first grid planar map, and includes a plurality of first occupied grids in a plurality of grids of the first grid planar map, each of the first occupied grids including at least one first projection location; the second planar map includes a second grid planar map, and includes a plurality of second occupied grids in a plurality of grids of the second grid planar map, each of the second occupied grids including at least one second projection location; Correspondingly, performing similarity matching based on a plurality of first projection positions in the first planar map and a plurality of second projection positions in the second planar map to obtain a similarity matching result includes: performing grid similarity matching based on the at least one first projected position in each first occupied grid of the first grid planar map and the at least one second projected position in each second occupied grid of the second grid planar map to obtain a similarity matching result.

[0013] Optionally, performing grid similarity matching based on the at least one first projected position in each first occupied grid of the first grid planar map and the at least one second projected position in each second occupied grid of the second grid planar map to obtain a similarity matching result includes: obtaining a first position normalization value corresponding to a first occupied grid based on the at least one first projected position in each first occupied grid of the first grid planar map, and obtaining a second position normalization value corresponding to a second grid based on the at least one second projected position in each second grid of the second grid planar map; and performing grid similarity matching based on the first position normalized value of each first occupied grid in the first grid planar map and the second position normalized value of each second occupied grid in the second grid planar map to obtain a similarity matching result.

[0014] Optionally, performing grid similarity matching based on a first position normalized value of each first occupied grid in the first grid planar map and a second position normalized value of each second occupied grid in the second grid planar map to obtain a similarity matching result includes: When any one of the first occupied grids in the first grid planar map is traversed, a first calculated parameter value is increased by 1; Incrementing a second calculation parameter value by 1 when a first position normalization value of any one of the first occupied lattices and a second position normalization value of a lattice corresponding to any one of the first occupied lattices in the second lattice planar map satisfy a predetermined identical condition; After the traversal is completed, calculating a quotient of the second calculated parameter value and the first calculated parameter value to obtain a first similarity matching result.

[0015] Optionally, performing grid similarity matching based on a first position normalized value of each first occupied grid in the first grid planar map and a second position normalized value of each second occupied grid in the second grid planar map to obtain a similarity matching result includes: When any one second occupied grid in the second grid planar map is traversed, a third calculation parameter value is increased by 1; Incrementing a fourth calculation parameter value by 1 when a second position normalization value of any one of the second occupied lattices and a first position normalization value of a lattice corresponding to any one of the second occupied lattices in the first lattice planar map satisfy a predetermined identical condition; After the traversal is completed, calculating a quotient of the fourth calculated parameter value and the third calculated parameter value to obtain a second similarity matching result.

[0016] Optionally, obtaining a first position normalization value corresponding to a first occupied grid based on the at least one first projected position in each first occupied grid of the first grid planar map, and obtaining a second position normalization value corresponding to a second grid based on the at least one second projected position in each second grid of the second grid planar map, may include: Identifying a first position normalization value corresponding to each first occupied grid as 1 based on the at least one first projected position in each first occupied grid of the first grid planar map; and determining a second position normalization value corresponding to each second grid as 1 based on the at least one first projected position in each second grid of the second grid planar map; Each grid in the first grid plane map has an initial position normalization value of zero before similarity matching is performed, and each grid in the second grid plane map has an initial position normalization value of zero before similarity matching is performed.

[0017] Optionally, determining whether a position change has occurred in the automated cleaning device based on the similarity matching results includes: The method includes determining that no position change has occurred in the automatic cleaning device when the first similarity matching result and / or the second similarity matching result is equal to or greater than a predetermined occupancy ratio threshold.

[0018] Optionally, determining whether a position change has occurred in the automated cleaning device based on the similarity matching results includes: The method includes determining that a position change has occurred in the automatic cleaning device when the first similarity matching result and / or the second similarity matching result is smaller than a predetermined occupancy ratio threshold.

[0019] According to a specific embodiment of the present invention, in a second aspect, the present invention provides a slippage detection device, the slippage detection device comprising: an odometry device, a laser radar, and a processor; the odometry is configured to obtain a measured distance value representing a rotation distance value of a drive wheel assembly; The laser radar is configured to acquire a frame point cloud while the automatic cleaning equipment is traveling; The processor is communicatively connected to the odometry and the laser radar, and the processor While the automatic cleaning device is running, a measured distance value representing a rotation distance value of a drive wheel assembly is acquired by odometry within a predetermined detection time period, and a plurality of frame point clouds are acquired by laser radar; The automated cleaning device is configured to identify a slippage state based on the distance measurements and the plurality of point frames.

[0020] Optionally, the processor is configured to identify the automatic cleaning device as being in a slipping state based on the distance measurements and the plurality of frame point clouds, further comprising: When the measured distance value is greater than a predetermined measured distance threshold, determining whether a position change has occurred in the automatic cleaning device based on the plurality of frame point clouds; and determining that the automatic cleaning device is in a slip state when it is determined that no position change has occurred in the automatic cleaning device.

[0021] Optionally, the processor is configured to identify the automatic cleaning device as being in a slipping state based on the distance measurements and the plurality of frame point clouds, further comprising: determining whether a position change has occurred in the automated cleaning device based on the plurality of frame point clouds; When it is determined that no position change has occurred in the automatic cleaning device based on the plurality of frame point clouds, determining whether the measured distance value is greater than a predetermined measured distance threshold value; and identifying the automatic cleaning device as being in a slip state when the measured distance value is greater than a predetermined measured distance threshold.

[0022] Optionally, each frame point cloud includes a plurality of point data; Accordingly, the processor may be configured to determine whether a position change has occurred in the automatic cleaning device based on the plurality of frame point clouds, Assigning the plurality of frame point clouds to a first point cloud set or a second point cloud set, respectively, where the first point cloud set includes a plurality of first frame point clouds and the second point cloud set includes a plurality of second frame point clouds; performing similarity matching based on the plurality of first frame point clouds and the plurality of second frame point clouds to obtain a similarity matching result; and determining whether a position change has occurred to the automated cleaning device based on the similarity matching results.

[0023] Optionally, the processor is configured to perform similarity matching based on the plurality of first frame point clouds and the plurality of second frame point clouds to obtain a similarity matching result, A first planar map is obtained by projecting the two-dimensional position of each point data in each first frame point cloud onto a predetermined plane, and a second planar map is obtained by projecting the two-dimensional position of each point data in each second frame point cloud onto a predetermined plane, wherein the first planar map includes a plurality of first projection positions, and the second planar map includes a plurality of second projection positions; and performing similarity matching based on a plurality of first projection positions in the first planar map and a plurality of second projection positions in the second planar map to obtain a similarity matching result.

[0024] Optionally, the pre-formed plane comprises a pre-formed grating plane; the first planar map includes a first grid planar map, and includes a plurality of first occupied grids in a plurality of grids of the first grid planar map, each of the first occupied grids including at least one first projection location; the second planar map includes a second grid planar map, and includes a plurality of second occupied grids in a plurality of grids of the second grid planar map, each of the second occupied grids including at least one second projection location; Correspondingly, the processor is configured to perform similarity matching based on a plurality of first projection positions in the first planar map and a plurality of second projection positions in the second planar map to obtain a similarity matching result, performing grid similarity matching based on the at least one first projected position in each first occupied grid of the first grid planar map and the at least one second projected position in each second occupied grid of the second grid planar map to obtain a similarity matching result.

[0025] Optionally, the processor is configured to perform grid similarity matching based on the at least one first projected position in each first occupied grid of the first grid planar map and the at least one second projected position in each second occupied grid of the second grid planar map to obtain a similarity matching result, obtaining a first position normalization value corresponding to a first occupied grid based on the at least one first projected position in each first occupied grid of the first grid planar map, and obtaining a second position normalization value corresponding to a second grid based on the at least one second projected position in each second grid of the second grid planar map; and performing grid similarity matching based on the first position normalized value of each first occupied grid in the first grid planar map and the second position normalized value of each second occupied grid in the second grid planar map to obtain a similarity matching result.

[0026] Optionally, the processor is configured to perform grid similarity matching based on a first position normalized value of each first occupied grid in the first grid plane map and a second position normalized value of each second occupied grid in the second grid plane map to obtain a similarity matching result, When any one of the first occupied grids in the first grid planar map is traversed, a first calculated parameter value is increased by 1; Incrementing a second calculation parameter value by 1 when a first position normalization value of any one of the first occupied lattices and a second position normalization value of a lattice corresponding to any one of the first occupied lattices in the second lattice planar map satisfy a predetermined identical condition; After the traversal is completed, calculating a quotient of the second calculated parameter value and the first calculated parameter value to obtain a first similarity matching result.

