Control method for cleaning equipment and cleaning equipment

Abstract

The embodiment of the invention provides a control method of cleaning equipment and the cleaning equipment, and relates to the technical field of intelligent control. The method comprises the steps that accessory bin state identity information of an accessory bin is collected, and the accessory bin is used for storing cleaning accessories for assisting cleaning equipment in cleaning; detecting whether the accessory bin is at a preset position based on the accessory bin state identity information, and obtaining an in-place detection result; and generating a cleaning strategy based on the in-place detection result, and executing a cleaning task according to the cleaning strategy. According to the method, high-reliability accessory bin in-place detection can be achieved by collecting and using the state identity information of the accessory bin, then the cleaning strategy beneficial to assisting the cleaning equipment in cleaning can be generated according to the in-place detection result, and the cleanliness and the cleaning efficiency can be improved when the cleaning equipment executes the cleaning task.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202512041276.5 (22) Application Date 2025.12.30 (71) Applicant: Zhuimi Innovation Technology (Suzhou) Co., Ltd. Address: Units 1, 2, and 3, Building 8, No. 1688, Songwei Road, Guoxiang Street, Wuzhong Economic Development Zone, Suzhou City, Jiangsu Province, 215000 Applicant: Zhuimi Intelligent Technology (Shaoxing) Co., Ltd. (72) Inventors: Shen Xinyue, Tu Zhenguan, Xing Junfeng, Zhao Liwa, Xu Haijun, Wang Hongtao, Nie Xiaoyun (74) Patent Agency: Beijing Tongli Juncheng Intellectual Property Agency Co., Ltd. 11205 Patent Attorney: Wang Peng (51) Int.Cl. A47L 11 / 40 (2006.01) A47L 11 / 24 (2006.01) (54) Invention Title: Control Method and Cleaning Equipment for Cleaning Equipment (57) Abstract: This application provides a control method and cleaning equipment for cleaning equipment, relating to the field of intelligent control technology. The method includes: collecting the status and identity information of a parts compartment, the parts compartment being used to store cleaning parts for assisting the cleaning equipment; detecting whether the parts compartment is in a preset position based on the status and identity information of the parts compartment, and obtaining an on-site detection result; generating a cleaning strategy based on the on-site detection result, and executing a cleaning task according to the cleaning strategy. This method can achieve highly reliable on-site detection of the parts compartment by collecting and using the status and identity information of the parts compartment, and can then generate a cleaning strategy that is beneficial to assisting the cleaning equipment based on the on-site detection result, thereby improving the cleanliness and cleaning efficiency of the cleaning equipment when performing cleaning tasks. Claims (3 pages), Description (26 pages), Drawings (4 pages), CN 121489359 A 2026.02.10 CN 1 21 48 93 59 A 1. A control method for a cleaning device, characterized in that the method includes: collecting accessory storage status identity information of an accessory storage compartment, the accessory storage compartment being used to store cleaning accessories that assist the cleaning device in cleaning; detecting whether the accessory storage compartment is in a preset position based on the accessory storage status identity information, and obtaining an on-site detection result; generating a cleaning strategy based on the on-site detection result, and executing a cleaning task according to the cleaning strategy. 2. The method according to claim 1, characterized in that detecting whether the accessory storage compartment is in a preset position based on the accessory storage status identity information and obtaining an on-site detection result includes: matching the accessory storage status identity information with a preset accessory storage feature model, the accessory storage feature model being a model constructed based on features extracted when the accessory storage compartment is in a preset position; if the matching is successful, obtaining an on-site detection result indicating that the accessory storage compartment is in the preset position; if the matching fails, obtaining an on-site detection result indicating that the accessory storage compartment is not in the preset position.3. The method according to claim 2, wherein the accessory storage status identity information includes a unique identifier of the accessory storage, and the accessory storage feature model includes an identifier feature extracted from the unique identifier of the accessory storage; the step of matching the accessory storage status identity information with a preset accessory storage feature model includes: matching the unique identifier of the accessory storage with the identifier feature of the accessory storage feature model. 4. The method according to claim 2, wherein the accessory storage status identity information includes the spatial pose of the accessory storage, and the accessory storage feature model includes the in-situ spatial pose range of the accessory storage; the step of matching the accessory storage status identity information with a preset accessory storage feature model includes: matching the spatial pose of the accessory storage with the in-situ spatial pose range of the accessory storage feature model. 5. The method according to any one of claims 1-4, wherein the step of generating a cleaning strategy based on the in-situ detection result includes: generating a cleaning strategy based on the cleaning accessories in the accessory storage when the in-situ detection result indicates that the accessory storage is in a preset position. 6. The method according to claim 5, wherein generating a cleaning strategy based on cleaning accessories in the accessory compartment comprises: identifying at least one cleaning accessory in the accessory compartment and determining the type of the at least one cleaning accessory; generating the cleaning strategy based on cleaning task requirements and the type of the at least one cleaning accessory. 7. The method according to claim 6, wherein identifying at least one cleaning accessory in the accessory compartment and determining the type of the at least one cleaning accessory comprises: identifying a unique accessory identifier of at least one cleaning accessory in the accessory compartment and determining the type of the at least one cleaning accessory based on the unique accessory identifier. 8. The method according to claim 6, wherein identifying at least one cleaning accessory in the accessory compartment and determining the type of the at least one cleaning accessory comprises: scanning a near-field communication (NFC) tag on at least one cleaning accessory in the accessory compartment using a NFC reader installed on the cleaning device; identifying at least one cleaning accessory in the accessory compartment and determining the type of the at least one cleaning accessory based on the data interaction identification result between the NFC reader and the NFC tag. 9. The method according to claim 6, wherein the cleaning strategy includes an accessory grasping parameter set, and the accessory grasping parameter set includes at least one grasping action parameter for grasping the cleaning accessory; generating the cleaning strategy according to the cleaning task requirements and the type of the at least one cleaning accessory includes: determining a target cleaning accessory that meets the cleaning task requirements according to the cleaning task requirements and the type of the at least one cleaning accessory;Based on the relative pose between the cleaning device and the target cleaning accessory, the grasping action parameters for grasping the target cleaning accessory are calculated, and an accessory grasping parameter set for the cleaning strategy is generated. 10. The method according to claim 9, characterized in that the method further includes: grasping the target cleaning accessory by the grasping device of the cleaning device according to the accessory grasping parameter set; during the grasping process, detecting the grasping opening degree of the grasping device by the position encoder of the grasping device; determining whether the grasping device has grasped the target cleaning accessory based on the grasping opening degree. 11. The method according to any one of claims 1-4, characterized in that the step of generating a cleaning strategy based on the in-situ detection result includes: when the in-situ detection result indicates that the accessory compartment is not in a preset position, collecting environmental perception data of the environment in which the cleaning device perceives its surroundings; based on the environmental perception data, identifying whether a cleaning tool exists in the environment through a visual model, the cleaning tool being a tool used to assist in cleaning; generating the cleaning strategy based on the cleaning tool identified in the environment. 12. The method according to claim 11, wherein generating the cleaning strategy based on the cleaning tools identified in the environment comprises: determining the tool type of the cleaning tools identified in the environment using the visual model; and generating the cleaning strategy based on the cleaning task requirements and the tool type. 13. The method according to claim 12, wherein generating the cleaning strategy based on the cleaning task requirements and the tool type comprises: determining whether the tool type meets the cleaning task requirements if the cleaning task requirements include cleaning blind spots; and generating a cleaning strategy that includes cleaning blind spots based on the tool type if the tool type meets the cleaning task requirements. 14. The method according to claim 12, wherein the cleaning strategy includes a tool grasping parameter set, the tool grasping parameter set including at least one grasping action parameter for grasping the cleaning tool; generating the cleaning strategy according to the cleaning task requirements and the tool type includes: when multiple cleaning tools of multiple tool types are identified, determining a target cleaning tool that meets the cleaning task requirements according to the cleaning task requirements and the tool types of the multiple cleaning tools; calculating the grasping action parameters for grasping the target cleaning tool according to the relative pose between the cleaning device and the target cleaning tool, and generating the tool grasping parameter set of the cleaning strategy. 15. The method according to claim 14, further comprising: grasping the target cleaning tool using the grasping device of the cleaning device according to the tool grasping parameter set; during the grasping process, detecting the grasping opening degree of the grasping device using a position encoder of the grasping device;Based on the grasping opening degree, determine whether the grasping device has grasped the target cleaning tool. 16. The method according to any one of claims 1-4, characterized in that, generating a cleaning strategy based on the in-situ detection result includes: when the in-situ detection result indicates that the accessory compartment is not in a preset position, calling the environmental map of the cleaning equipment, the environmental map being obtained by the cleaning equipment after spatial scanning and map construction of its environment, the environmental map including annotation information of obstacles; determining at least one cleaning tool among the at least one obstacle according to the annotation information of at least one obstacle in the environmental map, the cleaning tool being a tool for assisting cleaning; generating the cleaning strategy based on the at least one cleaning tool. 17. The method according to claim 16, characterized in that, the method further includes: when the cleaning equipment has grasped the at least one cleaning tool, collecting environmental perception data of the environment; updating the environmental map according to the environmental perception data to obtain an updated environmental map. 18. A cleaning device, characterized in that the cleaning device includes a gripping device for performing a gripping action, and the cleaning device is used to perform the method as described in any one of claims 1-17. Claims 3 / 3 Page 4 CN 121489359 A Control Method for Cleaning Device and Technical Field of Cleaning Device

[0001] This application relates to the field of intelligent control technology, and in particular to a control method for cleaning device and a cleaning device. Background Art

[0002] With the rapid development of smart home technology, cleaning devices such as robotic vacuum cleaners and smart vacuum cleaners have been widely used in homes, offices and industrial settings, becoming important tools for improving quality of life and work efficiency.

[0003] In complex and ever-changing cleaning environments, users have put forward higher requirements for the intelligence level of cleaning devices. They not only need cleaning devices to have basic autonomous navigation, obstacle avoidance and cleaning functions, but also expect them to be able to dynamically adapt to the needs of different cleaning tasks. For example, deep cleaning of special areas such as floor gaps, high planes (such as desktops, cabinet doors) or narrow corners (such as wall corners, furniture gaps).

[0004] Taking a robotic vacuum cleaner as an example, existing robotic vacuum cleaners do not have dedicated cleaning accessories and can only rely on the vacuum cleaner at the bottom of the robot body to clean the floor. These cleaning accessories include, for example, brushes, portable vacuum cleaners, and suction tubes.

[0005] Because existing technologies do not equip cleaning devices with cleaning accessories, they also lack a series of control logics and algorithms for detecting, identifying, grasping, and using these accessories. Therefore, existing cleaning devices struggle to accurately detect and store...Whether the accessory compartment for cleaning accessories is in a preset position also makes it difficult to control the cleaning equipment to perform cleaning tasks using cleaning accessories.

[0006] This application provides a control method and cleaning equipment for a cleaning equipment, used to control the cleaning equipment to perform more accurate on-site detection of the accessory compartment storing cleaning accessories, and to generate and execute a cleaning strategy based on the on-site detection result, so as to improve cleanliness and cleaning efficiency.

[0007] In a first aspect, this application provides a control method for a cleaning equipment, the method comprising: collecting accessory compartment status identity information, the accessory compartment being used to store cleaning accessories used to assist the cleaning equipment in cleaning; detecting whether the accessory compartment is in a preset position based on the accessory compartment status identity information, and obtaining an on-site detection result; generating a cleaning strategy based on the on-site detection result, and executing a cleaning task according to the cleaning strategy.

[0008] In one possible implementation, detecting whether a parts warehouse is in a preset location based on its status identity information and obtaining an on-site detection result includes: matching the parts warehouse status identity information with a preset parts warehouse feature model, wherein the parts warehouse feature model is a model constructed based on features extracted when the parts warehouse is in the preset location; if the match is successful, an on-site detection result of the parts warehouse being in the preset location is obtained; if the match fails, an on-site detection result of the parts warehouse not being in the preset location is obtained.

[0009] In one possible implementation, the parts warehouse status identity information includes a unique identifier for the parts warehouse, and the parts warehouse feature model includes an identifier feature extracted from the unique identifier of the parts warehouse; matching the parts warehouse status identity information with the preset parts warehouse feature model includes: matching the unique identifier of the parts warehouse with the identifier feature of the parts warehouse feature model.

[0010] In one possible implementation, the accessory compartment status identity information includes the spatial pose of the accessory compartment, and the accessory compartment feature model includes the in-situ spatial pose range of the accessory compartment; matching the accessory compartment status identity information with the preset accessory compartment feature model includes: matching the spatial pose of the accessory compartment with the in-situ spatial pose range of the accessory compartment feature model. Specification 1 / 26 page 5 CN 121489359 A

[0011] In one possible implementation, generating a cleaning strategy based on the in-situ detection result includes: generating a cleaning strategy based on the cleaning accessories in the accessory compartment when the in-situ detection result indicates that the accessory compartment is in a preset position.

[0012] In one possible implementation, generating a cleaning strategy based on the cleaning accessories in the accessory compartment includes: identifying at least one cleaning accessory in the accessory compartment, determining the type of at least one cleaning accessory; generating a cleaning strategy based on the cleaning task requirements and the type of at least one cleaning accessory.

[0013] In one possible implementation, identifying at least one cleaning accessory in the accessory compartment and determining the type of at least one cleaning accessory includes: identifying the accessory unique identifier of at least one cleaning accessory in the accessory compartment, and based on the accessory unique identifier...Identifying the type of at least one cleaning accessory.

[0014] In one possible implementation, identifying at least one cleaning accessory in the accessory compartment and determining the type of at least one cleaning accessory includes: scanning a near-field communication tag on at least one cleaning accessory in the accessory compartment using a near-field communication reader installed on the cleaning device; identifying at least one cleaning accessory in the accessory compartment and determining the type of at least one cleaning accessory based on the data interaction identification result between the near-field communication reader and the near-field communication tag.

