A room intelligent cleaning method and device based on local community discovery

Abstract

The present application relates to the technical field of intelligent household appliances, in particular to a room intelligent cleaning method and device based on local community discovery; the present application takes a movable cleaning robot with a camera and a mechanical arm as a room cleaning device, scans a target room to obtain a current image, compares the current image with an initial image of the target room, and distinguishes a plurality of to-be-processed objects; then the to-be-processed objects are converted and classified into a plurality of to-be-stored nodes and to-be-reset nodes; then the to-be-stored nodes are processed by using local community discovery, a local community is constructed, and the local community with the largest priority is stored, and the process is restarted until all the to-be-stored objects are stored; then the optimal degree of the to-be-reset nodes is calculated, and the to-be-reset node with the smallest optimal degree is reset, and the process is restarted until all the to-be-reset objects are reset. The present application realizes the good application of local community discovery in room cleaning work, and effectively improves the efficiency and intelligent level of room cleaning.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home appliances, and more specifically, to: 1. an intelligent room cleaning method based on local community discovery; 2. a room cleaning device based on local community discovery. Background Art

[0002] With the development of technology, room cleaning has shown a trend towards intelligent development, such as sweeping robots. Some intelligent room cleaning devices have also appeared, such as mobile cleaning robots with robotic arms, but most of these robots rely on manual control and their functions are not perfect.

[0003] Among them, local community discovery is an important technology aimed at discovering community structures in complex networks such as social networks, transportation networks, citation networks and biological networks, and is very important for understanding the characteristics of these complex networks.

[0004] For example, Chinese invention patent CN116720975A discloses a method and system for discovering local communities based on structural similarity. The inventors considered applying local community discovery to room cleaning, but found the method in the aforementioned patent to be too complex and inadequate for room cleaning. Therefore, the inventors proposed a method and device for intelligent room cleaning based on local community discovery. Summary of the Invention

[0005] Based on this, it is necessary to provide a room intelligent cleaning method and device based on local community discovery to address the problem that the existing local community discovery method is too complicated and cannot be well applied to room cleaning work.

[0006] The present invention is achieved by adopting the following technical solutions:

[0007] In a first aspect, the present invention discloses an intelligent room cleaning method based on local community discovery, in which a target room is cleaned by a room cleaning device, wherein the room cleaning device is a mobile cleaning robot equipped with a camera and a robotic arm.

[0008] The intelligent room cleaning method based on local community discovery includes the following steps:

[0009] Step 1: Use the camera to obtain the current image in the target room and compare it with the initial image in the target room to determine whether there are M items G1 to G2 to be processed. M ; The initial image in the target room corresponds to the state where all items in the target room are cleaned up;

[0010] If there are G1~G M , then proceed to step 2; otherwise end;

[0011] Step 2: G1~G M Convert to node representation to get M given nodes O1~O M , and O1~O M Classified into Y nodes to be stored Red1~Red Y 、N nodes to be reset Yellow1~Yellow N ; Y + N = M;

[0012] If N>0, proceed to step 3; otherwise, proceed to step 6;

[0013] Step 3: Based on Red1~Red Y Construct A candidate local communities CR1~CR A ;

[0014] Among them, step three includes:

[0015] S31, from Red1 to Red Y Find A seed nodes s1~s A ;

[0016] S32, the maximum operating distance of the robot arm Max As the threshold, take the a-th seed node S a Construct the ath candidate local community CR for the center a ;a∈[1,A];

[0017] S33, traverse s1~s A , and get CR1~CR A ;

[0018] Step 4: Calculate CR1 to CR A priority;

[0019] The candidate collection local community with the highest priority is used as the current collection local community, and the items to be processed in it are collected;

[0020] Step 5: After the current local community is collected, return to step 1;

[0021] Step 6: Yellow1~Yellow N Perform optimality calculation; take the node to be reset with the smallest optimality as the current reset node, and reset the corresponding item to be reset;

[0022] Step 7: After the current reset node is reset, return to step 1.

[0023] This intelligent room cleaning method based on local community discovery implements the method or process according to an embodiment of the present disclosure.

[0024] In a second aspect, the present invention discloses a room cleaning device based on local community discovery, which is the room cleaning device in the intelligent room cleaning method based on local community discovery disclosed in the first aspect.

