Control method, self-moving device and storage medium

Abstract

The invention provides a control method, a self-moving device and a storage medium. The method comprises the steps that a grid map generated in the operation process of the self-moving device is acquired; determining at least one closed area based on the grid map; based on a preset edge finding distance in each closed area, an edge finding path is determined, and the edge finding distance is the distance between the self-moving device and a non-working area; and executing a job task along the edge finding path. According to the method, the edge finding precision and efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of self-moving equipment, and in particular to a control method, a self-moving equipment and a storage medium. Background Art

[0002] Existing self-moving devices may miss tasks due to environmental factors or problems with the device itself while completing designated tasks. Generally, self-moving devices will check whether there are any missed tasks so that they can handle the missed tasks in a timely manner. For example, a mowing robot will perform a re-mowing operation on the lawn boundary or restricted area boundary after completing the mowing task or during the mowing process. Existing mowing robots mainly improve coverage by patrolling along the border and re-mowing the lawn. Their boundary acquisition mainly relies on technologies such as buried wire deployment, manual remote control recording, or automatic recording. Among them, the buried wire deployment method is cumbersome to construct, and the manual remote control recording method is time-consuming and has high labor costs in complex and multi-restricted area scenarios. The automatic recording method is difficult to cope with complex recording scenarios, affecting the accuracy and efficiency of the recording. Summary of the Invention

[0003] The embodiments of the present application disclose a control method, a self-propelled device, and a storage medium, which solve the technical problems of high labor costs, low patrol accuracy, and low efficiency in related border patrol technologies.

[0004] The present application provides a control method, which includes: obtaining a grid map generated during the operation of a self-moving device; determining at least one closed area based on the grid map; determining a patrol path based on a preset patrol distance in each closed area, wherein the patrol distance is the distance between the self-moving device and a non-operating area; and performing an operation task along the patrol path.

[0005] In some embodiments of the present application, determining at least one closed area based on the grid map includes: converting the grid map into a cost map; determining a first boundary corresponding to the working area and a second boundary corresponding to the at least one non-working area based on the cost value corresponding to each grid in the cost map; and determining the at least one closed area constructed by the first boundary and each second boundary based on the first boundary and the second boundary.

[0006] In some embodiments of the present application, determining the patrol path based on the preset patrol distance in each closed area includes: obtaining the patrol cost value corresponding to the patrol distance in each closed area; determining the target grid in each closed area based on the patrol cost value; and determining the patrol path based on the target grid.

[0007] In some embodiments of the present application, converting the grid map into a cost map includes: identifying the grid map according to a preset semantic segmentation model to determine the working area and the non-working area; based on the association relationship between the working area and each non-working area, determining the cost value of the grid corresponding to the working area and each non-working area to generate the cost map.

[0008] In some embodiments of the present application, determining the edge patrol path based on the target grid includes: determining at least one edge patrol contour based on the target grid; and smoothing the at least one edge patrol contour to obtain the edge patrol path.

[0009] In some embodiments of the present application, the method further includes: if there are N non-operating areas, determining that there are at least N edge patrol contours, where N is a positive integer.

[0010] In some embodiments of the present application, determining the patrol path based on the preset patrol distance in each closed area includes: determining the area range corresponding to each closed area; when the area range corresponding to any closed area meets the preset conditions, determining multiple patrol distances in the closed area corresponding to any closed area based on multiple preset thresholds, each patrol distance having a corresponding preset threshold; and determining multiple patrol paths corresponding to any closed area based on the multiple patrol distances.

[0011] In some embodiments of the present application, the grid map generated during the operation of the mobile device is obtained, including: obtaining a geometric map representing the geometric structure of the environment, and a semantic map carrying environmental semantic tags; and determining the grid map based on the geometric map and the semantic map.

[0012] The present application also provides a self-moving device, which includes a processor and a memory, and the processor is used to implement the control method when executing a computer program stored in the memory.

[0013] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the control method is implemented.