[0015] In one possible implementation, the cleaning strategy includes an accessory grasping parameter set, which includes at least one grasping action parameter for grasping the cleaning accessory; generating the cleaning strategy based on the cleaning task requirements and the type of at least one cleaning accessory includes: determining a target cleaning accessory that meets the cleaning task requirements based on the cleaning task requirements and the type of at least one cleaning accessory; calculating the grasping action parameter for grasping the target cleaning accessory based on the relative pose between the cleaning device and the target cleaning accessory, and generating the accessory grasping parameter set of the cleaning strategy.

[0016] In one possible implementation, the method further includes: grabbing a target cleaning accessory using a gripping device of the cleaning equipment according to an accessory gripping parameter set; detecting the gripping opening degree of the gripping device using a position encoder of the gripping device during the gripping process; and determining whether the gripping device has grabbed the target cleaning accessory based on the gripping opening degree.

[0017] In one possible implementation, generating a cleaning strategy based on the in-situ detection result includes: collecting environmental perception data of the environment in which the cleaning equipment perceives its surroundings when the in-situ detection result indicates that the accessory compartment is not in a preset position; identifying whether cleaning tools exist in the environment based on the environmental perception data using a visual model, wherein the cleaning tools are tools used to assist in cleaning; and generating a cleaning strategy based on the cleaning tools identified in the environment.

[0018] In one possible implementation, generating a cleaning strategy based on the cleaning tools identified in the environment includes: determining the tool type of the cleaning tools identified in the environment using a visual model; and generating a cleaning strategy based on the cleaning task requirements and the tool type.

[0019] In one possible implementation, generating a cleaning strategy based on cleaning task requirements and tool type includes: if the cleaning task requirements include cleaning blind spots, determining whether the tool type meets the cleaning task requirements; if the tool type meets the cleaning task requirements, generating a cleaning strategy based on the tool type that includes cleaning blind spots.

[0020] In one possible implementation, the cleaning strategy includes a tool grasping parameter set, which includes at least one grasping action parameter for grasping the cleaning tool; generating a cleaning strategy based on cleaning task requirements and tool type includes: if multiple cleaning tools of multiple tool types are identified, determining whether the tool type meets the cleaning task requirements and tool type.The method identifies the type of cleaning tool and determines the target cleaning tool that meets the cleaning task requirements. Based on the relative pose between the cleaning equipment and the target cleaning tool, it calculates the grasping action parameters for grasping the target cleaning tool and generates a tool grasping parameter set for the cleaning strategy.

[0021] In one possible implementation, the method further includes: grasping the target cleaning tool using the grasping device of the cleaning equipment according to the tool grasping parameter set; during the grasping process, detecting the grasping opening degree of the grasping device using the position encoder of the grasping device; and determining whether the grasping device has grasped the target cleaning tool based on the grasping opening degree.

[0022] In one possible implementation, generating a cleaning strategy based on the in-situ detection result includes: when the in-situ detection result indicates that the accessory compartment is not in a preset position, calling the environmental map of the cleaning equipment, which is obtained by the cleaning equipment performing spatial scanning and map construction on its environment, and includes annotation information of obstacles in the environmental map; determining at least one cleaning tool among at least one obstacle based on the annotation information of at least one obstacle in the environmental map, the cleaning tool being a tool used to assist in cleaning; and generating a cleaning strategy based on at least one cleaning tool.

[0023] In one possible implementation, the method further includes: when the cleaning equipment grabs at least one cleaning tool, collecting environmental perception data of the environment; updating the environmental map according to the environmental perception data to obtain an updated environmental map.

[0024] In a second aspect, embodiments of this application provide a control device for a cleaning equipment. The control device includes: a collection module for collecting the status identity information of a parts compartment, the parts compartment being used to store cleaning accessories that assist the cleaning equipment in cleaning; a detection module for detecting whether the parts compartment is in a preset position based on the status identity information of the parts compartment, and obtaining an in-situ detection result; and a generation module for generating a cleaning strategy based on the in-situ detection result, and executing a cleaning task according to the cleaning strategy.

[0025] In one possible implementation, the detection module is specifically used to: match the status identity information of the parts compartment with a preset parts compartment feature model, the parts compartment feature model being a model constructed based on features extracted when the parts compartment is in a preset position; if the match is successful, obtaining an in-situ detection result for the parts compartment being in the preset position; if the match fails, obtaining an in-situ detection result for the parts compartment not being in the preset position.

[0026] In one possible implementation, the parts warehouse status identity information includes a unique identifier for the parts warehouse, and the parts warehouse feature model includes identifier features extracted from the unique identifier of the parts warehouse; the detection module is specifically used to: match the unique identifier of the parts warehouse with the identifier features of the parts warehouse feature model.

[0027] In one possible implementation, the parts warehouse status identity information includes the spatial pose of the parts warehouse, the parts warehouse...The feature model includes the in-situ spatial pose range of the accessory compartment; the detection module is specifically used to: match the spatial pose of the accessory compartment with the in-situ spatial pose range of the feature model of the accessory compartment.

[0028] In one possible implementation, the generation module is specifically used to: generate a cleaning strategy based on the cleaning accessories in the accessory compartment when the in-situ detection result indicates that the accessory compartment is in a preset position.

[0029] In one possible implementation, the generation module is specifically used to: identify at least one cleaning accessory in the accessory compartment and determine the type of at least one cleaning accessory; generate a cleaning strategy based on the cleaning task requirements and the type of at least one cleaning accessory.

[0030] In one possible implementation, the generation module is specifically used to: identify the accessory unique identifier of at least one cleaning accessory in the accessory compartment and determine the type of at least one cleaning accessory based on the accessory unique identifier.

[0031] In one possible implementation, the generation module is specifically used to: scan the near-field communication tag set on at least one cleaning accessory in the accessory compartment through the near-field communication reader set on the cleaning device; identify at least one cleaning accessory in the accessory compartment and determine the type of at least one cleaning accessory based on the data interaction identification result between the near-field communication reader and the near-field communication tag.

[0032] In one possible implementation, the cleaning strategy includes an accessory grasping parameter set, which includes at least one grasping action parameter for grasping a cleaning accessory; the generation module is specifically used to: determine a target cleaning accessory that meets the cleaning task requirements based on the cleaning task requirements and the type of at least one cleaning accessory; calculate the grasping action parameter for grasping the target cleaning accessory based on the relative pose between the cleaning device and the target cleaning accessory, and generate the accessory grasping parameter set of the cleaning strategy. Specification 3 / 26 pages 7 CN 121489359 A

[0033] In one possible implementation, the control device further includes a grasping module, which is used to: grasp the target cleaning accessory using the grasping device of the cleaning device according to the accessory grasping parameter set; detect the grasping opening degree of the grasping device through the position encoder of the grasping device during the grasping process; and determine whether the grasping device has grasped the target cleaning accessory based on the grasping opening degree.

[0034] In one possible implementation, the generation module is specifically used for: collecting environmental perception data of the environment in which the cleaning equipment perceives its surroundings when the in-situ detection result indicates that the accessory compartment is not in a preset position; identifying whether cleaning tools exist in the environment based on the environmental perception data using a visual model, wherein the cleaning tools are tools used to assist in cleaning; and generating a cleaning strategy based on the cleaning tools identified in the environment.

[0035] In one possible implementation, the generation module is specifically used for: determining the tool type of the cleaning tools identified in the environment using a visual model; and generating a cleaning strategy based on the cleaning task requirements and the tool type.

[0036] In one possible implementation, the generation module is specifically used to: determine whether the tool type meets the cleaning task requirements when the cleaning task requirements include cleaning blind spots; if the tool type meets the cleaning task requirements, generate a cleaning strategy that includes cleaning blind spots based on the tool type.

[0037] In one possible implementation, the cleaning strategy includes a tool grasping parameter set, which includes at least one grasping action parameter for grasping a cleaning tool; the generation module is specifically used to: when multiple cleaning tools of multiple tool types are identified, determine a target cleaning tool that meets the cleaning task requirements based on the cleaning task requirements and the tool types of the multiple cleaning tools; calculate the grasping action parameters for grasping the target cleaning tool based on the relative pose between the cleaning device and the target cleaning tool, and generate a tool grasping parameter set for the cleaning strategy.

[0038] In one possible implementation, the control device further includes a grasping module, which is used to: grasp the target cleaning tool using the grasping device of the cleaning device according to the tool grasping parameter set; during the grasping process, detect the grasping opening degree of the grasping device through the position encoder of the grasping device; and determine whether the grasping device has grasped the target cleaning tool based on the grasping opening degree.

[0039] In one possible implementation, the generation module is specifically used to: when the in-situ detection result indicates that the accessory compartment is not in a preset position, call the environmental map of the cleaning equipment, the environmental map being obtained by the cleaning equipment after spatial scanning and map construction of its environment, the environmental map including the annotation information of obstacles; based on the annotation information of at least one obstacle in the environmental map, determine at least one cleaning tool among at least one obstacle, the cleaning tool being a tool used to assist in cleaning; generate a cleaning strategy based on at least one cleaning tool.

[0040] In one possible implementation, the control device further includes an update module, the update module being used to: when the cleaning equipment grabs at least one cleaning tool, collect environmental perception data of the environment; update the environmental map based on the environmental perception data to obtain an updated environmental map.

[0041] In a third aspect, embodiments of this application provide a cleaning equipment, the cleaning equipment including a gripping device, the gripping device being used to perform a gripping action, the cleaning equipment being used to implement the first aspect and / or various possible implementations of the first aspect as described above.

[0042] In a fourth aspect, embodiments of this application provide an electronic device, including: a memory and a processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0043] In a fifth aspect, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or the second aspect as described above.On the one hand, various possible implementation methods.

[0044] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program, computer program specification 4 / 26 pages 8 CN 121489359 A, which, when executed by a processor, implements the first aspect and / or various possible implementation methods of the first aspect.

[0045] The control method and cleaning equipment of the cleaning equipment provided in the embodiments of this application, the method collects the accessory compartment status identity information of the accessory compartment, and detects whether the accessory compartment is in a preset position based on the accessory compartment status identity information, so as to obtain an on-site detection result. Further, a cleaning strategy can be generated based on the on-site detection result, and a cleaning task can be executed according to the cleaning strategy. In this way, by collecting and using the accessory compartment status identity information, a highly reliable accessory compartment on-site detection can be achieved, and a cleaning strategy that is beneficial to assisting the cleaning equipment in cleaning can be generated based on the on-site detection result, which can improve the cleanliness and cleaning efficiency of the cleaning equipment when performing cleaning tasks. Brief Description of the Drawings

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0047] Figure 1 is a flowchart illustrating the control method of the cleaning equipment provided in an embodiment of this application;

[0048] Figure 2 is a structural diagram illustrating the control system of the cleaning equipment provided in an embodiment of this application;

[0049] Figure 3 is a control logic diagram illustrating the cleaning equipment provided in an embodiment of this application;

[0050] Figure 4 is a structural diagram illustrating the control device of the cleaning equipment provided in an embodiment of this application;

[0051] Figure 5 is a structural diagram illustrating an electronic device provided in an embodiment of this application.

[0052] The above figures illustrate specific embodiments of this application, which will be described in more detail below. These figures and descriptions are not intended to limit the scope of the concept of this application in any way, but rather to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. Detailed Description

[0053] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0054] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, and use of related data are all subject to the relevant authorization.The provision, disclosure, and application of this technology comply with the relevant laws, regulations, and standards of the relevant countries and regions, and necessary confidentiality measures have been taken. It does not violate public order and good morals, and provides corresponding operation entry points for users to choose to authorize or refuse.

[0055] Furthermore, the technical solution involved in this application, which performs big data analysis on user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and uses artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the automated decision results, provides users with corresponding operation entry points for users to choose to agree to or refuse the automated decision results; if the user chooses to refuse, the process proceeds to the expert decision-making process.

[0056] In the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner. Specification 5 / 26 Page 9 CN 121489359 A

[0057] In the embodiments of this application, if the words "first" and "second" are used, it is to distinguish the same or similar items with basically the same function and role. For example, the first electronic device and the second electronic device are only used to distinguish different electronic devices and do not limit their order. Those skilled in the art can understand that the words "first" and "second" do not limit the quantity and execution order, and the words "first" and "second" do not necessarily mean different.

[0058] In the embodiments of this application, "at least one" means one or more, and "more" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship.

[0059] For example, existing intelligent cleaning equipment such as sweeping robots have many technical pain points. For example, in narrow spaces (such as the narrow gap between a sofa and a wall), the robot vacuum cannot enter and it is difficult to effectively clean them using the vacuum cleaner at the bottom of the robot. Similarly, in non-floor areas (such as tabletops, cabinet doors, etc.), it is difficult to effectively clean them using the vacuum cleaner at the bottom.

[0060] Furthermore, although some existing robot vacuums are equipped with robotic arms, they do not utilize the advantages of these arms. For example, they can only use the robotic arm to grab small pieces of trash such as crumpled paper and paper cups and discard them into the trash can, without considering the use of mechanical...The arm grasps cleaning accessories or tools to perform deep cleaning of special areas such as narrow spaces, non-ground areas, and corners. Therefore, existing cleaning equipment cannot perform more comprehensive cleaning when controlled, resulting in low cleaning degree and efficiency.