[0025] In a third aspect, the present invention discloses a computer program product, comprising a computer program. When the computer program is executed by a processor, the computer program implements the steps of the method for intelligent room cleaning based on local community discovery disclosed in the first aspect.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention uses a mobile cleaning robot equipped with a camera and a robotic arm as a room cleaning device. It first scans the target room to obtain a current image, then compares it with the initial image of the target room to identify several items to be processed. These items are then converted according to the required operations and classified into several nodes to be stored and nodes to be reset. Local community discovery is then used to process the nodes to be stored, constructing local communities. The local communities with the highest priority are stored, and the process is repeated until all items to be stored are stored. The optimality of the nodes to be reset is then calculated, and the nodes to be reset with the lowest optimality are reset. The process is repeated again until all items to be reset are reset. This invention effectively applies local community discovery to room cleaning, effectively improving the efficiency and intelligence of room cleaning. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 This is a flow chart of the intelligent room cleaning method based on local community discovery provided in Example 1 of the present invention;

[0030] Figure 2 This is the flowchart for step three;

[0031] Figure 3 This is the flowchart for step six. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] It should be noted that when a component is referred to as being "mounted on" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component. When a component is considered to be "fixed to" another component, it may be directly fixed to the other component or there may be a central component.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0035] Example 1

[0036] This embodiment 1 discloses a room cleaning device based on local community discovery, which is a movable cleaning robot with a camera and a robotic arm.

[0037] The room-cleaning device is equipped with controllable wheels, such as Mecanum wheels, at its base, allowing it to move freely within the target room. A camera captures the room's contents, while a robotic arm with multiple degrees of freedom manipulates items, such as objects being processed, such as objects being moved or stored.

[0038] This embodiment 1 simultaneously discloses an intelligent room cleaning method based on local community discovery, that is, cleaning the target room by using the above-mentioned room cleaning device.

[0039] See Figure 1 ,The room intelligent cleaning method based on local community discovery includes the following steps:

[0040] Step 1: Use the camera to obtain the current image in the target room and compare it with the initial image in the target room to determine whether there are M items G1 to G2 to be processed. M .

[0041] The initial image in the target room corresponds to a state where all items in the target room are cleaned.

[0042] Specifically, step one includes:

[0043] S11, pre-scanning the cleaned target room to obtain an initial image of the target room;

[0044] S12, using a target detection algorithm (such as YoloV8 algorithm) to process the initial image to obtain all object information in the initial image, such as the type of object, the original location of the object, etc.;

[0045] S13, scanning the target room using a camera from the current position of the room cleaning device to obtain a current image of the target room;

[0046] S14, using a target detection algorithm (such as YoloV8 algorithm) to process the current image in the target room to obtain information about all objects in the current image, such as the type of object, the current location of the object, etc.;

[0047] S15, compare the current image in the target room with all the object information in the initial image to determine whether G1~G M ;

[0048] Among them, if an item to be processed only needs to be moved and does not need to be stored, then the item to be processed is an item to be reset;

[0049] If an item to be processed needs not only to be moved but also to be stored, then the item to be processed is an item to be stored.

[0050] It should be noted that if there are G1~G M , then proceed to step 2; otherwise, end.

[0051] Step 2: G1~G M Convert to node representation to get M given nodes O1~O M , and O1~O M Classified into Y nodes to be stored Red1~Red Y 、N nodes to be reset Yellow1~Yellow N ;Y+N=M.

[0052] Specifically, step 2 includes:

[0053] S21, constructing a three-dimensional rectangular coordinate system using the current position of the room cleaning device as a reference origin;

[0054] S22, obtain the point cloud data of the current image and process it into a three-dimensional model, and obtain G1~G MThe volume and distance from the room cleaning device are matched to the three-dimensional rectangular coordinate system to form O1~O M ;

[0055] S23, according to the type of items to be processed, M Divided into Red1~Red Y 、Yellow1~Yellow N ;

[0056] If a certain item to be processed is an item to be stored, then the given node corresponding to the item to be processed is a node to be stored;

[0057] If a certain item to be processed is an item to be reset, then the given node corresponding to the item to be processed is a node to be reset.