[0014] The control method provided in this application obtains a grid map generated during the operation of a mobile device and uses it to determine at least one closed area. This accurately determines the boundary between the operating and non-operating areas, reducing the difficulty of determining the boundary between the operating and non-operating areas. Furthermore, determining closed areas based on the grid map eliminates the need for user intervention, reducing labor costs. A patrol path is determined based on a preset patrol distance within each closed area, and the task is executed along this patrol path. This improves patrol accuracy and addresses the issue of missed tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a structural diagram of a self-moving device provided in an embodiment of the present application.

[0016] Figure 2 This is a flow chart of the control method provided in an embodiment of the present application.

[0017] Figure 3 It is a schematic diagram of a grid map in an embodiment of the present application.

[0018] Figure 4 This is a flow chart for determining a closed area provided in an embodiment of the present application.

[0019] Figure 5 It is a schematic diagram of the closed area provided in an embodiment of the present application.

[0020] Figure 6 This is a flow chart for determining a patrol path according to an embodiment of the present application.

[0021] Figure 7 Schematic diagram of the edge patrol profile provided in an embodiment of the present application.

[0022] Figure 8 This is a schematic diagram of an edge patrol profile provided in another embodiment of the present application. DETAILED DESCRIPTION

[0023] To facilitate understanding, some illustrations of concepts related to the embodiments of the present application are given for reference.

[0024] It should be noted that, in this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A alone, A and B together, and B alone, where A and B can be singular or plural. The terms "first," "second," "third," "fourth," and so on (if any) in the specification, claims, and drawings of this application are used to distinguish similar objects, not to describe a specific order or precedence.

[0025] Autonomous devices, such as robotic lawn mowers, are autonomously controlled and move. While completing their assigned tasks, they may miss tasks due to environmental factors or device issues. Generally, autonomous devices check for missed tasks and address them promptly. For example, a robotic lawn mower may perform a catch-up mowing operation at the lawn's perimeter or restricted area after completing a mowing task or while mowing.

[0026] Existing robotic lawn mowers primarily improve coverage by patrolling and mowing along boundaries. Boundary acquisition relies primarily on technologies such as wired deployment, manual remote recording, or automatic recording. The wired deployment method is cumbersome to construct, while the manual remote recording method is time-consuming and labor-intensive in complex, multi-restricted areas. Automatic recording struggles with complex recording scenarios, impacting accuracy and efficiency.

[0027] In order to solve the technical problems of high labor costs, low patrol accuracy, and low efficiency in related border patrol technologies, this application proposes a control method, a self-moving device, and a storage medium. Through a grid map, leak repair operations are performed without manual intervention, thus reducing labor costs. In addition, determining the patrol path through a grid map can also improve the accuracy and efficiency of leak repair operations. The following first introduces the application scenarios of the control method of this application.

[0028] Figure 1 This is a schematic diagram of the structure of the self-moving device provided in the embodiment of the present application. Figure 1 As shown, in the embodiment of the present application, the autonomous device 10 can be a semi-autonomous device or a fully autonomous device, such as a lawn mower robot, a sweeping robot, a snow sweeper, a cleaning robot, etc. The present application does not limit the type of the autonomous device 10.

[0029] The self-propelled device 10 may include a main body, and a memory 110, a processor 120, a power supply 130, a control device 140, a sensor 150, an operating device 160, a communication module 170, a positioning module 180, a driving wheel 190, and a bus 100 disposed on the main body. The processor 120 is coupled to the memory 110, the power supply 130, the control device 140, the sensor 150, the operating device 160, the communication module 170, the positioning module 180, and the driving wheel 190 via the bus 100.

[0030] The memory 110 may be used to store an operating system, computer programs, various data, images, etc. For example, the memory 110 stores a program corresponding to the control method provided in the embodiment of the present application.

[0031] The processor 120 provides computing and control capabilities to support the operation of the entire mobile device. For example, the processor 120 is used to execute the computer program stored in the memory 110 to implement the steps in the control method provided in the embodiment of the present application.

[0032] The power supply 130 is used to supply power to the mobile device 10. In one embodiment of the present application, the power supply 130 may include a battery pack composed of a plurality of battery cells.

