Cleanable area image establishment method and device, equipment and medium

Abstract

The invention relates to the technical field of cleaning robots, and discloses a cleanable area image establishment method and device, equipment and a medium, and the method comprises the steps: loading an environment map of a position where a cleaning robot is located based on a starting instruction; identifying a user labeling instruction based on the preprocessed environment map; on the basis of a user labeling instruction, a corresponding working area is recognized, and the working area comprises a cleaning area and a special cleaning area included in the cleaning area; performing unit segmentation and / or merging on the working area based on the selected path starting point; path planning is carried out in each unit, and a clean area optimization path is generated; and performing path splicing in each cleaning area based on the optimized path to form a cleaning path diagram. The working efficiency and the cleaning flexibility of the cleaning robot can be improved, and the comprehensiveness and the high efficiency of the cleaning task are ensured.

Description

Technical Field

[0001] The present application relates to the technical field of cleaning robots, and in particular to a method, device, equipment and medium for establishing an image of a cleanable area. Background Art

[0002] With the advancement of technology, smart cleaning robots have become an indispensable part of homes and offices. They can automatically complete tasks such as floor cleaning. However, existing smart cleaning robots usually use pre-set path planning algorithms to complete cleaning tasks, but the global path planning technology in the prior art relies on pre-built map data, and plans the walking path of the cleaning robot through obstacle information on the map. This method takes a long time to build and update map data, especially when the environment changes greatly; it is difficult for users to participate in the path planning process, and it is impossible to flexibly adjust the cleaning area and sequence according to actual needs. In addition, most robots lack user interaction functions, and it is difficult for users to adjust cleaning strategies in real time, such as specifying specific areas for priority cleaning or special treatment of certain areas. Adaptive changes cannot be made according to environmental changes and user's personalized needs.

[0003] Therefore, how to achieve full coverage planning of the cleaning area while increasing the flexibility of the path planning process is an urgent problem to be solved. Summary of the invention

[0004] The main purpose of this application is to provide a method, device, equipment and medium for establishing a cleanable area image, aiming to solve the technical problem of how to achieve full coverage planning of the clean area while increasing the flexibility in the path planning process.

[0005] The first aspect of the present application provides a method for establishing a cleanable area image, comprising:

[0006] Based on the startup instruction, load the environmental map of the location of the cleaning robot;

[0007] Based on the preprocessed environment map, identify the user's annotation instructions;

[0008] Based on the user marking instruction, identifying the corresponding work area, wherein the work area includes a cleaning area and a special cleaning area included in the cleaning area;

[0009] Splitting and / or merging units of the work area based on the selected path starting point;

[0010] Perform path planning in each unit and generate optimized paths for the cleaning area;

[0011] Based on the optimized path, the paths are spliced ​​in each cleaning area to form a cleaning path map.

[0012] Furthermore, the step of loading the environment map of the location of the cleaning robot based on the startup instruction includes:

[0013] Identify the start command issued by the user and use the built-in positioning system to determine the current location of the cleaning robot;

[0014] Searching the robot's internal storage or a cloud database to see whether there is an environment map corresponding to the location;

[0015] If there is no corresponding environment map, enter the map creation process;

[0016] If a corresponding environment map exists, the map of the environment where the cleaning robot is located is loaded.

[0017] Furthermore, the step of identifying the user's annotation instruction based on the preprocessed environment map includes:

[0018] Performing a binarization operation on the environment map to obtain a preprocessed environment map;

[0019] Identify the user's marking instructions, where the marking instructions are for the user to interact through the cleaning robot's control panel or a supporting application, and mark and divide the map of the cleaning robot's environment during the interaction.

[0020] Furthermore, after the step of identifying the user's annotation instruction based on the preprocessed environment map, the following steps are included:

[0021] Based on the user's marking instructions, identify the prohibited area and the eraser area marked by the user, wherein the eraser area is used to erase the active obstacle information on the environment map and the noise information in the mapping process;

[0022] Identify obstacles in the map and perform erosion and dilation on them.