[0061] When the inventors studied the pain points existing in the current scenario, they conceived that if the robot vacuum cleaner is equipped with professional cleaning accessories when delivered to the user, it can effectively clean special areas that are difficult to clean, such as floor gaps, high planes, or narrow corners, by using the cleaning accessories when performing cleaning tasks, which can improve the cleaning coverage area and cleaning ability, thereby improving the cleaning degree and efficiency. In addition, assuming that the user does not select cleaning accessories, if the cleaning equipment can autonomously find cleaning tools to clean special areas during the task, the cleaning degree and efficiency can also be improved.

[0062] For example, when the robot vacuum cleaner recognizes a brush that the user allows to use in the environment, it can grasp the brush through the robot vacuum cleaner's mechanical arm and clean the gaps between furniture or cabinet doors, etc., increasing the cleaned area and improving the cleaning degree and efficiency.

[0063] Taking a robotic vacuum cleaner as an example, since existing robotic vacuum cleaners are not equipped with cleaning accessories specifically for auxiliary cleaning, they lack the algorithm logic for finding and using cleaning accessories. Therefore, there is no need or method for detecting whether the accessory compartment storing cleaning accessories is in a preset location, and consequently, there is no specific implementation for generating and executing a cleaning strategy based on the presence detection result of whether the accessory compartment is in a preset location.

[0064] In view of this, embodiments of this application provide a control method for a cleaning device. This method collects the accessory compartment status identity information and detects whether the accessory compartment is in a preset location based on the accessory compartment status identity information to obtain a presence detection result. Further, a cleaning strategy can be generated based on the presence detection result, and a cleaning task can be executed according to the cleaning strategy. Thus, by collecting and using the accessory compartment status identity information, a highly reliable presence detection of the accessory compartment can be achieved. Furthermore, a cleaning strategy beneficial to auxiliary cleaning can be generated based on the presence detection result, improving the cleanliness and efficiency of the cleaning device when performing cleaning tasks.

[0065] The technical solution of this application will be described in detail below with specific embodiments. The specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings. Specification 6 / 26 pages 10 CN 121489359 A

[0066] Figure 1 is a schematic flowchart of the control method of the cleaning equipment provided in the embodiment of this application. The execution subject of the method can be an electronic device with corresponding data storage and computing capabilities. The electronic device can be understood as the cleaning equipmentThe accessory compartment can be a device that assists the cleaning equipment in cleaning, such as a robot vacuum cleaner, etc. It can also be understood as a processor, processing unit, or control chip in the cleaning equipment. As shown in Figure 1, the method includes:

[0067] S101, collecting the accessory compartment status and identity information. The accessory compartment is used to store cleaning accessories that assist the cleaning equipment in cleaning.

[0068] For example, the cleaning equipment can be an electronic device used for cleaning, such as a robot vacuum cleaner, a vacuum cleaner, etc. Cleaning accessories can be understood as devices that are matched with the cleaning equipment and used to assist the cleaning equipment in cleaning. For example, they can be special cleaning equipment designed and configured by the provider of the cleaning equipment, such as brushes, portable vacuum cleaners, vacuum hoses, and vacuum heads.

[0069] The accessory compartment can be understood as a storage device used to store cleaning accessories. For example, the accessory compartment has multiple slots, and each slot can be used to fix and store a cleaning accessory.

[0070] The accessory compartment status and identity information can be understood as multi-dimensional information used to characterize the physical state and identity of the accessory compartment, including but not limited to the unique identifier of the accessory compartment and the spatial orientation of the accessory compartment. The unique identifier of the accessory compartment can be, for example, a QR code, serial number, predefined code, barcode, icon, and / or Near Field Communication (NFC) tag. A unique identifier can be understood as an identifier that is globally unique within the cleaning equipment. The spatial pose of the accessory compartment includes, for example, its spatial position coordinates and attitude parameters. These attitude parameters are, for example, rotation angles within a spatial coordinate system (X-axis, Y-axis, Z-axis), such as roll angle, yaw angle, and pitch angle.

[0071] When collecting the accessory compartment's status and identity information, for example, the QR code, serial number, custom code, and / or icon of the accessory compartment can be scanned using the cleaning equipment's vision device; the spatial pose of the accessory compartment within the cleaning equipment's spatial coordinate system can also be collected using the cleaning equipment's vision device or positioning device. The vision device includes, for example, a camera, a camera, and / or a scanner. The positioning device may include, for example, a positioning radar, a laser rangefinder, and / or a depth camera.

[0072] For example, an Inertial Measurement Unit (IMU) sensor can also be installed in the accessory compartment to determine the attitude parameters of the accessory compartment itself. The cleaning device can read the attitude parameters of the accessory compartment through data communication to obtain the status and identity information of the accessory compartment.

[0073] S102, based on the status and identity information of the accessory compartment, detect whether the accessory compartment is in a preset position and obtain the on-site detection result.

[0074] For example, the preset position can be understood as the standard installation position set for the accessory compartment, which can be determined according to the mechanical structure of the device or the physical space range defined according to the user configuration. For example, the area around the charging compartment of the sweeping robot is a preset position.The location range can be defined by the map of the robot vacuum cleaner to define the preset location of the accessory compartment.

[0075] The presence detection result can be understood as the logical judgment result obtained after verifying or matching the status identity information of the accessory compartment, which can be used to characterize whether the accessory compartment is in the preset location. For example, if the cleaning device matches the unique identifier of the accessory compartment within the preset range of the accessory compartment, it means that the match is successful, and the presence detection result characterizing the accessory compartment in the preset location can be obtained, that is, "in place"; if the cleaning device cannot match the unique identifier of the accessory compartment within the preset range of the accessory compartment, it means that the match fails, and the presence detection result characterizing the accessory compartment is not in the preset location can be obtained, that is, "out of place" or "not in place".

[0076] S103, generate a cleaning strategy based on the presence detection result, and execute the cleaning task according to the cleaning strategy.

[0077] For example, after obtaining the presence detection result, the cleaning device knows whether the accessory compartment is in place or not. If it is in place, the cleaning device can combine the available cleaning accessories in the accessory compartment and the items that need to be cleaned at present to perform a comprehensive analysis, generate a cleaning strategy, and execute the cleaning task according to the cleaning strategy. Instruction manual 7 / 26 pages 11 CN 121489359 A

[0078] Wherein, the cleaning strategy can be understood as a set of cleaning equipment control instructions generated based on the in-situ detection results, used to guide the cleaning equipment to perform cleaning tasks. The cleaning strategy may include, for example, cleaning path, suction power, cleaning head rotation speed, etc.

[0079] For example, when the accessory compartment is detected to be in place, a cleaning path planning instruction for calling the brush accessory can be generated; when the accessory compartment is detected to be out of place, a cleaning strategy for cleaning using the cleaning equipment body can be generated. Alternatively, when the accessory compartment is detected to be out of place, a visual model can be used to identify whether there are any usable cleaning tools in the environment, and when cleaning tools are present, a cleaning path planning instruction based on the cleaning tools can be generated. Wherein, cleaning tools can be understood as tools other than cleaning accessories that can be used to assist in cleaning.

[0080] For example, in order to implement the above steps S101 to S103, firstly, the status identity information of the accessory compartment is collected. This status identity information of the accessory compartment can be obtained through a multimodal sensor (such as a vision device, near-field communication card reader), including a unique identifier and spatial pose.

[0081] Secondly, based on the collected accessory compartment status and identity information and the preset detection criteria for whether the accessory compartment is in a preset location, a presence detection result (either present or absent) can be obtained. For example, if the accessory compartment's near-field communication tag (NFC tag) is read by the near-field communication reader (NFC reader) in the cleaning equipment within the preset location, a presence detection result (present) can be obtained; if the accessory compartment's NFC tag cannot be read by the NFC reader in the cleaning equipment within the preset location, an absence detection result (absent) can be obtained. Another example is through a vision device and a fixed...The positioning device identifies the accessory compartment and locates its spatial pose. When the spatial pose of the accessory compartment conforms to the preset spatial pose, an in-situ detection result can be obtained; when the spatial pose of the accessory compartment does not conform to the preset spatial pose, an out-of-situ detection result can be obtained.

[0082] Finally, a cleaning strategy can be generated based on the in-situ detection result. For example, if in-situ, the cleaning accessory information in the accessory compartment is called to generate a cleaning path and cleaning parameters (such as travel speed, working frequency, power, etc.); if out-of-situ, environmental perception is triggered to identify whether there are available cleaning tools in the environment. The entire process described above can be processed and analyzed by a preset algorithm to realize real-time detection of the accessory compartment status and real-time generation of cleaning strategies, thereby achieving closed-loop control of the cleaning equipment.

[0083] For example, most existing cleaning equipment does not have an accessory compartment, let alone the need to detect whether the accessory compartment is in place. However, the cleaning accessories in the accessory compartment are an important aid to improving the cleaning ability of the cleaning equipment. Therefore, the method of this application embodiment is based on the detection of whether the accessory compartment is in a preset position, which can provide a basis for expanding the cleaning ability of the cleaning equipment and generate a cleaning strategy that is more conducive to improving the cleanliness and cleaning efficiency.

[0084] The control method of the cleaning equipment provided in this application embodiment collects the accessory compartment status identity information and detects whether the accessory compartment is in a preset position based on the accessory compartment status identity information to obtain the on-site detection result. Further, a cleaning strategy can be generated based on the on-site detection result, and the cleaning task can be executed according to the cleaning strategy. In this way, by collecting and using the accessory compartment status identity information, a highly reliable accessory compartment on-site detection can be achieved, and a cleaning strategy that is conducive to assisting the cleaning equipment can be generated based on the on-site detection result, which can improve the cleanliness and cleaning efficiency of the cleaning equipment when performing cleaning tasks.

[0085] It should be noted that in some application scenarios, before the cleaning equipment performs the on-site detection of the accessory compartment, the spatial environment may have been cleaned globally or locally, or the spatial environment may not have been cleaned globally or locally.

[0086] For example, when the robot vacuum starts this cleaning task, it first performs a general cleaning of the room. During the cleaning, some areas that cannot be cleaned thoroughly are marked, such as furniture gaps, tabletops, or corners of interior walls. In this case, the robot vacuum can return to a preset position to perform an on-site detection of the accessory compartment. If the accessory compartment is detected, it can generate an appropriate cleaning strategy based on the cleaning accessories in the accessory compartment and the characteristics of the remaining areas (such as narrow gaps being long and narrow, requiring a long brush for cleaning) to thoroughly clean the remaining areas.

[0087] In other application scenarios, the space environment has not yet been cleaned globally or locally before the cleaning device performs an on-site detection of the accessory compartment.

[0088] For example, when the robot vacuum starts the current cleaning task, it first checks whether the accessory compartment is in place. If the accessory compartment is detected to be in place, it can select the cleaning accessory last used during the last cleaning to generate a cleaning strategy; or it can select the cleaning accessory with the highest historical usage frequency to generate a cleaning strategy. After that, the robot vacuum starts to clean the space environment globally or locally. Since the robot vacuum already has a cleaning accessory when it enters the formal cleaning process, when it encounters a difficult-to-clean area that can be thoroughly cleaned by that cleaning accessory, such as crevices, tabletops, or corners of interior walls, it can directly clean without having to return to the preset position for in-place detection and regenerate a cleaning strategy, thus improving cleaning efficiency.

[0089] Of course, the description of using one cleaning accessory at a time in the various embodiments of this application is only for illustrative purposes and understanding. If the cleaning device has the ability to use multiple cleaning accessories simultaneously, it can also generate a cleaning strategy based on multiple cleaning accessories and use multiple cleaning accessories to perform the cleaning task simultaneously. This application does not limit this. For example, a robotic vacuum cleaner with multiple robotic arms can simultaneously grasp multiple cleaning accessories. A cleaning strategy can be generated based on these multiple cleaning accessories, and the cleaning task can be executed based on these accessories, thus significantly improving cleaning efficiency.

[0090] In one possible implementation, detecting whether the accessory compartment is in a preset location based on the accessory compartment status identity information and obtaining an on-site detection result includes: matching the accessory compartment status identity information with a preset accessory compartment feature model, where the accessory compartment feature model is a model constructed based on features extracted from the accessory compartment when it is in a preset location; if the match is successful, an on-site detection result for the accessory compartment being in the preset location is obtained; if the match fails, an on-site detection result for the accessory compartment not being in the preset location is obtained.

[0091] For example, the preset accessory compartment feature model can be understood as a model constructed based on features extracted from the accessory compartment when it is in a preset location, such as a model including a unique identifier feature of the preset location and / or a spatial pose range, which can be used to match the accessory compartment status identity information. For example, the unique identifier of the preset location is the hash value of the QR code, and the spatial pose range is the spatial position coordinates (x±5cm, y±5cm, z±2cm) and rotation angle (yaw angle ±10°).

[0092] Matching can be understood as comparing the status identity information of the parts warehouse with the preset parts warehouse feature model through rule matching or machine learning algorithms. For example, the unique identifier is verified by hash comparison, and the deviation of the spatial pose is verified by threshold judgment to see if it is within the allowable range. When matching, location information can be used in conjunction with matching. For example, within the preset range of the preset location, matching through the parts warehouse feature model can improve the accuracy of in-situ detection.

[0093] For example, the collected status identity information of the parts warehouse can be verified and matched with the preset feature model to realizeThe determination of whether the parts warehouse is in place is as follows. The verification and matching process can be divided into two branches: if the unique identifier (such as a QR code) is consistent with the identifier features in the preset parts warehouse feature model, and the spatial pose (such as IMU data) is within the preset spatial range, then it is determined to be in place, and an in-place detection result is obtained; otherwise, it is determined to be out of place, and an out-of-place in-place detection result is obtained. The verification and matching logic can be implemented through preset rules (such as hash comparison, threshold judgment) or machine learning models (such as classifiers) to ensure the reliability of the in-place detection result.