[0033] The control device 140 is used to control the movement and behavior of the mobile device 10. In one embodiment of the present application, the control device 140 can realize functions such as a motion controller and a logic controller.

[0034] The sensor 150 is used to obtain data from the mobile device 10, such as environmental data and data related to the mobile device 10. In one embodiment of the present application, the sensor 150 may include one or more of a collision sensor, a current sensor, a voltage sensor, a rain detection sensor, a laser radar, a camera, and an ultrasonic sensor.

[0035] The working device 160 is used to perform corresponding working functions, such as mowing, sweeping, and spraying pesticides. In one embodiment of the present application, the working device 160 may include a drive mechanism such as a motor and a hydraulic cylinder, as well as a cutterhead including blades. In one embodiment of the present application, the motor can drive the blades to move to complete the mowing operation. The motor can control the movement of the blades to adjust the height and speed of the mowing.

[0036] The communication module 170 is used to enable communication between the mobile device 10 and other devices. In one embodiment of the present application, the communication module 170 can exchange data with other devices via wired and / or wireless communication. Such wireless communication can include one or a combination of Bluetooth, Wi-Fi, and Near Field Communication (NFC).

[0037] The positioning module 180 is used to determine the position and movement direction of the mobile device 10. In one embodiment of the present application, the positioning module 180 may include a global positioning system (GPS), an inertial measurement unit (IMU), and the like.

[0038] The driving wheels 190 are used to enable the self-moving device 10 to move. In one embodiment of the present application, the driving wheels 190 can enable the self-moving device 10 to move along the target planned trajectory under the control of the control device 140. In one embodiment of the present application, the self-moving device 10 may include driving wheels and passive wheels, and the driving wheels may further include left and right driving wheels.

[0039] The bus 100 is at least used to provide a channel for mutual communication between the memory 110, processor 120, power supply 130, control device 140, sensor 150, operating device 160, communication module 170, positioning module 180, and driving wheel 190 in the mobile device 10.

[0040] In this embodiment, the memory 110 may be an internal memory of the mobile device 10, that is, a memory built into the mobile device 10. In other embodiments, the memory 110 may also be an external memory of the mobile device 10, that is, a memory externally connected to the mobile device 10.

[0041] It should be understood that memory 110 may include a program storage area and a data storage area. The program storage area may be used to store an operating system, at least one application program required for a method (such as a control method), and the like; the data storage area may be used to store data generated based on the use of mobile device 10. Furthermore, memory 110 may include volatile memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, at least one disk storage device, a flash memory device, or other storage devices.

[0042] In some embodiments of the present application, the memory 110 is used to store program codes and various data, and to achieve high-speed and automatic access to programs or data during operation of the mobile device 10 .

[0043] In some embodiments of the present application, the processor 120 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, a processor, or any other conventional processor.

[0044] The schematic Figure 1 This is merely an example of a self-mobile device and does not constitute a limitation of the self-mobile device. The self-mobile device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the self-mobile device may also include a display screen, a network device, etc.

[0045] Figure 2 This is a flow chart of a control method provided by an embodiment of the present application, which is applied to a mobile device (e.g. Figure 1 According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0046] Step S201: obtaining a grid map generated during the operation of a mobile device.

[0047] In some embodiments of the present application, the self-moving device generally plans a movement path within a designated work area and moves along the planned movement path to perform work. The self-moving device may perform autonomous navigation based on an environment map input by a user or a previously detected environment map. For example, if the self-moving device is a lawn mower robot, the environment map may be a lawn map including a lawn area, and the lawn mower robot may perform mowing tasks within the lawn area based on the lawn map.

[0048] The environmental map can include a geometric map generated by the Simultaneous Localization and Mapping (SLAM) module and a semantic map generated by the perception module during mobile device operation. The geometric map is a map of the environment's geometric structure and can be generated through real-time positioning and mapping using lidar, cameras, or multi-sensor fusion (such as visual-inertial odometry (VIO) and real-time kinematic (RTK)). The semantic map carries the semantic labels of the environment and can be generated using depth cameras, lidar, semantic segmentation models, and other methods. These environmental semantic labels can include areas where the vehicle can move and areas where it cannot move (such as areas where obstacles are located). A raster map is generated by fusing the geometric and semantic maps and performing rasterization.