[0023] Furthermore, the step of dividing and / or merging the workspace into units based on the selected path starting point includes:

[0024] Identify the starting point of the path selected by the user;

[0025] Based on the path starting point, the workspace is divided into several units in combination with its location and shape;

[0026] If adjacent cells meet the preset merging conditions, the adjacent cells are merged.

[0027] Furthermore, the step of performing path planning in each unit and generating an optimized path for the cleaning area includes:

[0028] Determine the order of visiting cells based on the path starting point;

[0029] Based on the access order of the cells, a planning path is generated within the cells through a preset algorithm;

[0030] According to the access order, the planned paths in each unit are connected to form the planned path of the clean area;

[0031] Optimize the planned path in the cleaning area and generate an optimized path.

[0032] Furthermore, after the step of performing path splicing in each cleaning area based on the optimized path to form a cleaning path map, the following steps are included:

[0033] Identify the real-time cleaning location of the current cleaning robot;

[0034] If the real-time cleaning position enters the special cleaning area, obtaining a cleaning mode corresponding to the special cleaning area;

[0035] The special cleaning area is cleaned based on the cleaning mode.

[0036] A second aspect of the present application provides a cleanable area image creation device, comprising:

[0037] A loading module, used to load an environment map of the location of the cleaning robot based on a startup instruction;

[0038] A first recognition module, used for recognizing user marking instructions based on the preprocessed environment map;

[0039] A second identification module is used to identify a corresponding work area based on a user marking instruction, wherein the work area includes a cleaning area and a special cleaning area included in the cleaning area;

[0040] A unit processing module, for performing unit segmentation and / or merging of the work area based on the selected path starting point;

[0041] The path generation module is used to plan the path in each unit and generate the optimized path of the cleaning area;

[0042] The splicing module is used to perform path splicing in each cleaning area based on the optimized path to form a cleaning path map.

[0043] A third aspect of the present application provides a computer device, comprising:

[0044] processor;

[0045] Memory for storing computer programs;

[0046] Wherein, when the processor executes the computer program, any of the above-mentioned methods for establishing the cleanable area image is implemented.

[0047] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the cleanable area image establishment method described in any one of the above items is implemented.

[0048] Beneficial effects:

[0049] The present application can effectively respond to the user's start-up instructions and automatically load the environmental map of the cleaning robot's location. By preprocessing and analyzing the environmental map, the system can accurately identify the instructions marked by the user on the map, and then clarify which areas are designated as cleaning areas and special cleaning areas therein. The system can also intelligently divide the cleaning area into several units according to the starting point selected by the user, and merge these units when necessary to improve cleaning efficiency. Subsequently, the optimal cleaning path is generated in each unit, and these paths are connected to form a complete path map of the entire cleaning area. This method not only improves the working efficiency of the cleaning robot and the flexibility of cleaning, but also ensures the comprehensiveness and efficiency of the cleaning task. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a schematic diagram of the steps of a method for establishing a cleanable area image according to an embodiment of the present application;

[0051] Figure 2 is a schematic block diagram of the structure of a cleanable area image establishment device according to an embodiment of the present application;

[0052] Figure 3 is a schematic block diagram of the structure of a computer device according to an embodiment of the present application;

[0053] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] It will be appreciated by those skilled in the art that, unless expressly stated, the singular forms "one", "above", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of features, integers, steps, operations, elements, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when an element is said to be "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may also be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any module and all combinations of one or more associated listed items.

[0056] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0057] Reference Figure 1 , the embodiment of the present application provides a method for establishing a cleanable area image, comprising the following steps S1-S5:

[0058] S1. Based on the start-up instruction, load the environment map of the location of the cleaning robot;

[0059] S2, based on the preprocessed environment map, identifying the user's annotation instructions;

[0060] S3. Based on the user's marking instruction, identifying the corresponding work area, wherein the work area includes a cleaning area and a special cleaning area included in the cleaning area;

[0061] S4, dividing and / or merging the work area into units based on the selected path starting point;

[0062] S5. Perform path planning in each unit and generate an optimized path for the cleaning area;

[0063] S6. Based on the optimized path, the paths are spliced ​​in each cleaning area to form a cleaning path map.