[0027] Optionally, the processor is configured to perform grid similarity matching based on a first position normalized value of each first occupied grid in the first grid plane map and a second position normalized value of each second occupied grid in the second grid plane map to obtain a similarity matching result, When any one second occupied grid in the second grid planar map is traversed, a third calculation parameter value is increased by 1; Incrementing a fourth calculation parameter value by 1 when a second position normalization value of any one of the second occupied lattices and a first position normalization value of a lattice corresponding to any one of the second occupied lattices in the first lattice planar map satisfy a predetermined identical condition; After the traversal is completed, calculating a quotient of the fourth calculated parameter value and the third calculated parameter value to obtain a second similarity matching result.

[0028] Optionally, the processor is configured to obtain a first position normalization value corresponding to a first occupied grid based on the at least one first projected position in each first occupied grid of the first grid planar map, and to obtain a second position normalization value corresponding to a second grid based on the at least one second projected position in each second grid of the second grid planar map, Identifying a first position normalization value corresponding to a first occupied grid as 1 based on the at least one first projected position in each first occupied grid of the first grid planar map, wherein each grid in the first grid planar map has an initial position normalization value of zero before similarity matching is performed; and determining a second position normalization value corresponding to the second grid as 1 based on the at least one first projection position in each second grid of the second grid plane map, wherein each grid in the second grid plane map has an initial position normalization value of zero before similarity matching is performed.

[0029] Optionally, the processor is configured to determine whether a position change has occurred in the automated cleaning device based on the similarity matching results, The method includes determining that no position change has occurred in the automatic cleaning device when the first similarity matching result and / or the second similarity matching result is equal to or greater than a predetermined occupancy ratio threshold.

[0030] Optionally, the processor is further configured to determine whether a position change has occurred in the automated cleaning device based on the similarity matching results, The method includes determining that a position change has occurred in the automatic cleaning device when the first similarity matching result and / or the second similarity matching result is smaller than a predetermined occupancy ratio threshold.

[0031] An embodiment of the present invention provides an automatic cleaning device, the automatic cleaning device comprising a processor and a memory, the memory storing computer program instructions executable by the processor, the processor, when executing the computer program instructions, performing the method steps of any one of the above methods.

[0032] An embodiment of the present invention provides a non-transitory computer-readable storage medium having computer program instructions stored thereon, which, when called and executed by a processor, perform the method steps of any one of the above-described methods.

[0033] The above solution of the embodiment of the present invention has at least the following advantageous effects. The present invention provides a slippage detection method, device, and storage medium, which acquires measured distance values ​​and a plurality of frame point clouds within a predetermined detection time period while an automatic cleaning device is running, and can quickly and accurately identify whether the automatic cleaning device is in a slippage state based on the measured distance values ​​and the plurality of frame point clouds, thereby improving the system's sensing efficiency and detection sensitivity and enhancing the user experience. [Brief explanation of the drawings]

[0034] [Figure 1] 1 shows a schematic diagram of the overall structure of an automatic cleaning device according to some embodiments of the present invention. [Figure 2] 1 shows a schematic bottom view of an automatic cleaning device according to some embodiments of the present invention. [Figure 3] 2 shows a flowchart of a slippage detection method according to an embodiment of the present invention. [Figure 4] 1 shows a schematic diagram of a grid planar map according to an embodiment of the present invention. [Figure 5] 1 shows a block diagram of an automated cleaning device implementation of some embodiments. [Figure 6] 1 shows a structural schematic diagram of an automatic cleaning device according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in more detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without any creative work are all included in the protection scope of the present invention.

[0036] The terms used in the embodiments of the present invention are used only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the embodiments of the present invention and the appended claims, the singular forms "a," "the," and "the" are also intended to encompass the plural, and "plurality" generally includes at least two, unless the context clearly indicates otherwise.

[0037] The term "and / or" used in this specification merely describes the relationship between related objects, and there are three relationships. For example, A and / or B means that A may exist alone, A and B may exist simultaneously, or B may exist alone. In addition, " / " in this specification generally indicates that the related objects before and after it are in an "or" relationship.

[0038] In addition, in the embodiments of the present invention, terms such as "first," "second," and "third" may be used for explanatory purposes, but it should be understood that the present invention should not be limited to these terms. These terms are used only for distinction. For example, a "first" may also be called a "second," and similarly, a "second" may also be called a "first," without departing from the scope of the embodiments of the present invention.

[0039] Depending on the context, for example, the words "if" and "and" used herein may be interpreted as "when" or "when," or "depending on particular circumstances," or "depending on the detection of." Similarly, depending on the context, the phrase "when identified" or "when (a described condition or event) is detected" may be interpreted as "when identified" or "depending on being identified," or "when (a described condition or event) is detected" or "depending on the detection of (a described condition or event)."

[0040] It should be noted that the terms "comprises," "has," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product or device comprising a set of elements not only includes those elements, but also other elements explicitly listed or inherent in those products or devices. Unless further limited, an element defined with the phrase "comprises" does not exclude the presence of other identical elements in a product or device that includes said element.

[0041] It should be particularly noted that symbols and / or numbers present in the specification are not symbols if they are not marked in the description of the drawings.

[0042] In the following, alternative embodiments of the present invention will be described in detail with reference to the drawings.

[0043] As shown in Figures 1 and 2, the automatic cleaning equipment may be a vacuum cleaning robot, a suction cleaning / floor mopping / brushing / suction cleaning and floor mopping robot, etc., and the automatic cleaning equipment may include a moving platform 100, a sensing system 120, a processor 130, a driving system 140, a cleaning module, an energy system 160 and a man-machine interactive system 170.

[0044] The mobile platform 100 may be configured to automatically move in a target direction on an operating surface. The operating surface may be a surface to be cleaned by an automatic cleaning device. In some embodiments, the automatic cleaning device may be a self-propelled robot, such as a floor mopping robot, a vacuum cleaning robot, or a vacuum and floor mopping robot, where the automatic cleaning device operates on a floor surface, and the floor surface may be any operating surface, such as a desktop, a roof, or a platform. The automatic cleaning device may be a window cleaning robot, where the automatic cleaning device operates on the glass exterior of a building, and the glass is the operating surface. The automatic cleaning device may be a pipe self-propelled robot, where the automatic cleaning device operates on the inner surface of a pipe, and the inner surface of the pipe is the operating surface. For purely illustrative purposes, the present invention will be described using a floor mopping robot as an example.

[0045] In some embodiments, the mobile platform 100 may be an autonomous mobile platform 100 or a non-autonomous mobile platform 100. The term "autonomous mobile platform 100" refers to a mobile platform 100 that can automatically and adaptively make operational decisions in response to unexpected environmental inputs, while the non-autonomous mobile platform 100 cannot adaptively make operational decisions in response to unexpected environmental inputs, but can operate according to a predetermined procedure or logic. Correspondingly, if the mobile platform 100 is an autonomous mobile platform 100, the target direction may be determined autonomously by an automatic cleaning device, while if the mobile platform 100 is a non-autonomous mobile platform 100, the target direction may be set by a system or manually. If the mobile platform 100 is an autonomous mobile platform 100, the mobile platform 100 includes a front portion 111 and a rear portion 110.

[0046] The sensing system 120 includes a pose identification device 121 on the mobile platform 100, a buffer 122 located in the front portion 111 of the mobile platform 100, a cliff sensor 123 located at the bottom of the mobile platform 100, a light detection assembly 180, a color detection assembly 190, and sensing devices such as magnetometers, accelerometers, gyros, and odometry 131, and provides various position information and movement status information of the equipment to the processor 130.

[0047] To more clearly describe the behavior of the automatic cleaning equipment, the following directions are defined. That is, the automatic cleaning equipment can move across a floor surface by various combinations of movement along three mutually perpendicular axes defined by the mobile platform 100: the lateral axis x, the fore-aft axis y, and the central vertical axis z. The forward drive direction along the fore-aft axis y is designated "forward," and the rearward drive direction along the fore-aft axis y is designated "rearward." The lateral axis x is substantially defined by the center point of the drive wheel assembly 141 of the drive system 140, and its axis center extends between the right and left wheels of the automatic cleaning equipment. Here, the automatic cleaning equipment can rotate about the x-axis. When the front portion 111 of the automatic cleaning equipment tilts upward and the rear portion 110 tilts downward, it is referred to as "pitch up," and when the front portion 111 of the automatic cleaning equipment tilts downward and the rear portion 110 tilts upward, it is referred to as "pitch down." Additionally, the automatic cleaning equipment can rotate about the z-axis. In front of the automatic cleaning equipment, if the automatic cleaning equipment is tilted to the right of the y-axis, it is considered a "right turn," and if the automatic cleaning equipment is tilted to the left of the y-axis, it is considered a "left turn."