[0094] In the embodiments of this application, since the parts warehouse feature model is a model constructed based on the features extracted from the parts warehouse in the preset location, the parts warehouse status identity information can characterize the status and identity information of the parts warehouse, ensuring the uniqueness of the parts warehouse identity and reducing the probability of misidentification. By performing logical verification and matching through the preset parts warehouse feature model, an accurate determination of whether the parts warehouse is in place can be achieved, improving the accuracy of in-place detection. By matching the status identity information of the parts warehouse with a preset parts warehouse feature model, it is possible to conveniently and accurately detect whether the parts warehouse is in place, taking into account both detection efficiency and detection accuracy.

[0095] For example, the unique identifier or spatial pose of the parts warehouse can be matched with the parts warehouse feature model to obtain the in-place detection result.

[0096] In one possible implementation, the status identity information of the parts warehouse includes the unique identifier of the parts warehouse, and the parts warehouse feature model includes the identifier features extracted from the unique identifier of the parts warehouse; matching the status identity information of the parts warehouse with the preset parts warehouse feature model includes: matching the unique identifier of the parts warehouse with the identifier features of the parts warehouse feature model.

[0097] For example, the identifier features of the parts warehouse feature model can be understood as feature data extracted from the unique identifier of the parts warehouse, which can be used to compare with the unique identifier of the parts warehouse. For example, the hash value of the unique identifier or the feature vector after dimensionality reduction by the feature extraction algorithm. Among them, the feature extraction algorithm is, for example, Principal Component Analysis (PCA) algorithm.

[0098] By matching the unique identifier with the identifier features, the matching logic can be further refined. The unique identifier of the parts compartment (such as a QR code) can be obtained through image recognition or scanning and can be compared with the identifier features (such as the encoding features of the QR code) in the preset parts compartment feature model. If the match is successful, it is determined that the parts compartment is in place; otherwise, it is determined that the parts compartment is not in place.

[0099] In the embodiments of this application, the unique identifier is the identifying information that uniquely indicates the parts compartment in the global context of the cleaning equipment, and has global uniqueness. The matching logic of the parts compartment presence detection is refined by matching the unique identifier. The unique identifierGlobal uniqueness ensures accurate identification of the accessory compartment, avoiding misjudgments caused by duplicate tags or environmental interference, and improving the robustness of detection. Based on this, the accuracy of in-situ detection of the accessory compartment can be enhanced, providing a more reliable basis for the generation of subsequent cleaning strategies.

[0100] In one possible implementation, the accessory compartment status identity information includes the spatial pose of the accessory compartment, and the accessory compartment feature model includes the in-situ spatial pose range of the accessory compartment; matching the accessory compartment status identity information with the preset accessory compartment feature model includes: matching the spatial pose of the accessory compartment with the in-situ spatial pose range of the accessory compartment feature model.

[0101] For example, the in-situ spatial pose range of the accessory compartment can be understood as a preset pose and its range pre-set for the accessory compartment. For example, a user or cleaning device can set the in-situ spatial pose range for the accessory compartment in the environmental map of the cleaning device through an application program (APP).

[0102] For example, in the environmental map constructed after the cleaning equipment scans the environment, the preset position and position range can be set by defining the center coordinates and boundary coordinates of the preset position of the parts compartment; the preset attitude can be set by inputting attitude parameters (such as the yaw angle), and the attitude range can be set by inputting the range of attitude parameters (such as yaw angle ±5°). Based on the preset position and position range, as well as the preset attitude and attitude range, the in-situ spatial pose range of the parts compartment can be set.

[0103] When performing in-situ detection, the current spatial position (x, y, z) and attitude (roll angle, yaw angle, and pitch angle) of the parts compartment are obtained, that is, the spatial pose of the parts compartment is obtained. The spatial pose is matched with the in-situ spatial pose range of the parts compartment feature model. If the spatial pose is within the in-situ spatial pose range, it can be determined that the match is successful, and the in-situ detection result of the parts compartment at the preset position is obtained; if the spatial pose is not within the in-situ spatial pose range, it can be determined that the match is unsuccessful, and the in-situ detection result of the parts compartment not being at the preset position is obtained.

[0104] In this embodiment, the spatial pose of the parts compartment is matched with the in-situ spatial pose range of the parts compartment feature model. By using the in-situ spatial pose range as the judgment threshold for spatial pose, it is possible to effectively verify whether the parts compartment is in a preset position, avoiding detection failure due to loose installation or displacement. The combination of the two, through rule-based or model-based matching algorithms, can improve the accuracy of in-situ detection results and provide a reliable basis for the subsequent cleaning strategy generation.

[0105] In summary, by collecting the status and identity information of the parts compartment and matching it with the preset parts compartment feature model, the accuracy of in-situ detection of the parts compartment can be improved.

[0106] For example, the matching of unique identifiers (such as QR codes) can ensure the uniqueness of the parts compartment's identity and reduce errors.The probability of recognition; the matching of spatial pose (such as spatial position coordinates and attitude parameters) verifies whether the accessory compartment is in the preset position, and can accurately determine the physical space where the accessory compartment is located, avoiding detection failure due to loose installation or displacement. By using a unique identifier and spatial pose, combined with the accessory compartment feature model matching algorithm (such as rule-based threshold judgment or machine learning classification), a logical closed loop is achieved, improving the reliability of the in-situ detection results.

[0107] Finally, the in-situ detection results can drive the generation of cleaning strategies. For example, when the accessory compartment is in place, the type information of the cleaning accessories can be called first, or when it is not in place, the identification process of environmental tools can be triggered, thereby realizing the adaptive response capability of the cleaning equipment to the status of the cleaning accessories. Based on this, the method of this application embodiment can improve the accuracy of accessory compartment in-situ detection and the scene adaptability of cleaning strategy generation, reduce the need for manual intervention by users, and enhance the intelligence level of the cleaning equipment.

[0108] For example, the in-situ detection results include two results: in-situ and not in-situ. Different cleaning strategies can be generated for these two different in-situ detection results.

[0109] In one possible implementation, generating a cleaning strategy based on the in-situ detection result includes: generating a cleaning strategy based on the cleaning accessories in the accessory compartment when the in-situ detection result indicates that the accessory compartment is in a preset position.

[0110] For example, when the in-situ detection result indicates that the accessory compartment is in a preset position, the cleaning device knows the existence of the accessory compartment and can generate a cleaning strategy that is more conducive to completing the cleaning task by combining the cleaning accessories in the accessory compartment.

[0111] For example, when generating a cleaning strategy based on the cleaning accessories in the accessory compartment, a cleaning accessory can be randomly selected to generate the corresponding cleaning strategy. Or, for example, the cleaning accessories to be used can be determined first according to the cleaning task requirements of the cleaning task. Then, the existence of the required cleaning accessory can be queried in the accessory compartment. If it exists, a corresponding cleaning strategy can be generated based on the cleaning accessory.

[0112] For another example, all the cleaning accessories currently stored in the accessory compartment can be traversed first, and all cleaning accessories can be analyzed according to the cleaning task requirements of the cleaning task to weigh one or more cleaning accessories that are conducive to efficiently completing the cleaning task. When generating and executing a cleaning strategy, multiple cleaning accessories can be used simultaneously, or one cleaning accessory can be used for cleaning followed by another, and so on until the cleaning task is completed.

[0113] When generating a cleaning strategy based on the cleaning accessories in the accessory compartment, the cleaning accessories in the accessory compartment can be flexibly matched, and optimization iterative analysis can be performed with high cleanliness and cleaning efficiency as constraints, so that the cleaning task can be completed efficiently.

[0114] In the embodiments of this application, when the in-situ detection result indicates that the accessory compartment is in a preset position, a cleaning strategy is generated based on the cleaning accessories in the accessory compartment, so that the cleaning accessories stored in the accessory compartment can be used to target specific cleaning tasks.The purpose of generating a cleaning strategy is to expand the cleaning capabilities of cleaning equipment, improve cleaning completion, and enhance the user experience by using cleaning accessories.

[0115] In one possible implementation, generating a cleaning strategy based on cleaning accessories in the accessory compartment includes: identifying at least one cleaning accessory in the accessory compartment and determining the type of at least one cleaning accessory; generating a cleaning strategy based on the cleaning task requirements and the type of at least one cleaning accessory.

[0116] For example, when identifying at least one cleaning accessory in the accessory compartment, identification can be performed according to a preset identification quantity. For example, if the execution logic presets the identification of N cleaning accessories, then during execution, N cleaning accessories can be identified accordingly, where N can be any value greater than zero and less than the total number of cleaning accessories. Alternatively, when identifying at least one cleaning accessory in the accessory compartment, all cleaning accessories in the accessory compartment can be traversed.

[0117] After identifying at least one cleaning accessory, the type of each cleaning accessory can be determined based on the accessory information of the cleaning accessory or based on the characteristics of the cleaning accessory. For example, accessory information includes information representing the type of cleaning accessory, such as accessory information representing a brush as a sweeping type and accessory information representing a crevice vacuum cleaner as a vacuuming type.

[0118] Alternatively, by analyzing the size, shape, edge, texture and / or material of at least one cleaning accessory through image recognition, the cleaning purpose of the cleaning accessory can be determined, and thus the type of cleaning accessory can be determined.

[0119] Cleaning task requirements can be understood as cleaning goals defined by the user or cleaning equipment, such as cleaning high planes, cleaning crevices, cleaning corners of walls, etc. For example, the user-set "clean desktop" task requirement.

[0120] When generating a cleaning strategy based on the cleaning task requirements and the type of at least one cleaning accessory, for example, by analyzing the necessary features required to meet the cleaning task requirements, and filtering the type of at least one cleaning accessory based on the necessary features, a cleaning accessory that meets the necessary features can be determined, which can be understood as the target cleaning accessory. A corresponding cleaning strategy can be generated based on the target cleaning accessory.

[0121] For example, by analyzing the cleaning task requirements for cleaning a desktop, it can be determined that the necessary feature required to meet the cleaning task requirements is the ability to clean non-ground spaces. The brushes stored in the accessory compartment can be used to clean non-ground spaces, so it can be determined that the brushes can meet the cleaning task requirements, and a cleaning strategy can be generated based on the brushes.

[0122] In this embodiment of the application, when generating a cleaning strategy based on the cleaning accessories in the accessory compartment, the type of at least one cleaning accessory is determined by identifying at least one cleaning accessory in the accessory compartment; a cleaning strategy is generated based on the cleaning task requirements and the type of at least one cleaning accessory. In this way, by combining both the cleaning task requirements and the type of cleaning accessory...Factors can be analyzed in a targeted manner to meet the needs of cleaning tasks, so as to generate personalized cleaning strategies that are adapted to the current tasks and improve the degree of matching of cleaning strategies with the needs of cleaning tasks.

[0123] In one possible implementation, identifying at least one cleaning accessory in the accessory compartment and determining the type of at least one cleaning accessory includes: identifying the accessory unique identifier of at least one cleaning accessory in the accessory compartment and determining the type of at least one cleaning accessory based on the accessory unique identifier.

[0124] For example, when determining the type of cleaning accessory, it can be determined by identifying the accessory unique identifier of the cleaning accessory. Similar to the unique identifier of the accessory compartment, the accessory unique identifier is the unique identifier of the cleaning accessory, which also has global uniqueness in the global cleaning equipment and has an identification function. For example, the accessory unique identifier can be the cleaning accessory's QR code, serial number, predefined code, barcode, icon and / or near-field communication tag, etc.

[0125] For example, by scanning the QR code set on the cleaning accessory body, the accessory information of the cleaning accessory can be determined, such as the type of the cleaning accessory (brush, vacuum cleaner, wiping cloth, etc.). It should be understood that the unique identifier (such as a QR code) of the cleaning accessory can be set on the body of the cleaning accessory or on the accessory compartment. For example, the accessory compartment is provided with multiple storage slots, each storage slot can be used to insert and store one cleaning accessory, and the unique identifier of the corresponding stored cleaning accessory can be marked around each storage slot. Alternatively, the unique identifier of the cleaning accessory can be displayed in other ways, which is not limited in this application embodiment.

[0126] In this application embodiment, by identifying the unique identifier of at least one cleaning accessory in the accessory compartment and determining the type of at least one cleaning accessory based on the unique identifier, the purpose of quickly and conveniently determining the type of the cleaning accessory can be achieved, improving the accuracy and speed of type determination.

[0127] In one possible implementation, identifying at least one cleaning accessory in the accessory compartment and determining the type of at least one cleaning accessory includes: scanning the near-field communication tag set on at least one cleaning accessory in the accessory compartment using a near-field communication card reader provided on the cleaning device; identifying at least one cleaning accessory in the accessory compartment and determining the type of at least one cleaning accessory based on the data interaction identification result between the near-field communication card reader and the near-field communication tag.

[0128] For example, a near-field communication (NFC) reader can be understood as a dedicated device based on NFC technology that can read or write data in carriers such as NFC tags and NFC cards within a short distance (e.g., within 10cm). NFC readers can be installed on cleaning equipment.

[0129] A near-field communication (NFC) tag can be understood as a device with a built-in NFC chip and antenna, requiring no power supply, and capable of reading or writing data within a short distance.A passive electronic tag is sensed by an NFC reader and used to complete data storage or interaction. Near-field communication tags can be set on cleaning accessories.

[0130] By scanning the near-field communication tag set on at least one cleaning accessory in the accessory compartment using the near-field communication reader set on the cleaning device, the cleaning device can obtain the accessory information of the cleaning accessory through data communication between the two, and thus obtain the type of cleaning accessory. For example, the cleaning device has a built-in mapping table of accessory information. By matching the mapping relationship in the mapping table, the type of each cleaning accessory can be determined.

[0131] By setting a near-field communication tag on each cleaning accessory, the probability of misidentification caused by the accessory's unique identifier not being on the cleaning accessory itself (e.g., the accessory's unique identifier is on the accessory compartment itself) can be reduced.