[0064] In step S1, the user can trigger a start command through the control panel, mobile application or other remote control device of the cleaning robot. The start command may contain some additional information, such as the selection of cleaning mode (such as normal cleaning, deep cleaning, etc.). The cleaning robot is equipped with a variety of sensors for positioning, including but not limited to laser radar (LiDAR), ultrasonic sensor, infrared sensor, etc. After receiving the start command, these sensors start working to obtain the current position information of the robot. According to the current position information of the robot, a pre-built environment map is read from the memory. The environment map can be constructed by the robot's autonomous exploration when it is used for the first time. For example, the environment map can be obtained by laser slam (Simultaneous Localization and Mapping) technology, or it can be manually drawn by the user or obtained by other means. The map data contains the layout of the room, the location of obstacles and other feature information related to cleaning. The loaded environment map data needs to be preprocessed, including data calibration, map update, and matching of the map with the current position of the robot. The purpose of preprocessing is to ensure the accuracy of the map data so as to facilitate path planning and cleaning task execution in subsequent steps. By loading the environmental map, the robot can quickly understand the layout of the current environment, thereby reducing unnecessary exploration time and improving cleaning efficiency. The loading and preprocessing process of the environmental map provides the basis for subsequent steps, enabling the robot to respond adaptively according to environmental changes.

[0065] In step S2, the robot needs to identify the cleaning instructions marked by the user. The robot first loads the pre-processed environmental map data, which has been calibrated and updated to ensure the accuracy of the map. The environmental map is displayed to the user through the control panel of the cleaning robot or the interface such as the mobile application. The user can view the layout of the entire environment through this interface and mark the areas that need to be cleaned. The user can mark on the displayed environmental map, for example, directly circle the areas that need to be cleaned, the areas that are prohibited from traveling, etc. on the map through the touch screen, or mark special areas that need special treatment (such as carpets, near fragile items, etc.). The user can also set the cleaning priority, such as specifying that a certain area needs to be cleaned first. The cleaning robot recognizes the user's marking instructions through the built-in software algorithm, which may include text labels, color coding or other forms of markings. Once the recognition is successful, the robot converts these instructions into executable operation commands. After the recognition is completed, the robot saves the user's marking information as an important basis for subsequent steps (such as path planning). By clearly marking, the robot can quickly identify key areas and avoid unnecessary repeated cleaning, thereby improving cleaning efficiency. Users can flexibly specify cleaning areas and priorities according to personal needs to achieve more personalized cleaning services. Through user participation, the cleaning robot can better adapt to changing environments and personal preferences, improving the intelligence level of the system. Throughout the process, users can change the labels at any time, allowing the cleaning robot to flexibly respond to emergencies or special needs.

[0066] In step S3, the robot needs to identify the corresponding work area according to these instructions, that is, the area to be cleaned and the special cleaning area inside it. The cleaning robot first loads the user annotation instructions saved in step S2, which contain the cleaning area specified by the user and its priority information. The user's annotation instructions are parsed through the built-in software algorithm to extract key information such as the location, shape and priority of the cleaning area. Based on the extracted information, the robot identifies the cleaning area marked on the map and defines it as a work area. The work area may include multiple independent areas. In each work area, the robot further identifies the special cleaning area marked by the user, such as the carpet area, the area near fragile items, etc. These special areas require special cleaning strategies. Finally, the robot saves the information of the identified work area and special cleaning area as an important basis for the next path planning. By identifying the special cleaning area, the robot can adopt different cleaning strategies for different materials and areas, which enhances the targetedness of cleaning. By clearly identifying the work area, the robot can clean in a targeted manner to avoid ineffective movement and cleaning. Users can mark special cleaning areas according to their needs, so that the cleaning process is more in line with user expectations and improves the user experience.