[0048] 2, cliff sensors 123 are provided at the bottom of the mobile platform 100, in front and behind the drive wheel assembly 141, to prevent the automatic cleaning equipment from falling when moving forward or backward, thereby preventing damage to the automatic cleaning equipment. The "front" refers to the side in the same direction as the automatic cleaning equipment's travel, and the "rear" refers to the side opposite to the direction of travel of the automatic cleaning equipment.

[0049] The pose identification device 121 includes, but is not limited to, a device capable of identifying the position or posture of the automatic cleaning equipment, such as a camera head, a laser radar 132 (LDS), a line structure light device, an ODO sensor, etc. The pose identification device 121 is not limited to being located on top of the automatic cleaning equipment. For example, the camera head and line structure light device may be located anywhere on the front or side of the automatic cleaning equipment as needed, and the ODO sensor may be located inside the automatic cleaning equipment.

[0050] Each assembly in the sensing system 120 may operate independently or may cooperate to more precisely achieve its purpose or function. Recognizing the surface to be cleaned through signals received from the light detection assembly 180 and the color detection assembly 190 identifies the physical characteristics of the surface to be cleaned, including the surface's material type, color level, etc. This identifies the operating mode of the automatic cleaning device.

[0051] A buffer 122 is provided on the front portion 111 of the mobile platform 100, and when the drive wheel assembly 141 propels the automatic cleaning equipment to travel on the floor surface during the cleaning process, the buffer 122 detects one or more events (or objects) in the travel path of the automatic cleaning equipment via a sensor system, such as a light detection assembly 180 and a color detection assembly 190, and the automatic cleaning equipment may control the drive wheel assembly 141 to respond to the event (or object), for example, through an obstacle or wall, detected by the buffer 122, so that the automatic cleaning equipment moves away from the obstacle.

[0052] The processor 130 is provided on a circuit board within the mobile platform 100 and includes an arithmetic processor such as a central processing unit (CPU) or an application processor that communicates with a non-transitory memory such as a hard disk, flash memory, or random access memory. The application processor receives environmental information sensed by the multiple sensors from the sensing system 120 and obstacle information fed back from the laser radar 132, uses a positioning algorithm, such as SLAM, to draw an instant map of the environment in which the automatic cleaning device is installed, autonomously determines a travel path based on the environmental information and the environmental map, and then controls operations such as forward movement, backward movement, and / or steering of the drive system 140 according to the autonomously determined travel path. Furthermore, the processor 130 can determine whether to activate a cleaning module to perform a cleaning operation based on the environmental information and the environmental map.

[0053] Specifically, the processor 130 combines distance and speed information fed back from sensing devices such as the buffer 122, cliff sensor 123, light detection assembly 180, color detection assembly 190, magnetometer, accelerometer, gyroscope, and odometry 131 to comprehensively determine the current operating status of the vacuum cleaner, such as whether it has crossed a threshold, climbed onto a carpet, been positioned on a cliff, been caught on the top or bottom, the dustbin is full, or been lifted, and provides specific next operating strategies according to different situations, allowing the operation of the automatic cleaning device to better meet the owner's requirements and provide a better user experience. Furthermore, the processor 130 can plan the most efficient and rational cleaning path and cleaning method according to the instant map information drawn by SLAM, thereby significantly improving the cleaning efficiency of the automatic cleaning device.

[0054] The drive system 140 executes drive commands based on specific distance and angle information, such as x, y, and θ components, to steer the automatic cleaning equipment across a floor surface. The drive system 140 includes a drive wheel assembly 141, which can simultaneously control the left and right wheels. To enable the automatic cleaning equipment to move more stably across a floor surface or to have higher mobility, the automatic cleaning equipment may include one or more steering assemblies 142, which may be driven wheels or drive wheels, and whose structural form may be a universal wheel, and the steering assembly 142 may be located in front of the drive wheel assembly 141.

[0055] The energy system 160 includes a rechargeable battery such as a nickel-metal hydride battery or a lithium battery. The rechargeable battery is connected to a charge control circuit, a battery pack charging temperature detection circuit, and a battery voltage drop monitoring circuit, which are connected to a microcomputer control circuit. The automatic cleaning device is connected to a charging pile for charging via charging electrodes provided on the side or bottom of the device.

[0056] The man-machine interactive system 170 includes keys on the automatic cleaning appliance panel that the user can use to select functions, and may further include a display screen and / or indicator lights and / or a speaker, which can display the current status or function options of the automatic cleaning appliance to the user. The man-machine interactive system 170 may further include a mobile phone client program. In the case of a route-navigation type automatic cleaning appliance, the mobile phone client can display a map of the environment in which the automatic cleaning appliance is located and the location of the automatic cleaning appliance to the user, providing the user with a richer and more user-friendly function selection. The user may also set cleaning parameters for the automatic cleaning appliance via the mobile phone client.

[0057] The cleaning module may include a dry cleaning module 151 and / or a wet cleaning module 400. As shown in FIG. 2, the dry cleaning module 151 includes a roller brush, a dust box, a fan, and an air outlet. The roller brush, which is in contact with the floor to some extent, sweeps dust from the floor toward the dust suction port between the roller brush and the dust box, and then sucks the dust into the dust box via suction gas generated by the fan. The dust removal capacity of an automatic cleaning device is characterized by its dust pickup efficiency (DPU). DPU is affected by the structure and material of the roller brush, the wind power utilization rate of the duct consisting of the dust suction port, dust box, fan, air outlet, and their connecting components, and the type and power of the fan, making it a complex system design issue. Compared to a typical plug-in dust collector, improved dust removal capacity is significant for energy-constrained automatic cleaning devices. This is because improved dust removal capacity directly and effectively reduces the amount of energy required, allowing a machine that can clean 80 square meters of floor space on a single charge to be able to clean more than 180 square meters on a single charge. Reducing the number of charges also significantly extends battery life, allowing users to replace batteries less frequently. More intuitively and importantly, improved dust removal capacity provides the most obvious and meaningful user experience, allowing users to directly determine whether the machine cleans or wipes clean. The dry cleaning module 151 may further include a side brush 152 on its rotating shaft, which is angled relative to the floor surface to move dirt to the roller brush area of ​​the dry cleaning module 151.

[0058] The wet cleaning module 400 of this embodiment is configured to clean at least a portion of the operating surface using a wet cleaning method. The wet cleaning module 400 includes a cleaning head 410 and a driving device. The cleaning head 410 cleans at least a portion of the operating surface, and the driving device drives the cleaning head 410 to reciprocate along the surface to be cleaned, which is a portion of the operating surface. The cleaning head 410 reciprocates along the surface to be cleaned, and a cleaning cloth or cleaning plate is provided on the contact surface with the surface to be cleaned. The reciprocating motion generates high-frequency friction between the cleaning head 410 and the surface to be cleaned, thereby removing dirt from the surface to be cleaned. The cleaning head 410 includes a movable region 412 and a fixed region 411, and the movable region 412 is located approximately at the center of the cleaning head 410.

[0059] Currently, the Slam algorithm is mainly used to determine the sliding state of the cleaning robot, and then the cleaning robot is controlled to release the trapped state by rotating or retreating on the spot.

[0060] However, the SLA algorithm requires high accuracy in positioning coordinates, obtains positioning by matching with an electronic map, and then determines whether the cleaning robot is in a slipping state based on the change in position. Matching with an electronic map takes time, so the processing time is long, requiring more than 10 seconds, and in extreme cases, more than 20 seconds, which is beyond the time that users are willing to accept.

[0061] Therefore, an embodiment of a slippage detection method will be provided as an embodiment of the present invention.

[0062] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0063] As shown in Figure 3 and Figure 4, the present invention provides a slippage detection method, which includes the following steps: In step S101, while the automatic cleaning device is traveling, the odometry 131 acquires a measured distance value and the laser radar 132 acquires a plurality of frame point clouds within a predetermined detection time period.

[0064] Here, the measured distance value represents the rotation distance value of the drive wheel assembly.

[0065] When there is no obstacle, the distance measured by the odometry 131 is equal to the distance traveled by the automatic cleaning device. However, when the automatic cleaning device slips, the distance measured by the odometry 131 changes, but the distance traveled by the automatic cleaning device does not change.

[0066] The laser radar 132 is configured to sense and measure distance outside the automatic cleaning equipment using laser light.

[0067] A point cloud (the full term in English is point cloud data) refers to a large collection of point data representing target surface characteristics in a two-dimensional coordinate system, where each point data contains a group of vectors.