[0132] For example, multiple accessory unique identifiers are set on the surface of the accessory compartment, and the arrangement order is as follows: accessory unique identifier A is above the first hole from left to right, accessory unique identifier B is above the second hole, and accessory unique identifier C is above the third hole. However, the A cleaning accessory corresponding to the unique accessory identifier A is currently stored in the second hole; the B cleaning accessory corresponding to the unique accessory identifier B is stored in the third hole; and the C cleaning accessory corresponding to the unique accessory identifier C is stored in the first hole. Thus, if the type of cleaning accessory is still identified by the unique accessory identifier, misidentification will occur.

[0133] In this embodiment, by setting the near-field communication tag on the cleaning accessory body, accessory information is embedded and bound to the corresponding cleaning accessory, preventing misidentification due to blurred, fallen, or misplaced unique identifiers, thus improving the reliability of identification.

[0134] In one possible implementation, the cleaning strategy includes an accessory grasping parameter set, which includes at least one grasping action parameter for grasping the cleaning accessory; generating a cleaning strategy based on the cleaning task requirements and the type of at least one cleaning accessory includes: determining a target cleaning accessory that meets the cleaning task requirements based on the cleaning task requirements and the type of at least one cleaning accessory; calculating the grasping action parameters for grasping the target cleaning accessory based on the relative pose between the cleaning device and the target cleaning accessory, and generating the accessory grasping parameter set of the cleaning strategy.

[0135] For example, the grasping action parameters can be understood as parameters that control the execution of the grasping action. These parameters can be of various types, such as grasping angle parameters, force parameters, and speed parameters. For instance, when controlling a robotic arm with a gripper at its end to perform a grasping action, the grasping parameter set may include the gripper's opening and closing degree, the rotation angle of the robotic arm joints, the spatial position of each joint of the robotic arm, and the moving speed of the robotic arm.

[0136] When identifying cleaning accessories in the accessory compartment, it is possible that not just one cleaning accessory is stored, but rather a combination of...Multiple cleaning accessories are available, therefore, it is necessary to determine the target cleaning accessory that is more advantageous for completing the cleaning task based on the cleaning task requirements.

[0137] For example, absorbent cloth and brush are identified in the accessory compartment. For the cleaning task requirement of cleaning water on the table, by analyzing the cleaning characteristics of the water (easy to flow), the absorbent cloth can be identified as the cleaning accessory that is more advantageous for completing the cleaning task among the absorbent cloth and brush. Therefore, the absorbent cloth can be identified as the target cleaning accessory that meets the cleaning task requirements.

[0138] When grasping the target cleaning accessory to perform the cleaning task, in order to grasp the target cleaning accessory more accurately and maintain a more advantageous gripping posture, the grasping action parameters of grasping the target cleaning accessory can be calculated based on the relative posture between the cleaning device and the target cleaning accessory, and the accessory grasping parameter set in the cleaning strategy can be generated. Specification 13 / 26 pages 17 CN 121489359 A

[0139] For example, the spatial position and posture parameters of the cleaning device body (or the gripping device of the cleaning device) and the target cleaning accessory are obtained respectively. By calculating the spatial position difference and attitude parameter difference between the cleaning equipment body (or the gripping device of the cleaning equipment) and the target cleaning accessory, the relative pose between the cleaning equipment body (or the gripping device of the cleaning equipment) and the target cleaning accessory can be obtained. This relative pose can also be understood as the pose difference. Using the relative pose as the constraint condition for inversely solving the gripping device (such as the robotic arm and its joints), at least one gripping action parameter of the gripping device can be solved inversely, and then the gripping parameter set can be obtained. This gripping parameter set can be understood as part of the cleaning strategy.

[0140] For example, after determining the target cleaning accessory that meets the cleaning task requirements based on the cleaning task requirements and the accessory types of multiple cleaning accessories, the relative pose between the target cleaning accessory and the robotic arm of the cleaning equipment can be determined and calculated by a device for detecting pose, such as a depth camera.

[0141] Based on the relative pose, one or more sets of robotic arm joint solutions can be inversely solved through optimization iterative calculation or prediction using a pre-trained model. Each set of robotic arm joint solutions can include the spatial coordinates of the robotic arm's movement from its current position to the target position (the position where it grasps the target cleaning accessory) and key rotation angles. Motion control of the robotic arm based on the optimal robotic arm solution can enable it to grasp the target cleaning accessory.

[0142] In this embodiment, based on the cleaning task requirements and the type of at least one cleaning accessory, a target cleaning accessory that meets the cleaning task requirements can be determined. This target cleaning accessory helps to complete the cleaning task with high cleanliness and high cleaning efficiency. Based on the relative pose between the cleaning device and the target cleaning accessory, the grasping action parameters for grasping the target cleaning accessory are calculated, thus generating a set of accessory grasping parameters for the cleaning strategy. In this way, based on the cleaning strategy including the accessory grasping parameter set...A cleaning strategy is implemented to successfully grasp the target cleaning accessory, thereby facilitating the execution of the corresponding cleaning task.

[0143] In one possible implementation, the method further includes: grasping the target cleaning accessory using a grasping device of the cleaning equipment according to the accessory grasping parameter set; during the grasping process, detecting the grasping opening degree of the grasping device using a position encoder of the grasping device; and determining whether the grasping device has grasped the target cleaning accessory based on the grasping opening degree.

[0144] For example, the grasping device can be understood as a mechanical structure of the cleaning equipment used to grasp cleaning accessories or cleaning tools, such as a robotic arm installed on the cleaning equipment and its end gripper. For example, a robotic arm equipped with grippers can be used to grasp cleaning accessories such as brushes or absorbent cloths. A position encoder can be understood as a sensor that measures the displacement or angle of the grasping mechanical components of the grasping device and can be used to provide real-time feedback on the grasping status. For example, an absolute encoder can monitor the closing angle of the gripper.

[0145] After the cleaning strategy is generated, the grasping action can be executed by the grasping device, and the grasping opening degree can be monitored in real time by the position encoder. For example, when the gripper closes, the position encoder detects whether the gripping opening degree (the opening degree of the gripper) is less than the width of the holding part of the target cleaning accessory. If it is less, it is determined that the gripping was unsuccessful and a retry logic is triggered. If the gripping opening degree is greater than or equal to the width of the holding part of the target cleaning accessory, it can be determined that the gripping was successful. Alternatively, it can be combined with the force detection and weight detection after gripping to comprehensively determine whether the target cleaning accessory has been gripped, so as to improve the accuracy of the judgment result.

[0146] In the embodiments of this application, the reliability of the gripping action is significantly improved by the closed-loop feedback control of the gripping device, and the success rate of gripping the target cleaning accessory is increased. For example, when gripping the target cleaning accessory, the gripper opening and closing and the force are adjusted in real time by the gripping opening degree, which can reduce the failure or damage to the target cleaning accessory caused by fixed parameters. Since the method of the embodiments of this application combines the feedback closed loop of the detection of the target cleaning accessory, the generation and execution of the cleaning strategy, it can improve the adaptive cleaning capability of the cleaning equipment to complex scenarios.

[0147] For example, if the cleaning equipment is not in the accessory compartment, it lacks the ability to actively identify cleaning tools in the environment, which will lead to the cleaning task not making full use of scene resources, which is not conducive to improving cleanliness and cleaning efficiency. To this end, the method provided in this application embodiment can analyze environmental perception data through a visual model (such as an image recognition model based on deep learning), identify cleaning tools (such as mops, long-handled brushes) in the environment, and generate an adapted cleaning strategy in combination with the cleaning task requirements.

[0148] In one possible implementation, generating a cleaning strategy based on the presence detection result includes: when the presence detection result indicates that the accessory compartment is not in a preset position, collecting environmental perception data of the environment in which the cleaning equipment perceives its surroundings.Data; Based on environmental perception data, a visual model is used to identify whether cleaning tools exist in the environment. Cleaning tools are tools used to assist in cleaning. A cleaning strategy is generated based on the cleaning tools identified in the environment.

[0149] For example, environmental perception data can be understood as environmental information collected by the cleaning device through sensors to identify available cleaning tools. Sensors can be, for example, depth cameras, cameras, lidar, millimeter-wave radar, and / or time-of-flight (TOF) radar. For example, color images and depth information collected by a red, green, blue and depth (RGBD) camera are both environmental perception data.

[0150] Cleaning tools can be understood as tools that can be used to assist in cleaning in addition to cleaning accessories. For example, they can be tools that are not standard equipment provided by the manufacturer and can be found in the environment, such as brushes, small vacuum cleaners, rags, and mops provided or permitted by the user. A visual model can be understood as an image recognition model based on deep learning, which can be used to detect the environment and cleaning tools in the environment. For example, it can be a U-Net. For example, a pre-trained UNet model is used to identify cleaning tools such as mops and long-handled brushes.

[0151] When the accessory compartment is not in place, the cleaning tools available in the environment can be identified by combining environmental perception data with a visual model. For example, the cleaning device collects environmental images through a camera, and the visual model analyzes the object features in the collected environmental images (such as the planar shape of the mop) and generates a cleaning strategy (such as grabbing the mop for cleaning non-ground spaces). This process adapts to tools of different shapes and materials through the generalization ability of the machine learning model, expanding the coverage of cleaning scenarios.

[0152] In the embodiments of this application, the active identification of cleaning tools available in the environment is achieved by collecting environmental perception data and combining it with a visual model. This solution solves the problem that cleaning strategies are difficult to generate when the accessory compartment is not in place. For example, when the user places a mop on the table, the cleaning device can automatically identify and plan a cleaning path at a high place. Through the generalization ability of the machine learning model, it adapts to the identification needs of different tools, significantly improving the flexibility and completion of cleaning tasks.

[0153] The introduction of a visual model solves the problems of dynamics and scene adaptability in cleaning tool identification. The visual model, based on a pre-trained feature library of cleaning tools (such as the planar shape of a mop and the rod-like structure of a long-handled brush), extracts visual features of target objects from real-time image input (such as RGB-D camera data) and matches them with the feature library. The matching results can be further combined with cleaning task requirements (such as cleaning high surfaces or crevices) to generate strategies, for example, prioritizing the planning of high-level cleaning paths when a mop is detected. This approach leverages the generalization ability of machine learning models to adapt to cleaning tools of different shapes and materials.This method not only avoids the limitations of traditional rule matching (such as color thresholds), but also significantly improves the accuracy and scene coverage of environmental tool recognition.

[0154] In one possible implementation, a cleaning strategy is generated based on the cleaning tools identified in the environment, including: determining the tool type of the cleaning tools identified in the environment through a visual model; and generating a cleaning strategy based on the cleaning task requirements and tool types.

[0155] For example, tool type can be understood as the category of cleaning tools, such as mop, long-handled brush, telescopic pole, etc. For example, the planar shape of the mop is identified through a visual model and classified as a "planar cleaning tool". The cleaning task requirements are similar to those described in the above embodiments and can be understood as cleaning goals defined by the user or cleaning equipment.

[0156] The identified cleaning tools are classified by type through a visual model, and a strategy can be generated in combination with the cleaning task requirements. For example, if the task requirement is to clean a high plane and a mop is identified, a cleaning path planning instruction to call the mop is generated. This process ensures the adaptability of the strategy to the actual needs by matching the task requirements with the tool types.

[0157] In this embodiment of the application, the dynamic adaptation of the cleaning strategy is achieved by matching the tool type with the task requirements. For example, when cleaning the crevices, if a brush is detected, a strategy to call the brush is generated; if a mop is detected, it is ignored. This refinement step significantly improves the targeting of the strategy generation, reduces resource waste and improves cleaning efficiency.

[0158] For example, after the cleaning device detects the cleaning tool, it can use the cleaning tool to clean non-dead corner areas or clean dead corner areas.

[0159] In one possible implementation, a cleaning strategy is generated according to the cleaning task requirements and the tool type, including: if the cleaning task requirements include cleaning dead corner areas, determining whether the tool type meets the cleaning task requirements; if the tool type meets the cleaning task requirements, generating a cleaning strategy that includes cleaning dead corner areas according to the tool type.

[0160] For example, a dead corner area can be understood as an area that is difficult for the cleaning device body to clean directly. For example, a cleaning device that uses a bottom vacuum cleaner and a rotating brush to clean the floor is difficult to directly clean non-floor areas and inaccessible crevices. Therefore, cleaning dead zones include non-floor areas and inaccessible crevices.

[0161] Non-floor areas can be understood as any space outside the floor, such as multiple non-floor spaces in a vertical space perpendicular to the floor, such as desktops, cabinet doors, cabinet interiors, and walls. Crevices can be gaps formed between any one or more objects, such as gaps between the floor and the bed, gaps between floorboards, gaps in cable trays, or...The gap between the side wall of the sofa and the wall, etc.

[0162] In addition, for some cleaning devices with certain shapes, such as round disc-shaped robot vacuums, when cleaning the inner corner area of ​​the wall, the robot vacuum may also form a cleaning dead corner area near the inner corner of the wall due to wall obstacle avoidance and other reasons. Through the method of the embodiment of this application, the robot vacuum can grab the mop, brush, etc. by the mechanical arm, so as to perform a more thorough deep cleaning of the cleaning dead corner area of ​​the wall, and improve the cleaning degree.

[0163] The cleaning task requirements can be user-set requirements or requirements generated by the cleaning device itself. For example, if the user issues an instruction for whole house cleaning, the whole house cleaning includes the requirement for cleaning dead corner areas. The cleaning device can analyze the instruction to determine that the cleaning dead corner areas should be cleaned, and then generate a cleaning strategy based on the requirement for cleaning dead corner areas.