[0067] In step S4, the cleaning robot first loads the recognition results saved in step S3, including information about the work area and the special cleaning area. According to the user's marking instructions or preset rules, the robot determines one or more path starting points. The selection of the path starting point is usually based on cleaning efficiency and user needs. The robot divides the work area into several smaller unit areas, each of which is an independent cleaning task. This segmentation helps the robot manage cleaning tasks more efficiently and better adapt to environmental changes. In some cases, adjacent unit areas may share similar cleaning requirements (such as the same floor material), in which case they can be merged into a larger unit area to simplify path planning and improve cleaning efficiency. The robot saves the results of unit segmentation and / or merging as an important basis for the next step of path planning. Through unit segmentation and merging, the robot can plan the path more reasonably, reduce unnecessary movement, and improve cleaning efficiency. By dynamically adjusting the size of the unit area, the robot can better adapt to environmental changes and personal preferences. Unit segmentation and merging can be adjusted according to the characteristics of different areas, enhancing the targetedness of cleaning.

[0068] In step S5, the built-in path planning algorithm is enabled, such as using the ox-ploughing method. For each unit area, the robot plans an optimal cleaning path according to its characteristics (such as ground material, obstacle distribution, etc.) and the special cleaning needs marked by the user. This path needs to cover the entire unit area as much as possible, while taking into account cleaning efficiency and avoiding collisions. After completing the path planning in each unit, the robot connects these paths to form an overall cleaning path map. During the connection process, the robot also needs to consider the smooth transition between the paths and the total length of the path to ensure the efficiency of the entire cleaning process. Finally, the robot saves the generated optimized path as the basis for the next cleaning operation. By planning the optimal path in each unit area, the robot can reduce unnecessary movement and improve cleaning efficiency. The optimized path reduces the ineffective movement of the robot, helps to reduce energy consumption, and the optimized path can be adjusted according to the characteristics of the unit area, enhancing the pertinence of cleaning.

[0069] Before entering step S6, the robot has completed the path planning work for each independent cleaning area and obtained the optimized path in each area. In step S6, before the path splicing is performed, each optimized path needs to be preprocessed, such as checking whether there is an overlapping part or whether the distance between adjacent paths is close enough for connection. For two adjacent cleaning areas, it is necessary to find the best connection point between them, that is, the path splicing point. The selection of these points should take into account factors such as the robot's turning radius and cleaning efficiency to ensure that the spliced ​​path is still efficient. Once the splicing point is determined, the robot needs to calculate the best transition path from one cleaning area to another. This process may involve straight-line movement, turning or other maneuvers to ensure that the robot can move smoothly from one area to another. In order to ensure that the robot is stable and does not undergo drastic movement changes during movement, the transition path can also be smoothed to avoid sharp turns or sudden stops. Finally, all the spliced ​​and smoothed paths will be integrated into a complete cleaning path map. The path splicing strategy can be flexibly adjusted according to the characteristics of different cleaning areas. This map contains all the path information that the robot executes in sequence throughout the cleaning area. After the cleaning path map is formed, it needs to be verified and optimized as necessary. The purpose of verification is to ensure the feasibility and effectiveness of the path, while the purpose of optimization is to further improve the cleaning efficiency. Through effective path splicing, the ineffective movement of the robot can be reduced and the cleaning efficiency can be improved. A reasonable path splicing strategy can reduce the number of repeated cleanings in the same area.

[0070] In one embodiment, based on the start-up instruction, the step of loading the environment map of the location of the cleaning robot includes:

[0071] S10, recognizing that the user issues a start command, and using the built-in positioning system to determine the current position of the cleaning robot;

[0072] S11, searching in the robot's internal storage or cloud database whether there is an environment map corresponding to the location;

[0073] S12. If there is no corresponding environment map, enter the map creation process;

[0074] S13: If a corresponding environment map exists, then the map of the environment where the cleaning robot is located is loaded.