[0068] When the laser radar 132 is irradiated onto the surface of an object, the reflected laser light carries information such as direction and distance. When the laser beam scans along its trajectory, it records reflected laser light point information (point data) as it scans. Because the scanning is extremely delicate, it is possible to acquire a large number of laser light points, which can then form a point cloud. Each point data includes the two-dimensional position of the laser light reflection point.

[0069] The frame point cloud refers to a point cloud obtained after the laser radar 132 scans the surrounding environment once. The frame point cloud includes a plurality of point data.

[0070] In the present invention, measured distance values ​​and multiple frame point groups are obtained within a predetermined detection time period, with the purpose of analyzing a certain amount of data, making it possible to clearly recognize differences between the data before and after, thereby improving the accuracy of detection.

[0071] In step S102, the automatic cleaning device is identified as being in a slipping state based on the measured distance values ​​and the plurality of frame point clouds.

[0072] In an embodiment of the present invention, while the automatic cleaning device is running, the measured distance values ​​and a plurality of frame point clouds are acquired within a predetermined detection time period, and the measured distance values ​​and the plurality of frame point clouds can be used to quickly and accurately identify whether the automatic cleaning device is in a slipping state, thereby improving the system's sensing efficiency and detection sensitivity and improving the user experience.

[0073] In some specific embodiments, identifying the automatic cleaning device as being in a slipping state based on the distance measurements and the plurality of frame point clouds includes the following steps: In step S102a-1, if the measured distance value of the automatic cleaning device is greater than a predetermined measured distance threshold, it is determined whether a position change has occurred in the automatic cleaning device based on the plurality of frame point clouds.

[0074] If the measured distance value is too small, it means that the drive wheel assembly is not operating. That is, the automatic cleaning device is in an active stop state or is in the process of entering an active stop state. Since this situation is not a slip state, the present invention recognizes the state of the automatic cleaning device that generates the measured distance value using a predetermined measured distance threshold and excludes this situation from the range of slip state detection. Unless the measured distance value of the automatic cleaning device is greater than the predetermined measured distance threshold, it is not detected whether a position change has occurred in the automatic cleaning device. This reduces the number of times a slip state is detected and improves the system's detection efficiency.

[0075] In some specific embodiments, each frame point cloud includes multiple point data.

[0076] Accordingly, determining whether a position change has occurred in the automatic cleaning device based on the plurality of frame point clouds includes the following steps: In step S102a-1-1, the plurality of frame point clouds are assigned to a first point cloud set or a second point cloud set, respectively.

[0077] The plurality of frame point clouds includes a plurality of first frame point clouds 31 and a plurality of second frame point clouds 32.

[0078] Here, the first point cloud set includes a plurality of first frame point clouds 31, and the second point cloud set includes a plurality of second frame point clouds 32.

[0079] The frame point clouds acquired within a predetermined detection time period may be assigned without considering the order. This does not affect whether the automatic cleaning device is in a slipping state. The present invention does not limit the number of the first frame point clouds 31 and the second frame point clouds 32. The total number of the first frame point clouds 31 and the second frame point clouds 32 may be equal to or less than the number of the second frame point clouds 32.

[0080] In some specific embodiments, respectively assigning the plurality of frame point clouds to a first point cloud set or a second point cloud set includes the following steps: In step S102a-1-1-1, a predetermined detection time period is divided into a first detection time period and a second detection time period. In step S102a-1-1-2, a plurality of frame point clouds within the first detection time period are respectively assigned to a first point cloud set, and a plurality of frame point clouds within the second detection time period are respectively assigned to a second point cloud set.

[0081] In this specific embodiment, a predetermined detection time period is divided into a first detection time period and a second detection time period, which are sequentially arranged in time order. Then, multiple frame point clouds acquired within the predetermined detection time period are assigned to a first point cloud set or a second point cloud set according to their acquisition times. The first point cloud set includes frame point clouds acquired during the first detection time period (i.e., multiple first frame point clouds 31), and the second point cloud set includes frame point clouds acquired during the second detection time period (i.e., multiple second frame point clouds 32).

[0082] In this specific implementation, the frame point clouds acquired within the previous and next time periods can clearly reflect the positional difference of the automatic cleaning device between different time periods. It is easy to detect whether a displacement has occurred in the automatic cleaning device based on the positional difference between the previous and next time periods. For example, based on the first and second detection time periods that occur in chronological order, only the first frame point cloud acquired within the first detection time period is assigned to a first point cloud set, and the last frame point cloud acquired within the second detection time period is assigned to a second point cloud set. During the normal operation of the automatic cleaning device, the first and last frame point clouds can clearly reflect the maximum positional difference of the automatic cleaning device. That is, within a predetermined detection time period, the first and last frame point clouds can be used to obtain the maximum displacement that matches the measured distance value. If the maximum displacement that matches the measured distance value cannot be obtained, the automatic cleaning device is operating abnormally within the predetermined detection time period.

[0083] In step S102a-1-2, similarity matching is performed based on the plurality of first frame point groups and the plurality of second frame point groups to obtain a similarity matching result.

[0084] In some specific embodiments, performing similarity matching based on the plurality of first frame point clouds and the plurality of second frame point clouds to obtain a similarity matching result includes the following steps: In step S102a-1-2-1, the two-dimensional positions of each point data in each first frame point group are projected onto a predetermined plane to obtain a first planar map, and the two-dimensional positions of each point data in each second frame point group are projected onto a predetermined plane to obtain a second planar map. Here, the first planar map includes a plurality of first projection positions, and the second planar map includes a plurality of second projection positions. In step S102a-1-2-2, similarity matching is performed based on a plurality of first projected positions in the first planar map and a plurality of second projected positions in the second planar map, and a similarity matching result is obtained.

[0085] In this specific embodiment, the two-dimensional position of each point data in each first frame point cloud is converted to a two-dimensional position on a predetermined plane to generate a first planar map. The two-dimensional position of each point data in each second frame point cloud is converted to a two-dimensional position on the same predetermined plane to generate a second planar map. Matching the first planar map and the second planar map on the same predetermined plane reduces the amount of calculation, reduces the complexity of matching, and improves matching efficiency.

[0086] In some specific embodiments, the pre-formed plane includes a pre-formed grating plane.

[0087] The first planar map includes a first grid planar map 33, and includes a plurality of first occupied grids 331 in a plurality of grids of the first grid planar map 33, and each first occupied grid 331 includes at least one first projection position.

[0088] The second planar map includes a second grid planar map 34, and includes a plurality of second occupied grids 341 in a plurality of grids of the second grid planar map 34, and each second occupied grid 341 includes at least one second projection position.

[0089] The two-dimensional positions of each point data in each first frame point group 31 are projected onto a pre-established grid plane to obtain a first grid plane map 33, and the two-dimensional positions of each point data in each second frame point group 32 are projected onto a pre-established grid plane to obtain a second grid plane map 34.

[0090] The pre-established grid plane is divided into a plurality of grids arranged in an array. The two-dimensional position of each point data in each first frame point cloud 31 of the first point cloud set is projected onto the pre-established grid plane to obtain a first grid plane map 33, and the two-dimensional position of each point data in each second frame point cloud 32 of the second point cloud set is projected onto the pre-established grid plane to obtain a second grid plane map 34.

[0091] As shown in FIG. 4, the first grid planar map 33 includes a plurality of grids arranged in an array, and the second grid planar map 34 also includes a plurality of grids arranged in an array.

[0092] Here, the first grid planar map 33 includes a plurality of first occupied grids 331 in a plurality of grids, each of which includes at least one first projection position, and the second grid planar map 34 includes a plurality of second occupied grids 341 in a plurality of grids, each of which includes at least one second projection position.

[0093] As can be understood, when any one of the grids of the first grid planar map 33 includes at least one first projected position, the one of the grids is referred to as a first occupied grid 331, and when any one of the grids of the first grid planar map 33 does not include any first projected position, the one of the grids is referred to as a first unoccupied grid. When any one of the grids of the second grid planar map 34 includes at least one second projected position, the one of the grids is referred to as a second occupied grid 341, and when any one of the grids of the second grid planar map 34 does not include any second projected position, the one of the grids is referred to as a second unoccupied grid.

[0094] Each first projection position has a one-to-one correspondence relationship with the two-dimensional position of one point in the plurality of first frame point groups 31. Each second projection position has a one-to-one correspondence relationship with the two-dimensional position of one point in the plurality of second frame point groups 32.

[0095] In this specific embodiment, the two-dimensional positions of point data in the frame point cloud are converted into two-dimensional positions in a pre-defined grid plane, thereby reducing the amount of calculation, reducing the complexity of matching, and improving the efficiency of matching.