[0164] Furthermore, in order to effectively clean the cleaning dead corner areas, the cleaning device can first determine whether the tool type of the identified cleaning tool meets the cleaning task requirements. For example, if the cleaning task requires cleaning the narrow gap between the sofa and the wall that the robot vacuum cannot enter, the robot vacuum can analyze the characteristics of the narrow gap (long and narrow, unable to enter) and combine this with the tool type of the long-bristled brush (a tool that is long enough to reach into the narrow gap). It can then determine that the tool type of the long-bristled brush meets the cleaning task requirements, and a cleaning strategy can be generated based on this long-bristled brush.

[0165] Generating a cleaning strategy based on the tool type that includes cleaning blind spots can be understood as generating a cleaning strategy that effectively cleans blind spots based on the cleaning tool type. For example, when generating a cleaning strategy for cleaning the narrow gap between the sofa and the wall based on the tool type of the long-bristled brush, the cleaning strategy includes the gripping angle of the robotic arm to allow the long-bristled brush to reach into the narrow gap; the cleaning strategy may also include the brushing angle, speed, and number of brushing strokes when brushing the narrow gap; the cleaning strategy may also include the planned movement path of the robot vacuum body and the joint angle of the robotic arm to complete the cleaning task.

[0166] In this embodiment of the application, when the cleaning task requirements include the need to clean blind spots, it can be first determined whether the cleaning tool can be used to clean the blind spots. If so, a corresponding cleaning instruction manual (pages 16 / 26, 20 CN 121489359 A) strategy is generated. In this way, the cleaning equipment can use the cleaning tools found in the environment to clean blind spots, reduce the cleaning residue area, improve the cleaning completion rate, and enhance the user experience.

[0167] In one possible implementation, the cleaning strategy includes a tool grasping parameter set, which includes at least one grasping action parameter for grasping the cleaning tool; according to the cleaning task requirements and the tool type, a cleaning instruction manual is generated.The cleaning strategy includes: when multiple cleaning tools of various tool types are identified, determining the target cleaning tool that meets the cleaning task requirements based on the cleaning task requirements and the tool types of the multiple cleaning tools; calculating the grasping action parameters of the target cleaning tool based on the relative pose between the cleaning device and the target cleaning tool, and generating a tool grasping parameter set for the cleaning strategy.

[0168] For example, the grasping action parameters can be understood as parameters that control the execution of the grasping action, and their types can be various, such as the grasping angle parameters, force parameters, and speed parameters. For example, when controlling a robotic arm with a gripper at the end to perform a grasping action, the grasping parameter set may include the gripping opening degree of the gripper, the rotation angle of the robotic arm joints, the spatial position of each joint of the robotic arm, and the moving speed of the robotic arm.

[0169] When identifying cleaning tools in the environment, more than one cleaning tool may be identified. Therefore, it is necessary to determine the target cleaning tool that is more advantageous for completing the cleaning task based on the cleaning task requirements.

[0170] For example, a long-handled brush, a short-handled brush, and a toothbrush that can be used for cleaning are identified in the environment. To address the cleaning task requirement of cleaning narrow crevices, by analyzing the spatial characteristics (length) of the narrow crevices, among long-handled brushes, short-handled brushes, and toothbrushes that can be used for cleaning, long-handled brushes are identified as the more advantageous cleaning tool for completing the cleaning task. Therefore, long-handled brushes can be determined as the target cleaning tool to meet the cleaning task requirements.

[0171] When grasping the target cleaning tool to perform the cleaning task, in order to accurately grasp the target cleaning tool and maintain a gripping posture that facilitates cleaning, the grasping action parameters of grasping the target cleaning tool can be calculated based on the relative pose between the cleaning device and the target cleaning tool, and a tool grasping parameter set for the cleaning strategy can be generated.

[0172] For example, the spatial position and posture parameters of the cleaning device body (or the gripping device of the cleaning device) and the target cleaning tool are obtained respectively. By calculating the spatial position difference and posture parameter difference between the cleaning device body (or the gripping device of the cleaning device) and the target cleaning tool, the relative pose between the cleaning device body (or the gripping device of the cleaning device) and the target cleaning tool can be obtained. This relative pose can also be understood as a pose difference. Using relative pose as the constraint condition for inversely solving the gripping device (such as a robotic arm and its joints), at least one gripping action parameter of the gripping device can be inversely solved, thereby obtaining a set of gripping parameters, which can be understood as part of the cleaning strategy.

[0173] For example, after determining the target cleaning tool that meets the cleaning task requirements based on the cleaning task requirements and the tool types of multiple cleaning tools, the relative pose between the target cleaning tool and the robotic arm of the cleaning equipment can be determined and calculated by a device that detects pose, such as radar. Based on this relative pose, the relative pose can be calculated through optimization iteration or by using a pre-trained model.By using methods such as prediction, one or more sets of robotic arm joint solutions can be inversely solved. Each set of robotic arm joint solutions can include the spatial coordinates of the robotic arm moving from its current position to the target position (the position where it grasps the target cleaning tool) and the key rotation angle. Based on the optimal robotic arm shutdown solution, the robotic arm can be motion controlled to grasp the target cleaning tool.

[0174] In the embodiments of this application, when multiple cleaning tools of various tool types are identified, a target cleaning tool that meets the cleaning task requirements can be determined according to the cleaning task requirements and the tool types of the multiple cleaning tools. This target cleaning tool helps to complete the cleaning task with high cleanliness and high cleaning efficiency. Based on the relative pose between the cleaning device and the target cleaning tool, the grasping action parameters for grasping the target cleaning tool are calculated, and a tool grasping parameter set for the cleaning strategy can be generated. In this way, based on the cleaning strategy including the tool grasping parameter set, the cleaning strategy can be executed to successfully grasp the target cleaning tool, so as to perform the corresponding cleaning task.

[0175] In one possible implementation, the method further includes: grasping a target cleaning tool using a grasping device of a cleaning device according to a tool grasping parameter set; detecting the grasping opening degree of the grasping device using a position encoder during the grasping process; and determining whether the grasping device has grasped the target cleaning tool based on the grasping opening degree.

[0176] For example, the grasping device can be understood as a mechanical structure of a cleaning device used to grasp cleaning accessories or cleaning tools, such as a robotic arm installed on a cleaning device and its end gripper. For example, a robotic arm equipped with grippers can be used to grasp brushes or mops. A position encoder can be understood as a sensor that measures the displacement or angle of mechanical parts and can be used to provide real-time feedback on the grasping state. For example, an absolute encoder can monitor the closing angle of the gripper.

[0177] After the cleaning strategy is generated, the grasping action can be performed by the grasping device, and the grasping opening degree can be monitored in real time by the position encoder. For example, when the gripper closes, the position encoder detects whether the gripping opening degree (the opening degree of the gripper) is less than the width of the holding part of the target cleaning tool. If it is less, it is determined that the gripping was unsuccessful and a retry logic is triggered; if the gripping opening degree is greater than or equal to the width of the holding part of the target cleaning tool, it can be determined that the gripping was successful. Alternatively, it can be combined with the detection of the force and weight after gripping to comprehensively determine whether the target cleaning tool has been gripped, so as to improve the accuracy of the judgment result.

[0178] In the embodiments of this application, the reliability of the gripping action is significantly improved by the closed-loop feedback control of the gripping device. For example, when gripping a brush, the gripping opening degree is adjusted in real time to adjust the gripper opening and closing and the force, which can reduce the failure or damage to the target cleaning tool caused by fixed parameters. This solution combines the cleaning tool detection, cleaning strategy generation and execution process.The feedback loop enables the cleaning equipment to adapt to complex scenarios.

[0179] In one possible implementation, a cleaning strategy is generated based on the in-situ detection result, including: when the in-situ detection result indicates that the accessory compartment is not in a preset position, calling the environmental map of the cleaning equipment, the environmental map is obtained by the cleaning equipment after spatial scanning and map construction of its environment, the environmental map includes the annotation information of obstacles; based on the annotation information of at least one obstacle in the environmental map, determining at least one cleaning tool in at least one obstacle, the cleaning tool is a tool used to assist cleaning; generating a cleaning strategy based on at least one cleaning tool.

[0180] For example, when the cleaning equipment performs a cleaning task for the first time and every time it performs a task thereafter, the environment of the task space can be scanned and a map can be built, for example, the environment map can be built by the Simultaneous Localization and Mapping (SLAM) algorithm. When building the environment map, the identified items can also be labeled.

[0181] For example, all obstacles can be semantically and positionally labeled. For example, when constructing an environmental map based on the SLAM algorithm, obstacles in the space (such as table legs, walls, long-handled brushes, etc.) are identified through a visual model, and the semantics (item type) of each obstacle and its position coordinates in the environmental map are identified through semantic classification. Both semantics and position coordinates can be the labeling information of the obstacle.

[0182] It should be understood that cleaning equipment may interpret all objects in the environment that block the path as obstacles. Therefore, some cleaning tools that can be used to assist cleaning will be interpreted as obstacles before being identified and tool type identified.

[0183] Further, for example, table legs and walls are fixed obstacles and cannot be used to assist cleaning, while long-handled brushes are obstacles that can be grabbed by the cleaning equipment and can be used to assist cleaning. Therefore, they can be added as cleaning tools in the labeling information. Alternatively, the tool type and position coordinates of the cleaning tool can also be added to the labeling information.

[0184] When a cleaning strategy needs to be generated and executed using cleaning tools according to the cleaning task requirements, the cleaning device can first analyze the tool type by using the annotation information of one or more obstacles already marked in the environmental map to determine whether there are cleaning tools (obstacles) that meet the cleaning task requirements. If there are, the device can move to the cleaning tool according to the location coordinates in the annotation information, and grab it to execute the cleaning task.

[0185] It can be understood that, as can be seen from the above embodiments, when the cleaning device needs to use cleaning tools for cleaning...There are at least two decision-making strategies. One is to perceive the environment and identify and analyze the target cleaning tool based on the environmental perception data. The other is to call the already constructed environmental map and query whether the target cleaning tool exists based on the annotation information of at least one obstacle in the environmental map. These two decision-making strategies can be executed one of them or in sequence.

[0186] In the embodiments of this application, by calling the environmental map of the cleaning device and determining at least one cleaning tool based on the annotation information of the obstacle in the environmental map, a cleaning strategy can be generated based on one or more cleaning tools. This can reduce the energy and computing power consumption caused by environmental perception, cleaning tool identification, etc., and can also obtain cleaning tools that help complete the cleaning task more conveniently and quickly, thereby improving the cleaning speed and execution efficiency.

[0187] In one possible implementation, the method further includes: when the cleaning device captures at least one cleaning tool, collecting environmental perception data of the environment; updating the environmental map based on the environmental perception data to obtain an updated environmental map.

[0188] For example, after the cleaning device captures at least one cleaning tool, the spatial layout of obstacles and the annotation information of obstacles in the environmental map can be updated. The updated environmental map can be used for subsequent navigation path planning and auxiliary cleaning strategy generation.

[0189] For example, after grabbing the long-handled brush, the area occupied by the long-handled brush can be updated into a passable free space in the environmental map, and waypoint search and path planning can be performed in this free space.

[0190] In this embodiment of the application, updating the environmental map after grabbing the cleaning tool can update the obstacle labeling information in the environmental map in real time. For example, after the cleaning tool is grabbed, several passable paths can be cleared, which helps to plan and generate more convenient navigation paths, reduce the situation of cleaning equipment detouring, and improve cleaning efficiency.

[0191] For example, based on the above embodiments, the method of this embodiment of the application can also be optimized based on multi-sensor fusion of accessory compartment in-situ detection.

[0192] For example, in the stage of collecting accessory compartment status identity information, multi-sensor fusion technology is introduced, combining data from NFC card reader, IMU (inertial measurement unit) and visual sensor (such as RGBD camera), and cross-validating the unique identifier and spatial pose of the accessory compartment through a weighted fusion algorithm. For example, when NFC reading fails, a visual sensor is used to identify the QR code or predefined pattern on the surface of the parts compartment, and the spatial pose deviation is corrected using IMU data.

[0193] Based on this, multi-sensor fusion can significantly improve the robustness of parts compartment status detection. In complex scenarios (such as strong light interference, NFC tag damage), the visual sensor and IMU can serve as redundant backups to avoid detection failure caused by the failure of a single sensor.The test failed. In addition, by dynamically adjusting the confidence of each sensor through a weighted algorithm, it can adapt to the detection needs in different environments. For example, in a vibration environment, IMU data is given priority, and in a well-lit environment, visual data is given priority. Ultimately, this solution can achieve high reliability and environmental adaptability in the status detection of the parts compartment, and reduce the false judgment rate.

[0194] For example, based on the above embodiments, the method of this application embodiment can also realize the acceleration of cleaning parts type identification based on edge computing.

[0195] For example, when the on-site detection result indicating that the parts compartment is in place is obtained, the unique identifier of the parts is processed locally by the edge computing module (such as an embedded artificial intelligence chip) built into the cleaning equipment, instead of relying on cloud computing. For example, after the NFC card reader scans the unique identifier of the parts, the edge computing module directly calls the pre-trained lightweight machine learning model (such as a lightweight convolutional neural network) for type identification, avoiding the delay of communication with the cloud.

[0196] Based on this, edge computing can significantly shorten the response time of parts type identification and improve the real-time performance of cleaning strategy generation. In scenarios where users frequently change accessories (such as home users switching between nozzles and brushes according to cleaning needs), localization processing (pages 19 / 26, CN 121489359 A) can reduce communication latency and ensure that the device can quickly adjust its cleaning actions. Furthermore, the low-power design of the edge computing module can extend the battery life of the cleaning device while reducing its dependence on network connectivity, adapting to usage needs in environments with no or weak network access.

[0197] For example, based on the above embodiments, the method of this application embodiment can also achieve enhanced environmental tool recognition based on multimodal data fusion.