[0049] In one embodiment, the grid resolution of the grid map is determined based on the geometry of the environment (e.g., the size of the actual environment), and the number of grid cells in the grid map is determined based on the grid resolution. The grid resolution represents the ratio of each grid cell to the size of the actual environment. The size of the grid cells occupied by the mobile device in the grid map is determined based on the size of the self-moving device. The size of the obstacle grid occupied by the obstacle in the grid map is determined based on the size of the obstacle predetermined in the actual environment. The obstacle grid represents a grid cell that the mobile device cannot pass through, while the non-obstruction grid represents a grid cell that the mobile device can pass through.

[0050] In one example, when the autonomous device is a lawn mower robot, the actual environment may be a lawn, and obstacles in the actual environment may include flower beds, lampposts, rocks, small tree stumps, fences, etc. The autonomous device can identify non-obstacle grids in a pre-built grid map to determine drivable locations in the actual environment, thereby achieving autonomous navigation. In other words, the autonomous device avoids obstacle grids in the grid map.

[0051] Step S202: determining at least one closed area based on the grid map.

[0052] In some embodiments of the present application, each grid in the grid map has a corresponding environmental semantic label, and different environmental semantic labels correspond to different objects. Figure 3 As shown in the figure, different environmental semantic labels are presented in different visualization ways, such as Figure 3 The grid map shown includes the road surface outside the lawn, the lawn, obstacles, and the boundary area between the lawn and non-lawn.

[0053] To avoid missing work, the outline of the non-work area and / or the outline of the work area can be determined to demarcate the work area from the non-work area. The outline of the work area is recorded as the first boundary, and the outline of the non-work area is recorded as the second boundary. The first boundary and the second boundary can form a closed area, which is used to represent the boundary between the work area and the non-work area.

[0054] In one example, assume that the self-moving device is a lawn mower robot, the operating area is a lawn, and the non-operating area includes two obstacles (such as a flower bed), which are denoted as obstacle A and obstacle B. The cost value is used to determine the second boundary A corresponding to obstacle A, and the cost value is used to determine the second boundary B corresponding to obstacle B. The cost value is used to determine the first boundary corresponding to the lawn. Then, a closed area can be formed by the first boundary and the second boundary A, and a closed area can also be formed by the first boundary and the second boundary B. Combined Figure 3 As shown, the boundary area between lawn and non-lawn is a closed area, wherein the non-lawn includes the road surface and obstacles outside the lawn.

[0055] In some embodiments of the present application, each grid in the grid map has a corresponding cost value, and the closer the distance between the mobile device and the obstacle is, the larger the cost value is. Different objects in the grid map can be represented by different cost values, and at least one closed contour can be determined by the cost values, which can be referred to as follows: Figure 4 Detailed description of the illustrated embodiment.

[0056] Step S203: determining a patrol path based on a preset patrol distance in each closed area.

[0057] In some embodiments of the present application, the patrol distance may be the distance between the self-moving device and the non-operating area within the closed area. In one embodiment, the target grid in the grid map can be determined based on the patrol distance, and a patrol contour is obtained by fitting all target grids, thereby obtaining a patrol path based on the patrol contour. For example, assuming the area range of the closed area is 10m and the patrol distance is 5m, all grids within the closed area that are 5m away from the second boundary of the non-operating area are searched from the grid map as target grids. The patrol path is obtained based on the patrol contour formed by all target grids.

[0058] In some embodiments of the present application, it is assumed that a patrol distance is set for a closed area. If there are N non-operation areas, there are N patrol contours, where N is a positive integer. For example, the first boundary of the operation area includes the outer boundary and the inner boundary, and the non-operation area includes the area outside the outer boundary and the non-operation area within the operation area. According to the outer boundary and the area outside the outer boundary (such as Figure 3If there are M non-operable areas within the inner boundary, then M closed areas can be formed based on the inner boundary and the second boundary of the M non-operable areas. In this way, if a patrol distance is set in a closed area, there are M+1 patrol paths. Figure 6 The embodiment shown.