[0096] Accordingly, performing similarity matching based on a plurality of first projection positions in the first planar map and a plurality of second projection positions in the second planar map and obtaining a similarity matching result includes the following steps: In step S102a-1-2-2a, grid similarity matching is performed based on the at least one first projection position in each first occupied grid 331 of the first grid planar map 33 and the at least one second projection position in each second occupied grid 341 of the second grid planar map 34, and a similarity matching result is obtained.

[0097] Since both the first grid planar map 33 and the second grid planar map 34 are generated on a predetermined grid plane, each grid in the first grid planar map 33 has a corresponding grid in the second grid planar map 34. In this specific embodiment, a grid is used as the unit of similarity matching, and the grids in the first grid planar map 33 and the second grid planar map 34 are compared one by one to finally obtain a similarity matching result. The similarity between the first grid planar map 33 and the second grid planar map 34 is evaluated based on the similarity matching result.

[0098] In some specific embodiments, performing grid similarity matching based on the at least one first projected position in each first occupied grid 331 of the first grid planar map 33 and the at least one second projected position in each second occupied grid 341 of the second grid planar map 34 to obtain a similarity matching result includes the following steps:

[0099] In step S102a-1-2-2a1, a first position normalization value corresponding to the first occupied lattice 331 is obtained based on the at least one first projected position in each first occupied lattice 331 of the first grid planar map 33, and a second position normalization value corresponding to the second lattice is obtained based on the at least one second projected position in each second lattice of the second grid planar map 34.

[0100] The position normalization value is a value that uniquely represents the position characteristics of all projection positions in the occupied lattice. For example, the position normalization value includes the average distribution position of all projection positions in the occupied lattice. If there are three projection positions (x1, y1), (x2, y2), and (x3, y3) in one occupied lattice, the average distribution position of the occupied lattice is (xn, yn). Here, xn = (x1 + x2 + x3) / 3, yn = (y1 + y2 + y3) / 3. Using the average distribution position as the position normalization value can improve the accuracy of lattice similarity matching and enhance detection precision.

[0101] The first position normalized value and the second position normalized value both belong to the position normalized value.

[0102] In some other specific embodiments, obtaining a first position normalization value corresponding to a first occupied grid 331 based on the at least one first projected position in each first occupied grid 331 of the first grid planar map 33, and obtaining a second position normalization value corresponding to a second grid based on the at least one second projected position in each second grid of the second grid planar map 34 includes the following steps:

[0103] In step S102a-1-2-2a-1a, a first position normalization value corresponding to a first occupied lattice 331 is determined as 1 based on the at least one first projected position in each first occupied lattice 331 of the first grid planar map 33, where the initial position normalization value before similarity matching of each lattice in the first grid planar map 33 is zero, and a second position normalization value corresponding to a second lattice is determined as 1 based on the at least one first projected position in each second lattice of the second grid planar map 34, where the initial position normalization value before similarity matching of each lattice in the second grid planar map 34 is zero.

[0104] In this specific embodiment, the position normalization values ​​of all occupied lattices are set to 1, while the position normalization values ​​of all unoccupied lattices are set to 0, thereby simplifying the process of calculating the average distribution position and improving the efficiency of detecting the slip state.

[0105] In step S102a-1-2-2a-2, grid similarity matching is performed based on the first position normalized value of each first occupied grid 331 in the first grid planar map 33 and the second position normalized value of each second occupied grid 341 in the second grid planar map 34, and a similarity matching result is obtained.

[0106] In this specific embodiment, the position normalization value uniquely represents the position features of all projected positions in the occupied grid, thereby reducing the complexity of projected position matching, reducing the amount of calculation, and improving the efficiency of matching.

[0107] In some specific embodiments, performing grid similarity matching based on the first position normalized value of each first occupied grid 331 in the first grid planar map 33 and the second position normalized value of each second occupied grid 341 in the second grid planar map 34 to obtain a similarity matching result includes the following steps: In step S102a-1-2-2a-2a-1, when any one of the first occupied grids 331 in the first grid planar map 33 is traversed, the first calculation parameter value is incremented by one.

[0108] The first calculated parameter value is initially set to zero before the traverse.

[0109] For example, as shown in Figure 4, the first grid planar map 33 includes a total of 25 grids, arranged 5x5. Among them, the 10 shaded grids are first occupied grids 331. When traversing the first occupied grids 331 in the first grid planar map 33, the first calculated parameter value is incremented by 1 each time a first occupied grid 331 is accessed. After the traversal, the first calculated parameter value becomes 10.

[0110] In step S102a-1-2-2a-2a-2, if the first position normalization value of any one of the first occupied lattices 331 and the second position normalization value of the lattice corresponding to any one of the first occupied lattices 331 in the second lattice planar map 34 satisfy a predetermined identical condition, the second calculation parameter value is incremented by 1.

[0111] The second calculated parameter value is initially set to zero before the traverse.

[0112] The lattice corresponding to any one of the first occupied lattices 331 in the second grid planar map 34 refers to a lattice at the same position in a pre-established grid plane. For example, as shown in Fig. 4, in the first grid planar map 33, the lattice at the first arrangement position is the first occupied lattice 331, and the lattice corresponding to the first occupied lattice 331 is the lattice at the first arrangement position (occupied lattice) in the second grid planar map 34, and the lattice at the third arrangement position in the first grid planar map 33 is the first occupied lattice 331, and the lattice corresponding to the first occupied lattice 331 is the lattice at the third arrangement position (unoccupied lattice) in the second grid planar map 34. This can be inferred from the above.

[0113] The predetermined identical condition means that the absolute value of the difference or distance between the first position normalization value and the second position normalization value is equal to or less than a predetermined difference threshold. For example, when the first position normalization values ​​of all first occupied lattices 331 and the second position normalization values ​​of all second occupied lattices 341 are both 1, the predetermined identical condition means that the absolute value of the difference between the first position normalization value and the second position normalization value is equal to zero. When the first position normalization value and the second position normalization value of two lattices at the same arrangement position in the first grid planar map 33 and the second grid planar map 34 are both 1, it means that the two lattices are the same. When the first position normalized values ​​of all the first occupied lattices 331 and the second position normalized values ​​of all the second occupied lattices 341 are both represented by average distribution positions, the predetermined identical condition means that the distance between the average distribution positions in two lattices at the same arrangement positions in the first lattice planar map 33 and the second lattice planar map 34 is less than a predetermined difference threshold, meaning that the two lattices are the same.

[0114] When two grids at the same array position in the first grid planar map 33 and the second grid planar map 34 are the same, the second calculation parameter value is incremented by 1. For example, as shown in FIG. 4, the second grid planar map 34 includes a total of 25 grids, which are 5×5, corresponding to the first grid planar map 33. Among them, there are seven second occupied grids 341. By comparison, it is found that the position normalization values ​​of the grids at the first array position, the fifth array position, the tenth array position, the fifteenth array position, and the twenty-first array position in the first grid planar map 33 and the second grid planar map 34 satisfy the predetermined identical condition, so the second calculation parameter value is 5.

[0115] In step S102a-1-2-2a-2a-3, after the traverse is completed, the quotient of the second calculated parameter value and the first calculated parameter value is calculated to obtain a first similarity matching result. For example, continuing from the above example, after the traverse is completed, if the second calculated parameter value is 5 and the first calculated parameter value is 10, the first similarity matching result is 5 / 10=50%.

[0116] In some other specific embodiments, performing grid similarity matching based on the first position normalized value of each first occupied grid 331 in the first grid planar map 33 and the second position normalized value of each second occupied grid 341 in the second grid planar map 34 to obtain a similarity matching result includes the following steps:

[0117] In step S102a-1-2-2a-2b-1, when any one of the second occupied grids 341 in the second grid planar map 34 is traversed, the third calculation parameter value is incremented by one.

[0118] The third calculated parameter value is initially set to zero before the traverse.

[0119] For example, as shown in Figure 4, the second grid planar map 34 includes a total of 25 grids, which are 5x5. Among them, there are seven second occupied grids 341. When traversing the second occupied grids 341 in the second grid planar map 34, the third calculated parameter value is incremented by 1 each time a second occupied grid 341 is accessed. After the traversal, the third calculated parameter value becomes 7.

[0120] In step S102a-1-2-2a-2b-2, if the second position normalization value of any one of the second occupied lattices 341 and the first position normalization value of the lattice corresponding to any one of the second occupied lattices 341 in the first lattice planar map 33 satisfy a predetermined identical condition, the fourth calculation parameter value is incremented by 1.