[0075] In this embodiment, the cleaning robot receives a start-up instruction from the user end through its built-in control system. These instructions may be issued through a remote control, a mobile application, or other user interface. Once the start-up instruction is received, the cleaning robot immediately activates its positioning system, which usually includes a laser radar (LiDAR), a visual sensor, a gyroscope, an accelerometer, etc., to determine the current position and direction of the robot. Through the data processing of the positioning system, the robot can determine its exact position in the environment. The cleaning robot either saves the environmental map data in its internal memory or connects to a cloud database through a wireless network, the latter of which usually contains map data of multiple different environments. Based on the current location information, the robot searches for map data of the corresponding environment in the internal memory or cloud database. If matching map data is found, it proceeds to the next step; if not, a new map needs to be created. If matching map data is found in the internal memory or cloud database, the robot will load this map. The loaded map data needs to be preprocessed to ensure its accuracy and applicability. By loading the environmental map, the robot can quickly understand the layout of the current environment, thereby reducing unnecessary exploration time and improving cleaning efficiency. The robot can load the corresponding environmental map according to the specific location, which is suitable for a changing home or office environment. The loading and preprocessing of the environment map provides the basis for subsequent steps, enabling the robot to respond adaptively to environmental changes.

[0076] In one embodiment, the step of identifying the user's annotation instruction based on the preprocessed environment map includes:

[0077] S20, performing a binarization operation on the environment map to obtain a preprocessed environment map;

[0078] S21. Identify the user's labeling instructions, wherein the labeling instructions are for the user to interact through the control panel of the cleaning robot or a supporting application, and to label and divide the map of the environment where the cleaning robot is located during the interaction.

[0079] In this embodiment, the environment map is binarized, that is, the pixels in the map are converted into black and white, usually black represents obstacles or impassable areas, and white represents passable areas. This process helps to simplify the map data and make it more suitable for subsequent processing and analysis. Select a suitable threshold to distinguish obstacles and passable areas in the map to ensure that the binarized map can accurately reflect the actual layout of the environment. Generate a binarized environment map to prepare for subsequent user labeling instruction recognition. The user interacts through the control panel of the cleaning robot or the supporting application to mark and divide the map of the environment where the cleaning robot is located. The cleaning robot recognizes the user's labeling instructions on the map through a built-in software algorithm. These instructions may include text labels, color coding or other forms of markings. Once the recognition is successful, the robot converts these instructions into executable operation commands.

[0080] In one embodiment, after the step of identifying the user's annotation instruction based on the pre-processed environment map, the following steps are included:

[0081] S30, based on the user's marking instruction, identifying the prohibited area and the eraser area marked by the user, wherein the eraser area is used to erase the active obstacle information on the environment map and the noise information in the mapping process;

[0082] S31. Identify obstacles in the map and perform corrosion and expansion on the obstacles.

[0083] In this embodiment, the user may mark some areas on the map, instructing the robot not to enter or temporarily not clean. These areas are called "no-go areas". Similar to no-go areas, but more flexible, users can temporarily cancel the cleaning of certain areas, which are called "eraser areas". By recognizing the user's marking instructions, the cleaning robot can accurately identify which areas are marked as no-go areas or eraser areas by the user. The positions of physical obstacles such as furniture and walls are identified on the environmental map. Through corrosion (reducing the edge of the obstacle to prevent the robot from getting too close to the obstacle and causing a collision) and expansion operations (expanding the edge of the obstacle to ensure that the robot has enough space to bypass the obstacle and avoid entering a narrow area by mistake) in image processing, the obstacle boundary is adjusted to ensure that the robot can avoid these obstacles. By corroding and expanding the obstacles, the cleaning robot can avoid obstacles more accurately and reduce the risk of collision. After identifying the no-go area and eraser area, the robot can plan the cleaning path more flexibly to avoid unnecessary cleaning areas. The user can control the behavior of the robot more meticulously, enhancing the user-friendliness of the product.

[0084] In one embodiment, the step of dividing and / or merging the workspace into units based on the selected path starting point includes:

[0085] S40, identifying the starting point of the path selected by the user;

[0086] S41, based on the path starting point, the work area is divided into a number of units in combination with the position and shape;

[0087] S42: If the adjacent units meet the preset merging condition, merge the adjacent units.