[0058] It should be noted that if N>0, proceed to step three; otherwise, proceed to step six.

[0059] That is, if there are items to be stored, the items to be stored are processed first; otherwise, the items to be restored are processed first.

[0060] Step 3: Based on Red1~Red Y Construct A candidate local communities CR1~CR A .

[0061] See Figure 2 , step three includes:

[0062] S31, from Red1 to Red Y Find A seed nodes s1~s A ;

[0063] Specifically, S31 includes:

[0064] S311, calculate Red1~Red Y Bias Add_Bias1 to Add_Bias Y ;

[0065] in,

[0066] Where Add_Bias y Red y Bias of y∈[1,Y]; Red y Indicates the yth node to be accommodated; Volume_red y Red y The volume of the corresponding items to be processed;

[0067] S312, according to Add_Bias1~Add_BiasY Calculate Red1~Red Y ReLU activation function value ReLU1~ReLU Y ;

[0068] in,

[0069] Where, ReLU y Red y The ReLU activation function value;

[0070] S313, traverse Red1~Red Y , we get s1~s A , and form a seed node set S.

[0071] Among them, if the ReLU activation function value of a node to be accepted is non-zero, the node to be accepted will be used as a seed node.

[0072] S32, the maximum operating distance of the robot arm Max As the threshold, take the a-th seed node S a Construct the ath candidate local community CR for the center a ; a∈[1,A].

[0073] Specifically, S32 includes:

[0074] S321, based on O1~O M Construct an undirected weighted graph;

[0075] Where Graph = {V, E}; V represents the node set in Graph, V = [O1,…,O M ]; E represents the edge set of Graph, which is initially empty;

[0076] S322, assign the given nodes other than S in V to the non-seed node set H; H = [h1,…,h C ]; A+C=M;

[0077] Calculate s a to h c distance Among them, h c Represents the cth non-S given node in H; c∈[1,C];

[0078] S323, if Then in s a 、h c Create an edge between a,c ;

[0079] Traverse s1~sA 、h1~h C , get several edges and update them to E;

[0080] S324, keep S a unchanged, with S a Centered on all e a,c 、h c Form candidate local community CR a .

[0081] S33, traverse s1~s A , and get CR1~CR A .

[0082] Step 4: Calculate CR1 to CR A priority;

[0083] The candidate storage local community with the highest priority is used as the current storage local community, and the items to be processed in it are stored by the robotic arm.

[0084] Among them, CR a Priority σ a for:

[0085]

[0086] In the formula, Num(CR a ∩S) represents the number of seed nodes in CRa; represents the sum of the volumes of items to be processed corresponding to all nodes in CRa; Represents the sum of the volumes of items to be processed corresponding to all nodes in V; Distance_S represents the sum of the distances between the items to be processed and the room cleaning device corresponding to all nodes in V; a Indicates S a The distance between the corresponding items to be processed and the room cleaning device.

[0087] Step 5: After the current local community is collected, return to step 1;

[0088] It should be noted that, considering that there may be obstructed items to be processed in the previous scan, this step must return to step 1 and start again until all items to be stored are stored.

[0089] Step 6: Yellow1~Yellow N Perform optimality calculation; take the node to be reset with the smallest optimality as the current reset node, and reset the corresponding object to be reset through the robotic arm.

[0090] See Figure 3, step six includes:

[0091] S61, obtain Yellow1 to Yellow N The corresponding N destination locations form R destination nodes Destination1 to Destination N ;

[0092] Among them, the nth node to be reset is Yellow n Corresponding to the nth destination node Destination n ; n∈[1,N];

[0093] S62, calculate Destination n to Yellow n distance

[0094] like Then Destination n As the first destination; otherwise, Destination n As a secondary endpoint;

[0095] S63, traverse Destination1~Destination N 、Yellow1~Yellow N , get P first-level endpoints i1~i P , Q secondary endpoints k1~k Q ;P+Q=N;

[0096] If P≠0, proceed to S64; if P=0, proceed to S65;

[0097] S64, calculate i1~i P The optimal degrees of the corresponding P nodes to be reset, and the node to be reset with the smallest optimal degree is taken as the current reset node;

[0098] Among them, the pth first-level terminal i p The corresponding node to be reset is I p ;