[0121] The fourth calculated parameter value is initially set to zero before the traverse.

[0122] In the first grid plane map 33, a grid corresponding to any one of the second occupied grids 341 refers to a grid at the same position in a pre-established grid plane.

[0123] When two grids at the same array position on the first grid planar map 33 and the second grid planar map 34 are the same, the fourth calculation parameter value is incremented by 1. For example, as shown in Fig. 4, a comparison shows that the position normalized values ​​of the grids at the first array position, the fifth array position, the tenth array position, the fifteenth array position, and the twenty-first array position on the first grid planar map 33 and the second grid planar map 34 satisfy the predetermined identical condition, and therefore the fourth calculation parameter value is 5.

[0124] In step S102a-1-2-2a-2b-3, after the traverse is completed, the quotient of the fourth calculated parameter value and the third calculated parameter value is calculated to obtain a second similarity matching result.

[0125] For example, continuing from the above example, after the traverse is completed, if the fourth calculated parameter value is 5 and the third calculated parameter value is 7, the second similarity matching result=5 / 7=71.4%.

[0126] In step S102a-1-3, it is determined whether or not a position change has occurred in the automatic cleaning device based on the similarity matching result.

[0127] In some specific embodiments, determining whether a position change has occurred in the automatic cleaning device based on the similarity matching result includes the following steps:

[0128] In step S102a-1-3a, if the first similarity matching result and / or the second similarity matching result is equal to or greater than a predetermined occupancy ratio threshold, it is determined that no position change has occurred in the automatic cleaning device.

[0129] For example, continuing from the above example, the predetermined occupancy ratio threshold is 50%, and the first similarity matching result is 50%, which is equal to the predetermined occupancy ratio threshold, so it is determined that no position change has occurred in the automatic cleaning device.

[0130] For example, continuing from the above example, the predetermined occupancy ratio threshold is 50%, while the second similarity matching result is 71.4%, which is greater than the predetermined occupancy ratio threshold, and therefore it is determined that no position change has occurred in the automatic cleaning device.

[0131] For example, continuing from the above example, the predetermined occupancy ratio threshold is 50%, and the first similarity matching result is 50%, which is equal to the predetermined occupancy ratio threshold, and the second similarity matching result is 71.4%, and since both the first similarity matching result and the second similarity matching result are above the predetermined occupancy ratio threshold, it is determined that no position change has occurred in the automatic cleaning equipment.

[0132] When the first similarity matching or the second similarity matching is equal to or greater than a predetermined occupancy ratio threshold, it means that there is a possibility that the first grid planar map 33 or the second grid planar map 34 is similar. However, the traverse mainly accesses the first occupied lattices 331 in the first grid planar map 33 and ignores the existence of the second occupied lattices 341 in the second grid planar map 34. To ensure the accuracy of the similarity matching result, this specific embodiment provides a system that mainly accesses the second occupied lattices 341 in the second grid planar map 34. When both the first similarity matching and the second similarity matching are equal to or greater than a predetermined occupancy ratio threshold, it is determined that no position change has occurred in the automatic cleaning device. This ensures the accuracy of the matching.

[0133] In some other specific embodiments, determining whether a position change has occurred in the automatic cleaning device based on the similarity matching result includes the following steps:

[0134] In step S102a-1-3b, when the first similarity matching result and / or the second similarity matching result is smaller than a predetermined occupancy ratio threshold, it is determined that a position change has occurred in the automatic cleaning device.

[0135] For example, if the predetermined occupancy ratio threshold is 50% and the first similarity matching result is 45%, it is determined that a position change has occurred in the automatic cleaning device because it is smaller than the predetermined occupancy ratio threshold of 50%.

[0136] For example, if the predetermined occupancy ratio threshold is 50% and the second similarity matching result is 48%, it is determined that a position change has occurred in the automatic cleaning device because it is smaller than the predetermined occupancy ratio threshold of 50%.

[0137] For example, if a predetermined occupancy ratio threshold is 50%, and the first similarity matching result is 45% and the second similarity matching result is 48%, it is determined that a position change has occurred in the automatic cleaning device because both the first similarity matching result and the second similarity matching result are smaller than the predetermined occupancy ratio threshold of 50%.

[0138] In this application, point data of multiple frame point clouds are assigned to a first point cloud set or a second point cloud set, respectively. The two-dimensional positions of each point data in the first point cloud set and the second point cloud set are then projected onto a predetermined grid plane to generate a first grid plane map 33 and a second grid plane map 34, respectively. Based on the similarity matching results between the first grid plane map 33 and the second grid plane map 34, it is determined whether a position change has occurred in the automatic cleaning device and whether the automatic cleaning device is in a sliding state. This avoids the need for complex map matching and positioning processes for 2D electronic maps. In the grid similarity matching process, the two-dimensional positions of the point data in the frame point clouds are converted into two-dimensional positions in an occupied grid on the predetermined grid plane using gradient descent, and the two-dimensional positions in the occupied grid are converted into normalized position values ​​of the occupied grid. This reduces the amount of data processing and the complexity of matching, and improves matching efficiency.

[0139] In step S102a-2, when it is determined that no position change has occurred in the automatic cleaning device, it is determined that the automatic cleaning device is in a slipping state.

[0140] In some other implementations, identifying the automatic cleaning device as being in a slipping state based on the distance measurements and the plurality of frame point clouds includes the following steps. In step S102b-1, it is determined whether a position change has occurred in the automatic cleaning device based on the plurality of frame point clouds. In step S102b-2, when it is determined based on the plurality of frame point clouds that no position change has occurred in the automatic cleaning device, it is determined whether the measured distance value is greater than a predetermined measured distance threshold value. In step S102b-3, if the measured distance value is greater than a predetermined measured distance threshold, it is determined that the automatic cleaning device is in a slip state.

[0141] The specific implementation methods of the above steps in this specific embodiment are the same as those in the above embodiment, and therefore, they will not be repeated again, and please refer to the above embodiment.

[0142] In an embodiment of the present invention, while the automatic cleaning device is running, measured distance values ​​and multiple frame point clouds of the automatic cleaning device are acquired within a predetermined detection time period, and by recognizing the measured distance values, the automatic cleaning device's active stop state within the predetermined detection time period is excluded from the detection range of the slippage state, thereby reducing the number of times the automatic cleaning device is detected and improving the system's detection efficiency. When a slippage state is detected, the matching relationship between the point data of the multiple frame point clouds is used to determine whether the automatic cleaning device has changed its position, and further determine that the automatic cleaning device is in a slippage state. This reduces the amount of data processing and the complexity of matching, improves matching efficiency, enables quick and accurate detection of the slippage state, improves detection sensitivity, and improves the user experience.

[0143] The present invention further provides an embodiment of an automatic cleaning device that matches the above embodiment, and performs the method steps described in the above embodiment. The interpretation based on the same name meaning is the same as the above embodiment, and the same action and effect as the above embodiment are also achieved, so they will not be described again here.

[0144] 5, the present invention provides an automatic cleaning device, which includes a processor 130, an odometry 131, and a laser radar 132. The odometry 131 is configured to obtain a measured distance value, where the measured distance value represents a rotation distance value of the drive wheel assembly. The laser radar 132 is configured to acquire a cloud of frame points while the automatic cleaning equipment is traveling. The processor 130 is communicatively connected to the odometry 131 and the laser radar 132, and the processor 130 During the travel of the automatic cleaning device, a measured distance value is acquired by odometry within a predetermined detection time period, and a plurality of frame point clouds are acquired by laser radar, wherein the measured distance value represents a rotation distance value of a drive wheel assembly; The automated cleaning device is configured to identify a slippage state based on the distance measurements and the plurality of point frames.

[0145] Optionally, the processor is configured to identify the automatic cleaning device as being in a slipping state based on the distance measurements and the plurality of frame point clouds, further comprising: When the measured distance value is greater than a predetermined measured distance threshold, determining whether a position change has occurred in the automatic cleaning device based on the plurality of frame point clouds; and determining that the automatic cleaning device is in a slip state when it is determined that no position change has occurred in the automatic cleaning device.

[0146] Optionally, each frame point cloud includes a plurality of point data; Accordingly, the processor 130 is configured to determine whether a position change has occurred in the automatic cleaning device based on the plurality of frame point clouds. Assigning the plurality of frame point clouds to a first point cloud set or a second point cloud set, respectively, where the first point cloud set includes a plurality of first frame point clouds 31, and the second point cloud set includes a plurality of second frame point clouds 32; Performing similarity matching based on the plurality of first frame point groups 31 and the plurality of second frame point groups 32 to obtain a similarity matching result; and determining whether a position change has occurred to the automated cleaning device based on the similarity matching results.