[0088] In this embodiment, the user can select one or more path starting points through the control panel of the cleaning robot or the supporting application. These starting points are usually the locations where the robot starts the cleaning task. According to the user's marking instructions, the work area that needs to be cleaned is identified, and these work areas may include multiple independent areas. Each work area is divided into several units, each unit is a smaller cleaning area, which is convenient for the robot to carry out more detailed path planning. When dividing the units, it is necessary to consider the location, shape and special needs of the work area (for example, whether a certain area, such as a blanket area, etc.) of the user. The preset merging conditions may include factors such as the similarity between units (such as the same ground material), the size of the unit, and the cleaning efficiency. If adjacent units meet the merging conditions, they are merged into a larger unit to simplify path planning and improve cleaning efficiency. By merging units, the complexity of path planning can be reduced while ensuring that the robot can complete the cleaning task more efficiently. Through unit division and merging, the robot can manage cleaning tasks more efficiently and reduce unnecessary movement. Unit division enables the robot to better allocate resources, such as allocating more time and energy in special cleaning areas. By dynamically adjusting the size of the unit area, the robot can better adapt to environmental changes and personal preferences.

[0089] In one embodiment, the step of performing path planning in each unit and generating an optimized path for the cleaning area includes:

[0090] S50, determining the access order of the units based on the path starting point;

[0091] S51, based on the access sequence of the unit, generating a planning path in the unit by a preset algorithm;

[0092] S52, connecting the planned paths in each unit according to the access order to form a clean area planned path;

[0093] S53, optimizing the planned path of the cleaning area to generate an optimized path.

[0094] In this embodiment, first, the working starting position of the cleaning robot is determined according to the path starting point selected by the user, and the access order of each unit is determined accordingly. The access order may be determined based on the principle of the shortest path or the least number of revolutions to reduce the ineffective movement of the robot. In each unit, an effective cleaning path is generated using the ox-till method. When planning the path, factors such as obstacle avoidance and cleaning efficiency should also be considered. The planned paths in each unit are connected according to the access order to form a continuous path for the entire cleaning area, ensuring that the robot can smoothly transition to the next unit and avoid repeated paths or ineffective movement. The path is further optimized by an optimization algorithm to reduce the total moving distance or cleaning time. The optimization method can adopt heuristic optimization methods such as genetic algorithms and simulated annealing algorithms to achieve optimization goals such as minimizing the total walking distance, reducing the number of revolutions, and maximizing the cleaning efficiency. Through the above steps, the cleaning robot can efficiently complete the cleaning task, ensuring that the cleaning path is both effective and economical, while improving the cleaning efficiency and reducing energy consumption. In addition, this intelligent path planning method enhances the flexibility and reliability of the robot, making the cleaning work more automated and intelligent.

[0095] In one embodiment, after the step of performing path splicing in each cleaning area based on the optimized path to form a cleaning path map, the method includes:

[0096] S60, identifying the real-time cleaning position of the current cleaning robot;

[0097] S61, if the real-time cleaning position enters the special cleaning area, obtaining a cleaning mode corresponding to the special cleaning area;

[0098] S62: Clean the special cleaning area based on the cleaning mode.

[0099] In this embodiment, the cleaning robot uses built-in sensors and positioning technology (such as laser radar, visual navigation system, etc.) to monitor and determine its current position in the cleaning area in real time. This helps the robot adjust the cleaning strategy according to the actual cleaning situation. When the cleaning robot detects that it has entered a predefined special cleaning area, it will automatically switch to a specific cleaning mode set for the area. Special cleaning areas can be areas that require more detailed or more frequent cleaning, such as kitchens, pet activity areas, etc. It can also be cleaning of floors made of special materials. In this case, it may be necessary to change the cleaning mode and switch the cleaning utensils of the cleaning robot, such as blankets and floors. Different cleaning methods and utensils are required. According to the special cleaning mode obtained, the cleaning robot adjusts its cleaning behavior, such as increasing suction, changing the cleaning frequency, or using different cleaning utensils to ensure that the special cleaning area is properly cleaned. Through these steps, the cleaning robot can not only effectively complete routine cleaning tasks, but also flexibly adjust the cleaning strategy according to the specific needs of different areas, thereby improving the overall cleaning effect and user experience. This intelligent cleaning solution enables the robot to work more autonomously and efficiently when facing complex environments.