[0099] I p The optimal degree ε p for:

[0100]

[0101] Where, Indicates room cleaning equipment to I p The distance of the corresponding items to be processed; Indicates I pThe corresponding items to be processed to i p The distance to the corresponding end point position;

[0102] S65, calculate k1~k Q The optimal degrees of the corresponding Q nodes to be reset, and the node to be reset with the smallest optimal degree is taken as the current reset node;

[0103] Among them, the qth secondary endpoint k q The corresponding node to be reset is K q ;

[0104] K q The optimal degree λ p for:

[0105]

[0106] Where, Indicates room cleaning equipment to K q The distance of the corresponding items to be processed; K q The corresponding items to be processed are k q The distance to the corresponding end point.

[0107] Step 7: After the current reset node is reset, return to step 1.

[0108] Similar to step five, considering that there may be obstructed objects to be processed in the previous scan, this step needs to return to step one and start again until all the objects to be reset are reset.

[0109] Through the above operations, the room cleaning device can realize intelligent cleaning of the target room.

[0110] Example 2

[0111] This embodiment 2 discloses a computer device including a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the room intelligent cleaning method based on local community discovery disclosed in embodiment 1 are implemented.

[0112] This embodiment 2 also discloses a readable storage medium, which stores computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the room intelligent cleaning method based on local community discovery disclosed in embodiment 1 are executed.

[0113] This embodiment 2 further discloses a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the steps of the intelligent room cleaning method based on local community discovery disclosed in embodiment 1.

[0114] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A room intelligent cleaning method based on local community discovery, which cleans the target room through a room cleaning device, characterized in that: The room cleaning device is a movable cleaning robot with a camera and a robotic arm; The intelligent room cleaning method based on local community discovery includes the following steps: Step 1: Use the camera to obtain the current image in the target room and compare it with the initial image in the target room to determine whether there are M items G1 to G2 to be processed. M ; The initial image in the target room corresponds to the state where all items in the target room are cleaned up; Step one includes: S11, pre-scanning the cleaned target room to obtain an initial image of the target room; S12, processing the initial image using an object detection algorithm to obtain information about all objects in the initial image; S13, scanning the target room using a camera from the current position of the room cleaning device to obtain a current image of the target room; S14, using a target detection algorithm to process the current image in the target room to obtain information about all objects in the current image; S15, compare the current image in the target room with all the object information in the initial image to determine whether G1~G M ; Among them, if an item to be processed only needs to be moved and does not need to be stored, then the item to be processed is an item to be reset; If an item to be processed not only needs to be moved but also needs to be stored, then the item to be processed is an item to be stored; If there are G1~G M , then proceed to step 2; otherwise, end; Step 2, G1~G M Convert to node representation to get M given nodes O1~O M , and O1~O M Classified into Y nodes to be stored Red1~Red Y 、N nodes to be reset Yellow1~Yellow N ; Y + N = M; If N>0, proceed to step 3; otherwise, proceed to step 6; Step 3: Based on Red1~Red Y Construct A candidate local communities CR1~CR A ; Among them, step three includes: S31, from Red1 to Red Y Find A seed nodes s1~s A ; S32, the maximum operating distance of the robot arm Max As the threshold, take the a-th seed node S a Construct the ath candidate local community CR for the center a ;a∈[1,A]; S33, traverse s1~s A , get CR1~CR A ; Step 4: Calculate CR1 to CR A priority; The candidate storage local community with the highest priority is selected as the current storage local community, and the robotic arm is used to store the items to be processed in it; Step 5: After the current local community is collected, return to step 1; Step 6: Yellow1~Yellow N Perform optimality calculation; select the node to be reset with the minimum optimality as the current reset node, and reset the corresponding object to be reset through the robotic arm; Step 7: After the current reset node is reset, return to step 1.

2. The intelligent room cleaning method based on local community discovery according to claim 1 is characterized in that: Step 2 includes: S21, constructing a three-dimensional rectangular coordinate system using the current position of the room cleaning device as a reference origin; S22, obtain the point cloud data of the current image and process it into a three-dimensional model, and obtain G1~G M The volume and distance from the room cleaning device are matched to the three-dimensional rectangular coordinate system to form O1~O M ; S23, according to the type of items to be processed, M Divided into Red1~Red Y 、Yellow1~Yellow N ; If a certain item to be processed is an item to be stored, then the given node corresponding to the item to be processed is a node to be stored; If a certain item to be processed is an item to be reset, then the given node corresponding to the item to be processed is a node to be reset.