[0147] Optionally, the processor 130 is configured to perform similarity matching based on the plurality of first frame point clouds 31 and the plurality of second frame point clouds 32 to obtain a similarity matching result, A first planar map is obtained by projecting the two-dimensional position of each point data in each first frame point group 31 onto a predetermined plane, and a second planar map is obtained by projecting the two-dimensional position of each point data in each second frame point group 32 onto a predetermined plane, wherein the first planar map includes a plurality of first projection positions, and the second planar map includes a plurality of second projection positions; and performing similarity matching based on a plurality of first projection positions in the first planar map and a plurality of second projection positions in the second planar map to obtain a similarity matching result.

[0148] Optionally, the pre-formed plane comprises a pre-formed grating plane; The first planar map includes a first grid planar map 33, and includes a plurality of first occupied grids 331 in a plurality of grids of the first grid planar map 33, and each first occupied grid 331 includes at least one first projection position; The second planar map 34 includes a second grid planar map 34, and includes a plurality of second occupied grids 341 in a plurality of grids of the second grid planar map 34, and each second occupied grid 341 includes at least one second projection position.

[0149] Accordingly, the processor 130 is configured to perform similarity matching based on a plurality of first projection positions in the first planar map and a plurality of second projection positions in the second planar map to obtain a similarity matching result, The method includes performing grid similarity matching based on the at least one first projected position in each first occupied grid 331 of the first grid planar map 33 and the at least one second projected position in each second occupied grid 341 of the second grid planar map 34, and obtaining a similarity matching result.

[0150] Optionally, the processor 130 is configured to perform grid similarity matching based on the at least one first projected position in each first occupied grid 331 of the first grid planar map 33 and the at least one second projected position in each second occupied grid 341 of the second grid planar map 34 to obtain a similarity matching result; Obtaining a first position normalization value corresponding to a first occupied grid 331 based on the at least one first projected position in each first occupied grid 331 of the first grid planar map 33, and obtaining a second position normalization value corresponding to a second grid based on the at least one second projected position in each second grid of the second grid planar map 34; and performing grid similarity matching based on the first position normalized value of each first occupied grid 331 in the first grid planar map 33 and the second position normalized value of each second occupied grid 341 in the second grid planar map 34 to obtain a similarity matching result.

[0151] Optionally, the processor 130 is configured to perform grid similarity matching based on a first position normalized value of each first occupied grid 331 in the first grid planar map 33 and a second position normalized value of each second occupied grid 341 in the second grid planar map 34 to obtain a similarity matching result; When any one of the first occupied grids 331 in the first grid planar map 33 is traversed, a first calculation parameter value is increased by 1; When a first position normalization value of any one of the first occupied lattices 331 and a second position normalization value of a lattice corresponding to any one of the first occupied lattices 331 in the second lattice planar map 34 satisfy a predetermined identical condition, incrementing a second calculation parameter value by 1; After the traversal is completed, calculating a quotient of the second calculated parameter value and the first calculated parameter value to obtain a first similarity matching result.

[0152] Optionally, the processor 130 is configured to perform grid similarity matching based on a first position normalized value of each first occupied grid 331 in the first grid planar map 33 and a second position normalized value of each second occupied grid in the second grid planar map 34 to obtain a similarity matching result; When any one of the second occupied grids 341 in the second grid planar map 34 is traversed, the third calculation parameter value is increased by 1; When the second position normalization value of any one of the second occupied lattices 341 and the first position normalization value of the lattice corresponding to any one of the second occupied lattices 341 in the first lattice planar map 33 satisfy a predetermined identical condition, incrementing a fourth calculation parameter value by 1; After the traversal is completed, calculating a quotient of the fourth calculated parameter value and the third calculated parameter value to obtain a second similarity matching result.

[0153] Optionally, the processor 130 is configured to obtain a first position normalization value corresponding to a first occupied grid 331 based on the at least one first projected position in each first occupied grid 331 of the first grid planar map 33, and to obtain a second position normalization value corresponding to a second grid based on the at least one second projected position in each second grid of the second grid planar map 34, Identifying a first position normalization value corresponding to the first occupied grid 331 as 1 based on the at least one first projected position in each first occupied grid 331 of the first grid planar map 33, wherein the initial position normalization value of each grid in the first grid planar map 33 before similarity matching is zero; and determining a second position normalization value corresponding to the second grid as 1 based on the at least one first projection position in each second grid of the second grid plane map 34, wherein the initial position normalization value of each grid in the second grid plane map 34 before similarity matching is zero.

[0154] Optionally, the processor 130 is configured to assign the plurality of frame point clouds to a first point cloud set or a second point cloud set, respectively, by: Dividing a predetermined detection time period into a first detection time period and a second detection time period; The method includes assigning a plurality of frame point clouds within the first detection time period to a first point cloud set, and assigning a plurality of frame point clouds within the second detection time period to a second point cloud set.

[0155] Optionally, the processor 130 is configured to determine whether a position change has occurred in the automated cleaning device based on the similarity matching results, The method includes determining that no position change has occurred in the automatic cleaning device when the first similarity matching result and / or the second similarity matching result is equal to or greater than a predetermined occupancy ratio threshold.

[0156] Optionally, the processor 130 is further configured to determine whether a position change has occurred in the automated cleaning device based on the similarity matching results; The method includes determining that a position change has occurred in the automatic cleaning device when the first similarity matching result and / or the second similarity matching result is smaller than a predetermined occupancy ratio threshold.

[0157] Optionally, the processor 130 is configured to identify the automatic cleaning device as being in a slipping state based on the distance measurements and the plurality of frame point clouds, determining whether a position change has occurred in the automated cleaning device based on the plurality of frame point clouds; When it is determined that no position change has occurred in the automatic cleaning device based on the plurality of frame point clouds, determining whether the measured distance value is greater than a predetermined measured distance threshold value; and identifying the automatic cleaning device as being in a slip state when the measured distance value is greater than a predetermined measured distance threshold.

[0158] In an embodiment of the present invention, while the automatic cleaning device is running, measured distance values ​​and multiple frame point clouds of the automatic cleaning device are acquired within a predetermined detection time period, and by recognizing the measured distance values, the automatic cleaning device's active stop state within the predetermined detection time period is excluded from the detection range of the slippage state, thereby reducing the number of times the automatic cleaning device is detected and improving the system's detection efficiency. When a slippage state is detected, the matching relationship between the point data of the multiple frame point clouds is used to determine whether the automatic cleaning device has changed its position, and further determine that the automatic cleaning device is in a slippage state. This reduces the amount of data processing and the complexity of matching, improves matching efficiency, enables quick and accurate detection of the slippage state, improves detection sensitivity, and improves the user experience.

[0159] An embodiment of the present invention provides a non-transitory computer-readable storage medium having computer program instructions stored thereon, the computer program instructions being executed by a processor to perform the method steps of any one of the above methods.

[0160] An embodiment of the present invention provides an automatic cleaning device, the automatic cleaning device comprising a processor and a memory, the memory storing computer program instructions executable by the processor, the processor, when executing the computer program instructions, performing the method steps of any of the above embodiments.

[0161] As shown in Figure 6, the automatic cleaning device may include a processing unit (e.g., a central processing unit, a graphics processor, etc.) 601. The processing unit can perform various appropriate operations and processes according to programs stored in a read-only memory (ROM) 602 or programs loaded from a storage device 608 into a random access memory (RAM) 603. The RAM 603 also stores various programs and data necessary for the operation of the automatic cleaning device. The processing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0162] Typically, the following devices may be connected to the I / O interface 605: input device 606 including, for example, a touch screen, touch panel, keyboard, mouse, camera head, microphone, accelerometer, gyro, etc.; output device 607 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 608 including, for example, a hard disk, etc.; and communication device 609. The communication device 609 may enable the electronic device to communicate wirelessly or via a wire with other devices for data exchange. While FIG. 6 illustrates an electronic device having various devices, it should be understood that the electronic device may not necessarily implement or include all of the devices shown. Alternatively, the electronic device may implement or include more or fewer devices.

[0163] The flowcharts and block diagrams in the figures illustrate possible architectural architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowcharts or block diagrams may represent a module, program segment, or portion of code. The module, program segment, or portion of code includes one or more executable instructions for implementing a specified logical function. In some alternative embodiments, the functions marked in the blocks may occur in a different order from that shown in the figures. For example, two consecutive blocks may indeed be essentially executed in parallel, and may also be executed in the reverse order, depending on the functionality. It should be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be realized in a dedicated hardware-based system that performs the specified functions or operations, or in a combination of dedicated hardware and computer instructions.