[0100] refer to Figure 2 , a cleanable area image establishing device, comprising:

[0101] The loading module 100 is used to load the environment map of the location of the cleaning robot based on the start-up instruction;

[0102] A first recognition module 200, for recognizing a user's annotation instruction based on the preprocessed environment map;

[0103] The second identification module 300 is used to identify the corresponding work area based on the user's marking instruction, wherein the work area includes a cleaning area and a special cleaning area included in the cleaning area;

[0104] The unit processing module 400 is used to perform unit segmentation and / or merging of the work area based on the selected path starting point;

[0105] A path generation module 500 is used to perform path planning in each unit and generate an optimized path for the cleaning area;

[0106] The splicing module 600 is used to perform path splicing in each cleaning area based on the optimized path to form a cleaning path map.

[0107] Furthermore, the loading module 100 includes:

[0108] The start and positioning unit is used to identify the start command issued by the user and determine the current position of the cleaning robot using the built-in positioning system;

[0109] A map retrieval unit, used to search in the robot's internal storage or a cloud database whether there is an environment map corresponding to the location;

[0110] A map creation determination unit, used to enter a map creation process if a corresponding environment map does not exist;

[0111] A map loading unit is used to load the map of the environment where the cleaning robot is located if a corresponding environment map exists.

[0112] Furthermore, the first identification module 200 includes:

[0113] A map preprocessing unit, used for performing a binarization operation on the environment map to obtain a preprocessed environment map;

[0114] The user marking recognition unit is used to recognize the user's marking instructions, wherein the marking instructions are the marking and division of the map of the environment in which the cleaning robot is located during the user's interaction through the control panel of the cleaning robot or the supporting application.

[0115] Furthermore, the second identification module 300 includes:

[0116] A prohibited area recognition unit, used to recognize prohibited areas and eraser areas marked by the user based on the user's marking instructions, wherein the eraser area is used to erase the active obstacle information on the environment map and the noise information in the mapping process;

[0117] The obstacle processing unit is used to identify obstacles in the map and perform corrosion and expansion on the obstacles.

[0118] Furthermore, the unit processing module 400 includes:

[0119] A starting point identification unit, used to identify the starting point of the path selected by the user;

[0120] The unit division unit is used to divide the work area into several units based on the path starting point and the position and shape of the work area;

[0121] The unit merging determination unit is used to merge adjacent units if the adjacent units meet a preset merging condition.

[0122] Furthermore, the path generation module 500 includes:

[0123] An access sequence determination unit, for determining an access sequence of the units based on a path starting point;

[0124] A planning path generation unit, used to generate a planning path in the unit by a preset algorithm based on the access sequence of the unit;

[0125] A path connection unit is used to connect the planned paths in each unit according to the access order to form a cleaning area planned path;

[0126] The path optimization unit is used to optimize the planned path in the cleaning area and generate an optimized path.

[0127] In one embodiment, the above device further comprises a special cleaning module, and the special cleaning module comprises:

[0128] A real-time position recognition unit, used to recognize the real-time cleaning position of the current cleaning robot;

[0129] A special cleaning area mode acquisition unit, configured to acquire a cleaning mode corresponding to the special cleaning area if the real-time cleaning position enters the special cleaning area;

[0130] The special cleaning execution unit is used to clean the special cleaning area based on the cleaning mode.

[0131] Reference Figure 3 In an embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface and a database connected by a bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores operations, computer programs and databases. The memory provides an environment for the operation of the non-volatile storage medium and the computer program. The database of the computer device is used to store data such as a method for establishing a cleanable area image. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for establishing a cleanable area image is implemented, including the steps of: based on a startup instruction, loading an environmental map of the location of the cleaning robot; based on the pre-processed environmental map, identifying a user marking instruction; based on the user marking instruction, identifying a corresponding work area, wherein the work area includes a cleaning area and a special cleaning area included in the cleaning area; based on the selected path starting point, the work area is divided into units and / or merged; path planning is performed in each unit, and an optimized path for the cleaning area is generated; based on the optimized path, path splicing is performed in each cleaning area to form a cleaning path map.