[0059] If multiple patrol distances are set for a closed area, each closed area corresponds to multiple patrol paths. For example, if two patrol distances are set for each closed area, two patrol paths can be determined in each closed area. Figure 6 The embodiment shown.

[0060] Step S204: Execute the work task along the patrol path.

[0061] In some embodiments of the present application, the state of the mobile device is obtained. If the mobile device is still in the process of operation, step S201 is continued and the patrol path is updated in real time. If the mobile device is in the state of completing the operation, the mobile device performs the operation task along the patrol path.

[0062] In one example, a robotic lawn mower is used as a self-mobile device. A raster map generated by the self-mobile device during mowing is obtained to determine a patrol path. After the self-mobile device completes its mowing task, a follow-up mowing task is performed based on the patrol path to avoid missing any grass.

[0063] Through the above-described embodiment, a grid map generated during the operation of a mobile device is obtained, and at least one closed area is determined from the grid map, accurately determining the boundary between the operating and non-operating areas. This reduces the difficulty of determining the boundary between the operating and non-operating areas. Furthermore, determining closed areas based on the grid map eliminates the need for user involvement, reducing labor costs. A patrol path is determined based on the preset patrol distance within each closed area, and the task is executed along this patrol path. This improves patrol accuracy and addresses the issue of missed tasks.

[0064] Figure 4 This is a flow chart for determining a closed area provided by an embodiment of the present application. Figure 4 As shown, determining at least one closed area based on a grid map includes the following steps.

[0065] Step S401: convert the grid map into a cost map.

[0066] In some embodiments of the present application, a preset semantic segmentation model is used to identify a grid map to obtain the work area and non-work area. Based on the association between the work area and each non-work area, the cost value of the grid corresponding to the work area and each non-work area is determined to generate a cost map. The association relationship can be determined based on the distance between the work area and each non-work area, or based on the degree of danger of the non-work area to the work area.

[0067] In one example, assume the work area is a lawn, and the non-work area includes the road surface outside the lawn, static obstacles, dynamic obstacles, and the boundary between the lawn and the road surface outside the lawn. The lawn cost is set to a low cost, such as 10. Since the road surface outside the lawn is a non-workable area, the road surface outside the lawn can be set to a high cost or an infinite value, such as 255. Since the hazard level of static obstacles is moderate, the cost of static obstacles can be set to a higher cost, such as 200. Furthermore, the cost of static obstacles can be further subdivided based on their distance from the lawn. For example, the cost of static obstacles near the lawn boundary can be 200, while the cost of other areas not near the lawn can be 198. Since the hazard level of dynamic obstacles is very dangerous, the cost of dynamic obstacles can be set to the highest cost, such as 254. Since the boundary area may be missed, the cost of the boundary area can be set to a medium cost, such as 100. The above are just examples; the cost values ​​can be set accordingly based on the actual type of non-work area.

[0068] Step S402 : determining a first boundary corresponding to the operating area and a second boundary corresponding to at least one non-operating area based on the cost value corresponding to each grid in the cost map.

[0069] In some embodiments of the present application, after determining the cost value for each grid in the grid map, in order to avoid missing operations, the cost value can be used to determine the outline of the non-operating area and / or the outline of the operating area, thereby dividing the operating area and the non-operating area. The outline of the operating area determined by the cost value is recorded as the first boundary, and the outline of the non-operating area determined by the cost value is recorded as the second boundary.

[0070] In one example, a cost value corresponding to the outline of the work area is determined, recorded as a first value. A first grid corresponding to the first value is located in the cost map, and a closed curve formed by connecting all adjacent first grids is used as the first boundary. A cost value corresponding to the outline of the non-work area is determined, recorded as a second value. A second grid corresponding to the second value is located in the cost map, and a closed curve formed by connecting all adjacent second grids is used as the second boundary.

[0071] Step S403: Based on the first boundary and the second boundary, determine at least one closed area constructed by the first boundary and each second boundary.