[0198] For example, when a location detection result indicating that the accessory compartment is not in place is obtained, the recognition accuracy of cleaning tools in the environment is improved by combining visual models and LiDAR data through multimodal data fusion technology. For example, after the visual model detects an object suspected to be a cleaning tool, the LiDAR verifies its spatial shape (such as the planar features of a mop, the rod-like structure of a long-handled brush) through point cloud data, and performs secondary confirmation by combining it with a preset tool type knowledge base.

[0199] Based on this, multimodal data fusion can effectively solve the problem of misidentification by visual models in complex lighting or occlusion scenarios. For example, in a strong backlight environment, the visual model may mistakenly identify a shadow area as a cleaning tool, while the point cloud data of the LiDAR can provide three-dimensional morphological information to help the system eliminate misjudgments. In addition, this solution can identify more types of cleaning tools (such as self-made tools with non-standard shapes), expand the environmental adaptability of the device, and improve the flexibility and completion of cleaning tasks.

[0200] For example, based on the above embodiments, the method of this application embodiment can also realize adaptive optimization of grasping action parameters based on reinforcement learning.

[0201] For example, in the stage of generating grasping action parameters, a reinforcement learning model (such as a deep Q-network, DQN) is introduced. The model is trained using historical grasping success / failure data, and the grasping parameters (such as the joint angle of the robotic arm and the opening and closing degree of the gripper) are dynamically adjusted. For example, the model generates the optimal grasping strategy based on the type and position of the current accessory / tool ​​and environmental interference factors (such as ground friction), and the results are fed back through the position encoder after execution to optimize the model parameters.

[0202] Based on this, reinforcement learning can enable the grasping strategy to have adaptive capabilities, adapting to the physical characteristics of different accessories / tools (such as the softness of the brush and the rigidity of the mop) and environmental changes (such as the slipperiness of the ground). For example, when grasping a brush that is easy to slip, the model can automatically adjust the opening and closing degree of the gripper to increase friction; when grasping a mop that is easy to deform, the model can optimize the robotic arm path to avoid deformation. Ultimately, this solution can significantly improve the grasping success rate, reduce failures caused by fixed parameters, and reduce the equipment's dependence on manual debugging.

[0203] For example, based on the above embodiments, the method of this application embodiment can also realize real-time gripping feedback closed loop based on a high-precision encoder.

[0204] For example, a high-precision absolute encoder is integrated into the gripping device to monitor the rotation angle of each joint of the robotic arm and the opening and closing degree of the gripper in real time, and to dynamically analyze the feedback data through a sliding time window algorithm. For example, during the gripper closing process, when the encoder detects a sudden drop in the rate of change of the gripper opening degree, the system can determine that it is in contact with the target accessory / tool ​​and trigger a fine-tuning action to optimize the gripping force.

[0205] Based on this, the combination of a high-precision encoder and a sliding time window algorithm can achieve millisecond-level response of gripping feedback, significantly shortening the delay of closed-loop control. For example, when gripping fragile accessories, the system can sense the contact force of the gripper in real time and automatically adjust it to avoid damage to the accessory due to excessive gripping force. In addition, this solution can adapt to the gripping needs of different accessories / tools (such as brushes needing gentle gripping and mops needing stable gripping), improve the precision of gripping actions, and reduce the risk of equipment damage.

[0206] In the prior art, there is a lack of solutions for whether a robotic vacuum cleaner equipped with a robotic arm has grasped a cleaning tool or whether a cleaning accessory is in the accessory compartment. There is also a lack of solutions on how to use cleaning equipment to identify cleaning tools, actively grasp them, and dynamically expand the robotic vacuum cleaner's degrees of freedom based on different cleaning tools. The method provided in this application aims to address these technological gaps. Specification 20 / 26 pages 24 CN 121489359 A

[0207] For a robotic vacuum cleaner equipped with a robotic arm and accessory compartment, when grasping a cleaning accessory, it is necessary to detect whether the accessory compartment is in place; otherwise, it will affect subsequent cleaning tasks. After confirming that the accessory compartment is in place, it is also necessary to detect whether the cleaning accessory is in place and whether it is the target cleaning accessory that meets the requirements of the cleaning task.

[0208] The control method of the cleaning equipment provided in the embodiments of this application will be further described below with reference to Figures 2 and 3. Figure 2 is a schematic diagram of the structure of the control system of the cleaning equipment provided in the embodiments of this application. Figure 3 is a schematic diagram of the control logic of the cleaning equipment provided in the embodiments of this application. The control system of the cleaning equipment shown in Figure 2 can be used to execute any of the methods provided in the embodiments of this application. The control system of the cleaning equipment can be understood as a system including electronic devices and accessory compartments, etc. The control system of the cleaning equipment may include software modules and / or hardware modules.

[0209] As shown in Figure 2, the control system of the cleaning equipment includes a multimodal perception module, an accessory compartment module, a grasping executability analysis module, an accessory and tool usage decision module, and a robotic arm control module.

[0210] Multimodal perception module: equipped with a depth camera and a robotic arm docking signal sensor, it collects three-dimensional environmental information and obstacle attributes (size, material) in real time. Based on the visual artificial intelligence (AI) model, it identifies graspable objects (cleaning accessories or cleaning tools) and calculates their relative pose with the robotic arm.

[0211] For example, cleaning accessories may refer to the accessories in the accessory compartment officially provided by the robot vacuum cleaner. Cleaning tools can refer to small brushes that users can provide for the robot vacuum cleaner if they do not purchase the accessory compartment. The visual AI model can identify the small brushes as tools based on algorithms and training. The robot vacuum cleaner's robotic arm can grasp the cleaning tools for use.

[0212] Accessory compartment module: Equipped with an accessory module that can communicate with the robot vacuum cleaner, the real-time signal assists the robotic arm in grasping accessories. The accessory compartment and the QR codes on the accessories can help the machine identify whether the accessory compartment and accessories are in place. For example, after the robotic arm successfully docks with the accessory via QR code or signal docking module (such as TOF radar), the docking between the robotic arm and the accessory can be controlled.

[0213] Grasping feasibility analysis module: The gripper is equipped with a position encoder, which can determine whether it has opened to the appropriate position. It can be used to detect whether the gripper has grasped the item that needs to be grasped. Based on similar sizes, it ensures that the appropriate tool or item has been grasped. For example, there is a difference in the opening degree of the gripper when the cleaning accessory is successfully grasped and the opening degree of the gripper when the cleaning accessory is not successfully grasped, which can be used to determine whether the cleaning accessory or cleaning tool has been grasped.

[0214] Equipped with a gripper camera, it can identify whether the accessory compartment is in place. For example, the gripper camera can identify whether the QR code of the cleaning accessory exists. Or, it can determine whether the accessory compartment is in place based on the depth.

[0215] Accessory and tool usage decision module: Based on the gripping tool or accessory identified by the visual AI model, it decides different cleaning modes to achieve cleaning of non-floor spaces and narrow spaces. For example, a mode suitable for cleaning baseboards. Including but not limited to cabinet door cleaning modes, such as the robot vacuum cleaner opening the cabinet to clean the cabinet, opening the door to clean the back of the door panel, etc. According to the cleaningThe decision is based on the mapping relationship between the tools or cleaning accessories and the preset multiple cleaning modes and scenes.

[0216] Robotic arm control module: According to the type of cleaning accessories and cleaning tools, the pre-trained grasping strategy is called to plan the obstacle avoidance trajectory. For example, the obstacle is moved to a temporary safe area and the robotic arm is retracted, or the current grasping state is temporarily maintained (for example, when there is no safe area around).

[0217] As shown in Figure 3, the control logic during cleaning is as follows: After starting, the cleaning task begins. It is determined whether the user selects the robotic arm assisted cleaning mode. If not, the current cleaning task is completed; if yes, it is determined whether there is a signal from the accessory compartment. If not, during cleaning, obstacles are identified and the obstacle semantics are marked, and it is determined whether a graspable cleaning tool / accessory for assisted cleaning is identified. If not, the current cleaning task is completed; if yes, the cleaning accessory / cleaning tool is grasped.

[0218] Afterwards, it can be determined whether the grasping is successful. If not, the grasping continues; if yes, the map is updated, and the cleaning equipment navigates to the corresponding vertical cleaning space according to the type of the grasped cleaning accessory / cleaning tool. Determine whether the cleaning accessory listed on page 21 / 26 of the instruction manual (CN 121489359 A) is being retrieved from the accessory compartment. If so, the cleaning accessory is opened remotely, and the robotic arm holds the cleaning accessory and cleans the items in the vertical space. If not, the robotic arm holds the cleaning tool and cleans the items in the vertical space. Afterward, the environmental map can be updated.

[0219] Then, it can be determined whether there is an uncleaned vertical space area. If so, return to the step of retrieving the cleaning accessory / cleaning tool. If not, return the cleaning accessory. If the cleaning tool is retrieved, return it to the cleaning tool area marked by the user or return it to the position where the cleaning tool was retrieved, and perform cleaning device recharging. Afterward, the process ends.

[0220] For example, in the workflow, the user selects the robotic arm assisted cleaning mode; after starting cleaning, a local environmental scan is initiated to identify and pair the cleaning accessory signal and identify the accessory compartment QR code; if no accessory signal or QR code is identified, the robot vacuum can identify the usable cleaning tool during cleaning and perform robotic arm assisted cleaning. Accessory signals assist the robotic arm in docking and gripping cleaning accessories. Alternatively, AI can be used to identify cleaning tools. The degree of gripper opening helps determine whether the corresponding tool has been gripped. A gripper camera can be used to identify whether the correct tool has been gripped. The robotic arm performs gripping and moving operations, synchronously updating the environmental map, which may also include sub-maps such as obstacle maps, narrow space maps, and non-ground cleanable area maps. The robotic arm performs assisted cleaning operations.

[0221] The method provided in this application embodiment breaks through the limitation of traditional sweeping robots that can only clean the ground, allowing the robotic arm to assist in cleaning a wider range of spaces, possessing the ability to clean non-ground spaces. Based on multi-system collaborative optimization, the robotic arm control...The method deeply integrates control, object recognition and path planning algorithms to achieve a technical effect of 1+1>2. In addition, it can also improve scene adaptability, handle cleaning tasks in complex areas such as non-ground spaces and narrow areas, and reduce the need for manual pre-cleaning. The method can also reuse the hardware resources of existing robotic arms and expand functions through software upgrades, which has cost-effectiveness advantages.

[0222] Figure 4 is a schematic diagram of the structure of the control device of the cleaning equipment provided in the embodiment of this application. As shown in Figure 4, the embodiment of this application provides a control device for cleaning equipment. The control device includes:

[0223] A collection module 401, used to collect the status identity information of the accessory compartment, the accessory compartment is used to store cleaning accessories that assist the cleaning equipment in cleaning; a detection module 402, used to detect whether the accessory compartment is in a preset position based on the status identity information of the accessory compartment, and obtain the on-site detection result; a generation module 403, used to generate a cleaning strategy based on the on-site detection result, and execute the cleaning task according to the cleaning strategy.

[0224] In one possible implementation, the detection module 402 is specifically used to: match the parts storage compartment status identity information with a preset parts storage compartment feature model, wherein the parts storage compartment feature model is a model constructed based on features extracted from the parts storage compartment when it is in a preset position; if the match is successful, an in-situ detection result of the parts storage compartment being in the preset position is obtained; if the match fails, an in-situ detection result of the parts storage compartment not being in the preset position is obtained.

[0225] In one possible implementation, the parts storage compartment status identity information includes a unique identifier of the parts storage compartment, and the parts storage compartment feature model includes identifier features extracted from the unique identifier of the parts storage compartment; the detection module 402 is specifically used to: match the unique identifier of the parts storage compartment with the identifier features of the parts storage compartment feature model.

[0226] In one possible implementation, the parts storage compartment status identity information includes the spatial pose of the parts storage compartment, and the parts storage compartment feature model includes the in-situ spatial pose range of the parts storage compartment; the detection module 402 is specifically used to: match the spatial pose of the parts storage compartment with the in-situ spatial pose range of the parts storage compartment feature model.

[0227] In one possible implementation, the generation module 403 is specifically used to: generate a cleaning strategy based on the cleaning accessories in the accessory compartment when the in-situ detection result indicates that the accessory compartment is in a preset position.

[0228] In one possible implementation, the generation module 403 is specifically used to: identify at least one cleaning accessory in the accessory compartment, determine the type of at least one cleaning accessory; and generate a cleaning strategy based on the cleaning task requirements and the type of at least one cleaning accessory. Specification 22 / 26 pages 26 CN 121489359 A

[0229] In one possible implementation, the generation module 403 is specifically used to: identify the accessory unique identifier of at least one cleaning accessory in the accessory compartment, and determine the type of at least one cleaning accessory based on the accessory unique identifier.

[0230] In one possible implementation, the generation module 403 is specifically used to: scan a near-field communication tag on at least one cleaning accessory in the accessory compartment using a near-field communication reader installed on the cleaning device; identify at least one cleaning accessory in the accessory compartment based on the data interaction identification result between the near-field communication reader and the near-field communication tag, and determine the type of at least one cleaning accessory.

[0231] In one possible implementation, the cleaning strategy includes an accessory grasping parameter set, which includes at least one grasping action parameter for grasping the cleaning accessory; the generation module 403 is specifically used to: determine a target cleaning accessory that meets the cleaning task requirements based on the cleaning task requirements and the type of at least one cleaning accessory; calculate the grasping action parameter for grasping the target cleaning accessory based on the relative pose between the cleaning device and the target cleaning accessory, and generate the accessory grasping parameter set of the cleaning strategy.