[0132] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a method for establishing an image of a cleanable area is implemented, comprising the following steps: based on a startup instruction, loading an environmental map of the location of the cleaning robot; based on a preprocessed environmental map, identifying a user labeling instruction; based on the user labeling instruction, identifying a corresponding work area, wherein the work area includes a cleaning area and a special cleaning area included in the cleaning area; dividing and / or merging the work area into units based on a selected path starting point; performing path planning in each unit and generating an optimized path for the cleaning area; and performing path splicing in each cleaning area based on the optimized path to form a cleaning path map.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in the present application and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0134] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for establishing a cleanable area image, characterized in that: include: Based on the startup instruction, load the environmental map of the location of the cleaning robot; Based on the preprocessed environment map, identify the user's annotation instructions; Based on the user marking instruction, identifying the corresponding work area, wherein the work area includes a cleaning area and a special cleaning area included in the cleaning area; Splitting and / or merging units of the work area based on the selected path starting point; Perform path planning in each unit and generate optimized paths for the cleaning area; Based on the optimized path, the paths are spliced ​​in each cleaning area to form a cleaning path map.

2. The cleanable area image creation method according to claim 1, characterized in that: The step of loading the environment map of the location of the cleaning robot based on the startup instruction includes: Identify the start command issued by the user and use the built-in positioning system to determine the current location of the cleaning robot; Searching the robot's internal storage or a cloud database to see whether there is an environment map corresponding to the location; If there is no corresponding environment map, enter the map creation process; If a corresponding environment map exists, the map of the environment where the cleaning robot is located is loaded.

3. The cleanable area image creation method according to claim 1, characterized in that: The step of identifying the user's annotation instruction based on the preprocessed environment map includes: Performing a binarization operation on the environment map to obtain a preprocessed environment map; Identify the user's marking instructions, where the marking instructions are for the user to interact through the cleaning robot's control panel or a supporting application, and mark and divide the map of the cleaning robot's environment during the interaction.

4. The cleanable area image creation method according to claim 1, characterized in that: After the step of identifying the user's annotation instruction based on the preprocessed environment map, the method further includes: Based on the user's marking instructions, identify the prohibited area and the eraser area marked by the user, wherein the eraser area is used to erase the active obstacle information on the environment map and the noise information in the mapping process; Identify obstacles in the map and perform erosion and dilation on them.

5. The cleanable area image creation method according to claim 1, characterized in that: The step of dividing and / or merging the workspace into units based on the selected path starting point comprises: Identify the starting point of the path selected by the user; Based on the path starting point, the workspace is divided into several units in combination with its location and shape; If adjacent cells meet the preset merging conditions, the adjacent cells are merged.

6. The cleanable area image creation method according to claim 1, characterized in that: The step of performing path planning in each unit and generating an optimized path for the cleaning area includes: Determine the order of visiting cells based on the path starting point; Based on the access order of the cells, a planning path is generated within the cells through a preset algorithm; According to the access order, the planned paths in each unit are connected to form the planned path of the clean area; Optimize the planned path in the cleaning area and generate an optimized path.

7. The cleanable area image creation method according to claim 1, characterized in that: After the step of performing path splicing in each cleaning area based on the optimized path to form a cleaning path map, the method further comprises: Identify the real-time cleaning location of the current cleaning robot; If the real-time cleaning position enters the special cleaning area, obtaining a cleaning mode corresponding to the special cleaning area; The special cleaning area is cleaned based on the cleaning mode.

8. A cleanable area image creation device, characterized in that: include: A loading module, used to load an environment map of the location of the cleaning robot based on a startup instruction; A first recognition module, used for recognizing user marking instructions based on the preprocessed environment map; A second identification module is used to identify a corresponding work area based on a user marking instruction, wherein the work area includes a cleaning area and a special cleaning area included in the cleaning area; A unit processing module, for performing unit segmentation and / or merging of the work area based on the selected path starting point; The path generation module is used to plan the path in each unit and generate the optimized path of the cleaning area; The splicing module is used to perform path splicing in each cleaning area based on the optimized path to form a cleaning path map.

9. A computer device, characterized in that: include: processor; Memory for storing computer programs; Wherein, when the processor executes the computer program, the cleanable area image establishing method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for establishing a cleanable area image according to any one of claims 1 to 7 is implemented.

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