[0072] In some embodiments of the present application, after the first boundary and the second boundary are determined, the boundary area between the first boundary and the second boundary is regarded as a closed area. Figure 5 As shown, based on the first boundary corresponding to the working area and the second boundary corresponding to the non-working area, a closed area with an area range of d is formed.

[0073] Through the above embodiment, the cost map is used to determine the first boundary and the second boundary, and the boundary of the working area and the boundary of the non-working area are accurately located, which improves the accuracy and efficiency of the working task (such as the mowing task of patching leaks) to a certain extent.

[0074] Figure 6 This is a flow chart for determining the patrol path provided by the embodiment of the present application. Figure 6 As shown, determining a patrol path according to one or more preset patrol distances includes the following steps.

[0075] Step S601: Obtain the patrol cost value corresponding to the patrol distance in each closed area.

[0076] In some embodiments of the present application, the area range corresponding to each closed area is obtained. If the area range is smaller than a preset range, it indicates that the preset conditions are not met and a small unfinished area exists. If the area range is greater than or equal to the preset range, it indicates that the preset conditions are met and a large unfinished area exists.

[0077] In one example, if the range of any closed area does not meet the preset conditions, a patrol distance can be set for the closed area, and the patrol cost corresponding to the patrol distance can be obtained. For example, if the first closed area does not meet the preset conditions and the range of the area is d, the patrol distance can be set to w (w>=0 and w≤d). Within the area, the cost value c corresponding to the grid at a distance w from the non-operating area is determined, and the patrol cost value is c.

[0078] In another example, if there is any closed area whose area range meets the preset conditions, since the area where the operation is missed is large, in order to improve efficiency and avoid the recurrence of missed cutting, multiple patrol distances can be set for the closed area based on multiple preset thresholds, and one patrol distance corresponds to one preset threshold. Obtain the patrol cost value corresponding to each patrol distance. For example, if there is a second closed area that meets the preset conditions, and the area range is S, the patrol distances can be set to p1 and p2 (p1, p2>=0 and p1≤S or p2≤S). Within the area range, determine the cost value k1 corresponding to the grid with a distance of P1 from the non-operating area, and determine the cost value k2 corresponding to the grid with a distance of P2 from the non-operating area, then the patrol cost value includes k1 and k2. The above is just an example. The size and number of patrol distances can be customized according to the size of the area range, and this application is not limited to this.

[0079] Step S602: determining a target grid in each closed area based on the patrol cost.

[0080] In some embodiments of the present application, after the patrol cost is determined, the target grid can be determined by searching for the cost value corresponding to each grid in the closed area.

[0081] In one example, if the patrol cost value corresponding to the first closed area includes a cost value c, a grid having a cost value c is searched for from all grids within the first closed area.

[0082] In another example, if the second closed area corresponds to multiple patrol cost values, namely k1 and k2, then a grid having a cost value of k1 and a grid having a cost value of k2 are searched from all grids in the second closed area.

[0083] Step S603: Determine a border patrol path based on the target grid.

[0084] In some embodiments of the present application, after determining the target grid, one or more ordered closed-loop contours can be formed based on the target grid, recorded as patrol contours. The patrol contours are then smoothed to obtain patrol paths. Smoothing methods may include, but are not limited to, local fitting-based smoothing techniques (such as local polynomial least squares fitting), frequency domain filtering techniques (Fourier transform filtering, wavelet transform denoising), median filtering, and Gaussian filtering.

[0085] Taking the local polynomial least squares fitting technique as an example, for each target grid to be fitted, a low-order polynomial (such as quadratic or cubic) is fitted using the target grids within a fixed window before and after it. The value of the fitted polynomial at the center of the window is the smoothed value. The window is then moved, and this process is repeated for each target grid, resulting in a smoothed edge contour.

[0086] like Figure 7 If the area ranges of the two closed areas shown do not meet the preset conditions, then one closed area corresponds to one patrol distance. Assuming that the patrol distances set for the two closed areas are the same, the target grid is recorded as grid a, and all grids a are fitted to obtain the following: Figure 7 The closed area shown corresponds to a patrol contour.