3. The intelligent room cleaning method based on local community discovery according to claim 2 is characterized in that: S31 includes: S311, calculate Red1~Red Y Bias Add_Bias1 to Add_Bias Y ; in, Where Add_Bias y Red y Bias of y∈[1,Y]; Red y Indicates the yth node to be accommodated; Volume_red y Red y The volume of the corresponding items to be processed; S312, according to Add_Bias1~Add_Bias Y Calculate Red1~Red Y ReLU activation function value ReLU1~ReLU Y ; in, Where, ReLU y Red y The ReLU activation function value; S313, traverse Red1~Red Y , we get s1~s A , and form a seed node set S; Among them, if the ReLU activation function value of a node to be accepted is non-zero, the node to be accepted will be used as a seed node.

4. The intelligent room cleaning method based on local community discovery according to claim 3 is characterized in that: S32 includes: S321, based on O1~O M Construct an undirected weighted graph; Where Graph = {V, E}; V represents the node set in Graph, V = [O1,…,O M ]; E represents the edge set of Graph, which is initially empty; S322, assign the given nodes other than S in V to the non-seed node set H; H = [h1,…,h C ]; A+C=M; Calculate s a to h c distance Among them, h c Represents the cth non-S given node in H; c∈[1,C]; S323, if Then in s a 、h c Create an edge between a,c ; Traverse s1~s A 、h1~h C , get several edges and update them to E; S324, keep S a unchanged, with S a Centered on all e a,c 、h c Form candidate local community CR a .

5. The intelligent room cleaning method based on local community discovery according to claim 1 is characterized in that: In step 4, CR a Priority σ a for: In the formula, Num(CR a ∩S) represents CR a The number of seed nodes in the CRa The sum of the volumes of items to be processed corresponding to all nodes in ; Represents the sum of the volumes of items to be processed corresponding to all nodes in V; Distance_S represents the sum of the distances between the items to be processed and the room cleaning device corresponding to all nodes in V; a Indicates S a The distance between the corresponding items to be processed and the room cleaning device.

6. The intelligent room cleaning method based on local community discovery according to claim 1 is characterized in that: Step six includes: S61, obtain Yellow1 to Yellow N The corresponding N destination locations form R destination nodes Destination1 to Destination N ; Among them, the nth node to be reset is Yellow n Corresponding to the nth destination node Destination n ; n∈[1,N]; S62, calculate Destination n to Yellow n distance like Then Destination n As the first destination; otherwise, Destination n As a secondary endpoint; S63, traverse Destination1~Destination N 、Yellow1~Yellow N , get P first-level endpoints i1~i P , Q secondary endpoints k1~k Q ;P+Q=N; If P≠0, proceed to S64; if P=0, proceed to S65; S64, calculate i1~i P The optimal degrees of the corresponding P nodes to be reset, and the node to be reset with the smallest optimal degree is taken as the current reset node; S65, calculate k1~k Q The optimal degrees of the corresponding Q nodes to be reset are calculated, and the node to be reset with the smallest optimal degree is taken as the current reset node.

7. The intelligent room cleaning method based on local community discovery according to claim 6 is characterized in that: In S64, the pth first-level terminal i p The corresponding node to be reset is I p ; I p The optimal degree ε p for: Where, Indicates room cleaning equipment to I p The distance of the corresponding items to be processed; Indicates I p The corresponding items to be processed to i p The distance to the corresponding end point position; In S65, the qth secondary endpoint k q The corresponding node to be reset is K q ; K q The optimal degree λ p for: Where, Indicates room cleaning equipment to K q The distance of the corresponding items to be processed; K q The corresponding items to be processed are k q The distance to the corresponding end point.

8. A room cleaning device based on local community discovery, characterized in that: It is a room cleaning device in the intelligent room cleaning method based on local community discovery as described in any one of claims 1-7.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the intelligent room cleaning method based on local community discovery are implemented.

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