[0164] Finally, it should be noted that the embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments, and references to the same or similar parts between the embodiments are sufficient. The systems or devices described in the embodiments correspond to the methods described in the embodiments, so the description is relatively simple, and references to the description of the method embodiments are sufficient for relevant parts.

[0165] The above embodiments are used to explain the technical solutions of the present invention, but are not intended to limit them. The present invention has been described in detail with reference to the above embodiments. However, those skilled in the art can still modify the technical solutions described in each of the above embodiments or substitute some technical features with equivalents, and these modifications and substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each of the embodiments of the present invention.

Claims

1. A slippage detection method, comprising: While the automatic cleaning device is running, a measured distance value representing a rotation distance value of a drive wheel assembly is acquired by odometry within a predetermined detection time period, and a plurality of frame point clouds are acquired by a laser radar; and identifying the automatic cleaning device as being in a slippage state based on the distance measurements and the plurality of frame point clouds.

2. Identifying the automatic cleaning device as being in a slipping state based on the measured distance values ​​and the plurality of frame point clouds includes: When the measured distance value is greater than a predetermined measured distance threshold, determining whether a position change has occurred in the automatic cleaning device based on the plurality of frame point clouds; and determining that the automatic cleaning device is in a slip state when it is determined that no position change has occurred in the automatic cleaning device.

3. Identifying the automatic cleaning device as being in a slipping state based on the measured distance values ​​and the plurality of frame point clouds includes: determining whether a position change has occurred in the automated cleaning device based on the plurality of frame point clouds; When it is determined that no position change has occurred in the automatic cleaning device based on the plurality of frame point clouds, determining whether the measured distance value is greater than a predetermined measured distance threshold value; and identifying the automatic cleaning device as being in a slip state when the measured distance value is greater than a predetermined measured distance threshold.

4. Each frame point group includes a plurality of point data, Determining whether a position change has occurred in the automatic cleaning device based on the plurality of frame point clouds includes: assigning the plurality of frame point clouds to a first point cloud set including a plurality of first frame point clouds or a second point cloud set including a plurality of second frame point clouds; performing similarity matching based on the plurality of first frame point clouds and the plurality of second frame point clouds to obtain a similarity matching result; The method for detecting a slippage state according to claim 2 or 3, further comprising: determining whether a position change has occurred in the automatic cleaning device based on the similarity matching result.

5. The step of assigning the plurality of frame point clouds to a first point cloud set or a second point cloud set includes: Dividing a predetermined detection time period into a first detection time period and a second detection time period; 5. The method for detecting a slippage state according to claim 4, further comprising: assigning a plurality of frame point clouds within the first detection time period to a first point cloud set, and assigning a plurality of frame point clouds within the second detection time period to a second point cloud set.

6. performing similarity matching based on the plurality of first frame point groups and the plurality of second frame point groups to obtain a similarity matching result, projecting the two-dimensional position of each point data in each first frame point group onto a predetermined plane to obtain a first planar map including a plurality of first projection positions, and projecting the two-dimensional position of each point data in each second frame point group onto a predetermined plane to obtain a second planar map including a plurality of second projection positions; 5. The method for detecting a slippage state according to claim 4, further comprising: performing similarity matching based on a plurality of first projection positions in the first planar map and a plurality of second projection positions in the second planar map, and obtaining a similarity matching result.

7. the predetermined plane includes a predetermined grating plane; the first planar map includes a first grid planar map, including a plurality of first occupied grids in a plurality of grids of the first grid planar map, each of the first occupied grids including at least one first projection location; the second planar map includes a second grid planar map, and includes a plurality of second occupied grids in a plurality of grids of the second grid planar map, each of the second occupied grids including at least one second projection location; Correspondingly, performing similarity matching based on a plurality of first projection positions in the first planar map and a plurality of second projection positions in the second planar map to obtain a similarity matching result includes:

7. The slippage state detection method according to claim 6, further comprising: performing grid similarity matching based on the at least one first projected position in each first occupied grid of the first grid planar map and the at least one second projected position in each second occupied grid of the second grid planar map, and obtaining a similarity matching result.

8. performing grid similarity matching based on the at least one first projected position in each first occupied grid of the first grid planar map and the at least one second projected position in each second occupied grid of the second grid planar map to obtain a similarity matching result, obtaining a first position normalization value corresponding to a first occupied grid based on the at least one first projected position in each first occupied grid of the first grid planar map, and obtaining a second position normalization value corresponding to a second grid based on the at least one second projected position in each second grid of the second grid planar map; 8. The method for detecting a slippage state according to claim 7, further comprising: performing grid similarity matching based on a first position normalized value of each first occupied grid in the first grid planar map and a second position normalized value of each second occupied grid in the second grid planar map, and obtaining a similarity matching result.

9. performing grid similarity matching based on a first position normalized value of each first occupied grid in the first grid planar map and a second position normalized value of each second occupied grid in the second grid planar map to obtain a similarity matching result, When any one of the first occupied grids in the first grid planar map is traversed, a first calculated parameter value is increased by 1; Incrementing a second calculation parameter value by 1 when a first position normalization value of any one of the first occupied lattices and a second position normalization value of a lattice corresponding to any one of the first occupied lattices in the second lattice planar map satisfy a predetermined identical condition; 9. The method for detecting a slippage state according to claim 8, further comprising: after the traverse is completed, calculating a quotient of the second calculated parameter value and the first calculated parameter value to obtain a first similarity matching result.

10. performing grid similarity matching based on a first position normalized value of each first occupied grid in the first grid planar map and a second position normalized value of each second occupied grid in the second grid planar map to obtain a similarity matching result, When any one second occupied grid in the second grid planar map is traversed, a third calculated parameter value is increased by 1; When the second position normalization value of any one of the second occupied lattices and the first position normalization value of a lattice corresponding to any one of the second occupied lattices in the first lattice planar map satisfy a predetermined identical condition, incrementing a fourth calculation parameter value by 1; 10. The method for detecting a slippage state according to claim 9, further comprising: after the traverse is completed, calculating a quotient of the fourth calculated parameter value and the third calculated parameter value to obtain a second similarity matching result.

11. Obtaining a first position normalization value corresponding to a first occupied grid based on the at least one first projected position in each first occupied grid of the first grid planar map, and obtaining a second position normalization value corresponding to a second grid based on the at least one second projected position in each second grid of the second grid planar map, Identifying a first position normalization value corresponding to each first occupied grid as 1 based on the at least one first projected position in each first occupied grid of the first grid planar map; and determining a second position normalization value corresponding to each second grid as 1 based on the at least one first projected position in each second grid of the second grid planar map; 9. The method for detecting a slippage state according to claim 8, wherein each grid in the first grid planar map has an initial position normalized value of zero before similarity matching is performed, and each grid in the second grid planar map has an initial position normalized value of zero before similarity matching is performed.

12. Determining whether a position change has occurred in the automatic cleaning device based on the similarity matching result includes: The slippage detection method of claim 10, further comprising determining that no position change has occurred in the automatic cleaning device when the first similarity matching result and / or the second similarity matching result is greater than or equal to a predetermined occupancy ratio threshold.

13. Determining whether a position change has occurred in the automatic cleaning device based on the similarity matching result includes: The slippage detection method of claim 10, further comprising determining that a position change has occurred in the automatic cleaning device when the first similarity matching result and / or the second similarity matching result is smaller than a predetermined occupancy ratio threshold.

14. 1. An automatic cleaning device comprising: The present invention includes an odometry system, a laser radar, and a processor. the odometry is configured to obtain a measured distance value representing a rotation distance value of a drive wheel assembly; The laser radar is configured to acquire a frame point cloud while the automatic cleaning equipment is traveling; The processor is communicatively connected to the odometry and the laser radar, and the processor While the automatic cleaning device is running, a measured distance value representing a rotation distance value of a drive wheel assembly is acquired by odometry within a predetermined detection time period, and a plurality of frame point clouds are acquired by laser radar; The automatic cleaning device is configured to identify the automatic cleaning device as being in a slipping state based on the distance measurements and the plurality of frame point clouds.

15. 1. An automatic cleaning device comprising: An automatic cleaning appliance comprising a processor and a memory, wherein the memory stores computer program instructions executable by the processor, and when the processor executes the computer program instructions, the automatic cleaning appliance performs the steps of the slippage detection method according to any one of claims 1 to 13.

16. 1. A non-transitory computer-readable storage medium, comprising: The non-transitory computer-readable storage medium stores computer program instructions, which, when called and executed by a processor, perform the steps of the slippage detection method described in any one of claims 1 to 13.

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