[0087] like Figure 8 If the area ranges of the two closed areas shown meet the preset conditions, then a closed area can correspond to multiple patrol distances. Taking a closed area with two patrol distances as an example, the boundary area between the working area and the non-working area outside the working area is used as the first closed area, and the boundary area between the working area and the obstacle is used as the second closed area. In the first closed area, based on the two patrol distances, the following is formed: Figure 8 In the second closed area, based on the two edge patrol distances, the following Figure 8 The two patrol contours are shown.

[0088] The above embodiment determines the size and number of patrol distances based on the area, improving the efficiency of executing tasks along the patrol path and avoiding missed tasks in large, closed areas. Furthermore, by determining the patrol contour, unfinished areas (such as missed cuts) can be retained, avoiding duplicate tasks.

[0089] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the various embodiments of the control method of the present application.

[0090] The computer-readable storage medium may be an internal storage unit of the mobile device described in the aforementioned embodiment, such as a hard disk or memory of the mobile device. The computer-readable storage medium may also be an external storage device of the mobile device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc., equipped on the mobile device.

[0091] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile device, etc.

[0092] The self-moving device and computer-readable storage medium provided in the aforementioned embodiments can determine the motion compensation information of the self-moving device based on the expected position information and actual position information of the self-moving device when the self-moving device is in a slipping state, and determine the actual speed information of the self-moving device based on the motion compensation information, and control the self-moving device to move according to the actual speed information, thereby providing a basis for the self-moving device to continue to perform subsequent operations.

[0093] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0094] It should also be understood that the term "and / or" used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or system. In the absence of further limitations, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system that includes the element.

[0095] The serial numbers of the embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments. The above description is only a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A control method, characterized in that: The method comprises: Obtain raster maps generated during mobile device operation; determining at least one closed area based on the grid map; Determining a patrol path based on a preset patrol distance in each closed area, wherein the patrol distance is the distance between the self-moving device and the non-operating area; The working task is performed along the patrol path.

2. The control method according to claim 1, characterized in that: The determining of at least one closed area based on the grid map includes: Converting the raster map into a costmap; Determining a first boundary corresponding to the operating area and a second boundary corresponding to the at least one non-operating area based on a cost value corresponding to each grid in the cost map; Based on the first boundary and the second boundary, the at least one closed area constructed by the first boundary and each second boundary is determined.

3. The control method according to claim 2, characterized in that: The determining of the patrol path based on the preset patrol distance in each closed area includes: Obtaining a patrol cost value corresponding to the patrol distance in each closed area; determining a target grid in each closed area based on the patrol cost; The edge patrol path is determined based on the target grid.

4. The control method according to claim 2, characterized in that: The step of converting the grid map into a cost map comprises: Identify the grid map according to a preset semantic segmentation model to determine the operating area and the non-operating area; Based on the association relationship between the working area and each non-working area, the cost value of the grid corresponding to the working area and each non-working area is determined to generate the cost map.

5. The control method according to claim 3, characterized in that: The determining the patrol path based on the target grid includes: Determining at least one edge patrol profile based on the target grid; The at least one edge patrol contour is smoothed to obtain the edge patrol path.

6. The control method according to claim 5, characterized in that: The method further comprises: If there are N non-operating areas, it is determined that there are at least N edge patrol contours, where N is a positive integer.

7. The control method according to claim 1, characterized in that: The determining of the patrol path based on the preset patrol distance in each closed area includes: Determine the area range corresponding to each closed area; When the area range corresponding to any closed area meets a preset condition, based on a plurality of preset thresholds, a plurality of patrol distances are determined in the closed area corresponding to the any closed area, each patrol distance having a corresponding preset threshold; Based on the multiple patrol distances, multiple patrol paths corresponding to any closed area are determined.

8. The control method according to claim 1, characterized in that: The grid map generated during the operation of the mobile device is obtained, including: Obtain a geometric map representing the geometric structure of the environment and a semantic map carrying the semantic labels of the environment; The grid map is determined based on the geometric map and the semantic map.

9. A self-propelled device, characterized in that: The self-mobile device includes a processor and a memory, the memory stores a computer program, and the processor implements the control method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the control method according to any one of claims 1 to 8 is implemented.

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