[0232] In one possible embodiment, the control device further includes a grasping module, which is used to: grasp the target cleaning accessory using the grasping device of the cleaning equipment according to the accessory grasping parameter set; during the grasping process, detect the grasping opening degree of the grasping device using the position encoder of the grasping device; and determine whether the grasping device has grasped the target cleaning accessory based on the grasping opening degree.

[0233] In one possible embodiment, the generation module 403 is specifically used to: collect environmental perception data of the environment in which the cleaning equipment perceives its surroundings when the on-site detection result indicates that the accessory compartment is not in the preset position; identify whether there are cleaning tools in the environment based on the environmental perception data using a visual model, wherein the cleaning tools are tools used to assist in cleaning; and generate a cleaning strategy based on the cleaning tools identified in the environment.

[0234] In one possible embodiment, the generation module 403 is specifically used to: determine the tool type of the cleaning tools identified in the environment using a visual model; and generate a cleaning strategy based on the cleaning task requirements and the tool type.

[0235] In one possible implementation, the generation module 403 is specifically used to: determine whether the tool type meets the cleaning task requirements when the cleaning task requirements include cleaning blind spots; if the tool type meets the cleaning task requirements, generate a cleaning strategy that includes cleaning blind spots based on the tool type.

[0236] In one possible implementation, the cleaning strategy includes a tool grasping parameter set, which includes at least one grasping action parameter for grasping a cleaning tool; the generation module 403 is specifically used to: when multiple cleaning tools of various tool types are identified, determine a target cleaning tool that meets the cleaning task requirements based on the cleaning task requirements and the tool types of the multiple cleaning tools; calculate the grasping action parameters for grasping the target cleaning tool based on the relative pose between the cleaning device and the target cleaning tool, and generate a tool grasping parameter set for the cleaning strategy.

[0237] In one possible implementation, the control device further includes a grasping module, which is used to: grasp a target cleaning tool using a grasping device of the cleaning equipment according to a tool grasping parameter set; during the grasping process, detect the grasping opening degree of the grasping device using a position encoder of the grasping device; and determine whether the grasping device has grasped the target cleaning tool based on the grasping opening degree.

[0238] In one possible implementation, the generation module 403 is specifically used to: when the on-site detection result indicates that the accessory compartment is not in a preset position, call the environmental map of the cleaning equipment, the environmental map being obtained by the cleaning equipment after spatial scanning and map construction of its environment, the environmental map including the annotation information of obstacles; determine at least one cleaning tool among at least one obstacle according to the annotation information of at least one obstacle in the environmental map, the cleaning tool being a tool used to assist in cleaning; and generate a cleaning strategy based on at least one cleaning tool.

[0239] In one possible implementation, the control device further includes an update module, which is used to: collect environmental perception data of the environment when at least one cleaning tool is captured in the cleaning equipment manual (pages 23 / 26, CN 121489359 A); update the environmental map according to the environmental perception data to obtain an updated environmental map.

[0240] The control device for the cleaning equipment provided in this application embodiment can be used to execute the technical solution of the control method of the cleaning equipment in any of the above embodiments of this application. Its implementation principle and technical effect are similar, and will not be repeated here.

[0241] FIG5 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in FIG5, the electronic device of this embodiment may include: at least one processor 501; and a memory 502 communicatively connected to at least one processor; wherein, the memory 502 stores instructions that can be executed by at least one processor 501, and the instructions are executed by at least one processor 501 to make the electronic device perform the method as described in any of the above embodiments.

[0242] Optionally, the memory 502 may be independent or integrated with the processor 501.

[0243] The implementation principle and technical effects of the electronic device provided in this embodiment can be found in the foregoing embodiments, and will not be repeated here.

[0244] This application embodiment also provides a cleaning device, which includes a gripping device for performing a gripping action. The cleaning device is used to implement the method of any of the foregoing embodiments.

[0245] For example, the cleaning device may be a robotic vacuum cleaner, a smart vacuum cleaner, etc.

[0246] This application embodiment also provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, the method of any of the foregoing embodiments is implemented.

[0247] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method of any of the foregoing embodiments.

[0248] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0249] The integrated modules implemented in the form of software functional modules can be stored in a computer-readable storage medium. The software functional modules stored in a storage medium include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0250] It should be understood that the processor may be a central processing unit (CPU) or other general-purpose processors. The processor may also be a digital signal processor (DSP) or an application-specific integrated circuit (ASIC), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly embodied in the execution of the hardware processor, or the execution of the processor by a combination of hardware and software modules.

[0251] The memory may include random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be various media capable of storing program code such as USB flash drive, portable hard drive, read-only memory (ROM), disk or optical disk.

[0252] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof. The storage medium is, for example, static random access memory (SRAM) or electrically erasable programmable read-only memory (EEPROM). Specification page 24 / 26 28 CN 121489359 A

[0253] The storage medium is, for example, an erasable programmable read-only memory (Erasable)The storage medium can also be a read-only memory (EPROM) or a programmable read-only memory (PROM). The storage medium can also be a read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk, etc. The storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0254] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic device or host device.

[0255] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0256] The sequence numbers of the embodiments in this application are merely for description and do not represent the superiority or inferiority of the embodiments. Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0257] Based on this understanding, the technical solution of this application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0258] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0259] It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand...It is understood that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0260] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise expressly stated herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders.

[0261] Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0262] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should all be considered to be within the scope of this specification. Specification 25 / 26 pages 29 CN 121489359 A

[0263] Other embodiments of this application will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of this application that follow the general principles of this application and include common knowledge or customary technical means in the art that are not disclosed in this application. The specification and embodiments are to be regarded as exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0264] It should be understood that this application is not limited to the precise structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims. Instruction manual, 26 / 26 pages, 30 CN 121489359 A, Figure 1, Instruction manual, Figure 1 / 4 pages, 31 CN 121489359 A, Figure 2, Instruction manual, Figure 2 / 4 pages, 32 CN 121489359 A, Figure 3, Instruction manual, Figure 3 / 4 pages, 33 CN 121489359 A, Figure 4, Figure 5, Instruction manual, Figure 4 / 4 pages, 34 CN 121489359 A, CONTROL METHOD FOR CLEANING EQUIPMENT AND CLEANING EQUIPMENT AbstractEmbodiments of the present application provide a control method for cleaning equipment and cleaning equipment, and relate to the technical field of intelligent control. The method includes: collecting state and identity information of an accessory storage, where the accessory storage is used to store cleaning accessories for assisting the cleaning equipment in cleaning; detecting whether the accessory storage is located at a preset position based on the state and identity information of the accessory storage, and obtaining an in-place detection result; generating a cleaning strategy according to the in-place detection result, and executing a cleaning task in accordance with the cleaning strategy. By collecting and adopting the state and identity information of the accessory storage, the method can realize high-reliability in-place detection of the accessory storage. Furthermore, a cleaning strategy conducive to auxiliary cleaning of the cleaning equipment can be formulated based on thein-place detection result, so as to effectively improve the cleaning quality and cleaning efficiency of the cleaning equipment during task execution.

Claims

1. A control method for cleaning equipment, characterized in that, The method includes: Collect the status and identity information of the accessory storage compartment, which is used to store cleaning accessories that assist the cleaning equipment in cleaning; Based on the status and identity information of the parts storage compartment, detect whether the parts storage compartment is in a preset location and obtain an on-site detection result; A cleaning strategy is generated based on the in-situ detection results, and a cleaning task is executed according to the cleaning strategy.

2. The method according to claim 1, characterized in that, The step of detecting whether the parts warehouse is in a preset location based on the parts warehouse status and identity information, and obtaining an on-site detection result, includes: The status and identity information of the parts warehouse is matched with a preset parts warehouse feature model, which is a model constructed based on the features extracted from the parts warehouse in a preset location. If the match is successful, the in-situ detection result of the accessory compartment at the preset location is obtained; If the matching fails, the result of the on-site detection is obtained that the accessory compartment is not in the preset position.

3. The method according to claim 2, characterized in that, The spare parts warehouse status identity information includes the unique identifier of the spare parts warehouse, and the spare parts warehouse feature model includes identifier features extracted from the unique identifier of the spare parts warehouse; The step of matching the status and identity information of the parts warehouse with a preset parts warehouse feature model includes: The unique identifier of the parts warehouse is matched with the identifier features of the parts warehouse feature model.

4. The method according to claim 2, characterized in that, The status and identity information of the parts storage compartment includes the spatial pose of the parts storage compartment, and the feature model of the parts storage compartment includes the in-situ spatial pose range of the parts storage compartment; The step of matching the status and identity information of the parts warehouse with a preset parts warehouse feature model includes: The spatial pose of the parts compartment is matched with the in-situ spatial pose range of the parts compartment feature model.

5. The method according to any one of claims 1-4, characterized in that, The generation of a cleaning strategy based on the in-situ detection results includes: When the in-situ detection results indicate that the accessory compartment is in a preset position, a cleaning strategy is generated based on the cleaning accessories in the accessory compartment.

6. The method according to claim 5, characterized in that, The step of generating a cleaning strategy based on the cleaning accessories in the accessory compartment includes: Identify at least one cleaning accessory in the accessory compartment and determine the type of the at least one cleaning accessory; The cleaning strategy is generated based on the cleaning task requirements and the type of the at least one cleaning accessory.

7. The method according to claim 6, characterized in that, The step of identifying at least one cleaning accessory in the accessory compartment and determining the type of the at least one cleaning accessory includes: Identify the unique identifier of at least one cleaning accessory in the accessory compartment, and determine the type of the at least one cleaning accessory based on the unique identifier.

8. The method according to claim 6, characterized in that, The step of identifying at least one cleaning accessory in the accessory compartment and determining the type of the at least one cleaning accessory includes: Scan the near-field communication tag on at least one cleaning accessory in the accessory compartment using the near-field communication card reader installed on the cleaning equipment; Based on the data interaction identification results between the near-field communication reader and the near-field communication tag, at least one cleaning accessory in the accessory compartment is identified, and the type of the at least one cleaning accessory is determined.

9. The method according to claim 6, characterized in that, The cleaning strategy includes an accessory grasping parameter set, which includes at least one grasping action parameter for grasping the cleaning accessory; generating the cleaning strategy based on the cleaning task requirements and the type of the at least one cleaning accessory includes: Based on the cleaning task requirements and the type of the at least one cleaning accessory, determine the target cleaning accessory that meets the cleaning task requirements; Based on the relative pose between the cleaning device and the target cleaning accessory, the grasping action parameters for grasping the target cleaning accessory are calculated, and the accessory grasping parameter set of the cleaning strategy is generated.

10. The method according to claim 9, characterized in that, The method further includes: The target cleaning accessory is grasped by the grasping device of the cleaning equipment according to the accessory grasping parameter set. During the grasping process, the grasping opening degree of the grasping device is detected by the position encoder of the grasping device; Based on the gripping opening degree, it is determined whether the gripping device has gripped the target cleaning accessory.

11. The method according to any one of claims 1-4, characterized in that, The generation of a cleaning strategy based on the in-situ detection results includes: When the in-situ detection result indicates that the accessory compartment is not in the preset position, environmental perception data is collected to enable the cleaning equipment to perceive its surrounding environment. Based on the environmental perception data, a visual model is used to identify whether cleaning tools exist in the environment, and the cleaning tools are tools used to assist in cleaning. The cleaning strategy is generated based on the cleaning tools identified within the environment.

12. The method according to claim 11, characterized in that, The step of generating the cleaning strategy based on the cleaning tools identified in the environment includes: The visual model is used to determine the tool type of the cleaning tools identified in the environment; The cleaning strategy is generated based on the cleaning task requirements and the tool type.

13. The method according to claim 12, characterized in that, The step of generating the cleaning strategy based on the cleaning task requirements and the tool type includes: If the cleaning task requires cleaning hard-to-reach areas, determine whether the tool type meets the cleaning task requirements. If the tool type meets the cleaning task requirements, a cleaning strategy including cleaning blind spots is generated based on the tool type.

14. The method according to claim 12, characterized in that, The cleaning strategy includes a tool grasping parameter set, which includes at least one grasping action parameter for grasping the cleaning tool; generating the cleaning strategy based on the cleaning task requirements and the tool type includes: When multiple cleaning tools of various tool types are identified, a target cleaning tool that meets the cleaning task requirements is determined based on the cleaning task requirements and the tool types of the multiple cleaning tools. Based on the relative pose between the cleaning device and the target cleaning tool, the grasping action parameters for grasping the target cleaning tool are calculated, and the tool grasping parameter set of the cleaning strategy is generated.

15. The method according to claim 14, characterized in that, The method further includes: The target cleaning tool is grasped by the grasping device of the cleaning equipment according to the tool grasping parameter set; During the grasping process, the grasping opening degree of the grasping device is detected by the position encoder of the grasping device; Based on the gripping opening degree, it is determined whether the gripping device has gripped the target cleaning tool.

16. The method according to any one of claims 1-4, characterized in that, The generation of a cleaning strategy based on the in-situ detection results includes: If the in-situ detection result indicates that the accessory compartment is not in the preset position, the environmental map of the cleaning equipment is invoked. The environmental map is obtained by the cleaning equipment after spatial scanning and map construction of its environment, and the environmental map includes the annotation information of obstacles. Based on the labeling information of at least one obstacle in the environmental map, at least one cleaning tool is identified among the at least one obstacle, and the cleaning tool is a tool used to assist in cleaning; The cleaning strategy is generated based on the at least one cleaning tool.

17. The method according to claim 16, characterized in that, The method further includes: When the cleaning equipment catches at least one cleaning tool, environmental perception data of the environment is collected; The environmental map is updated based on the environmental perception data to obtain an updated environmental map.

18. A cleaning device, characterized in that, The cleaning equipment includes a gripping device for performing a gripping action, and the cleaning equipment is used to perform the method as described in any one of claims 1-17.

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