CN116300974A

CN116300974A - Operation planning, partitioning, operation method, autonomous mobile device and cleaning robot

Info

Publication number: CN116300974A
Application number: CN202310559715.XA
Authority: CN
Inventors: 许开立; 单俊杰
Original assignee: Ecovacs Robotics Suzhou Co Ltd
Current assignee: Ecovacs Robotics Suzhou Co Ltd
Priority date: 2023-05-18
Filing date: 2023-05-18
Publication date: 2023-06-23

Abstract

The embodiment of the application provides a job planning, partitioning, a job method, autonomous mobile equipment and a cleaning robot. Wherein, the method includes: acquiring site characteristic information of a site where the autonomous mobile equipment is located; dividing the place into a plurality of subareas according to the place characteristic information; determining partition characteristic information of each partition in the plurality of partitions; and respectively determining the operation modes adapted during operation of each partition for the autonomous mobile equipment according to the partition characteristic information of each partition. By adopting the technical scheme provided by the application, the partitioned areas can be ensured to be more reasonable, the actual operation requirements can be met, in addition, the determined operation mode can be ensured to be matched with the characteristics of the partitioned areas, and the operation efficiency and effect of the autonomous mobile equipment can be improved.

Description

Operation planning, partitioning, operation method, autonomous mobile device and cleaning robot

Technical Field

The application relates to the technical field of artificial intelligence, in particular to a job planning, partitioning, a job method, autonomous mobile equipment and a cleaning robot.

Background

With the development of artificial intelligence technology, intelligent cleaning devices are becoming more and more popular, such as cleaning robots for home or business use. At present, a cleaning robot cleans a target environment area by area according to partitions, specifically, the target environment to be cleaned is divided into a plurality of partitions, so that the target environment is cleaned area by area.

In the process of partitioning the target environment, the existing cleaning robot usually uses a door as a partition basis or only depends on the morphological structure of the environment to partition, and the partition mode ensures that the partitioned partition can only meet structural rationality and does not meet actual cleaning requirements, so that the partitioned partition is not beneficial to cleaning operation of the cleaning robot, and the cleaning efficiency and the cleaning effect of the cleaning robot are low. In addition, when the existing cleaning robot performs cleaning tasks in the plurality of different partitions, the same set of configuration parameters, such as a moving speed, a rolling brush rotating speed, a water spraying amount and the like, are basically adopted, which is a big reason that the cleaning efficiency and the cleaning effect of the cleaning robot are often lower.

Disclosure of Invention

In view of the above, the present application provides a job planning, partitioning, partition job method, autonomous mobile device, cleaning robot, which solves the above-mentioned problems or at least partially solves the above-mentioned problems.

In one embodiment of the present application, a job planning method is provided. The method comprises the following steps:

acquiring site characteristic information of a site where the autonomous mobile equipment is located;

Dividing the place into a plurality of subareas according to the place characteristic information;

determining partition characteristic information of each partition in the plurality of partitions;

and respectively determining the operation modes adapted during operation of each partition for the autonomous mobile equipment according to the partition characteristic information of each partition.

In another embodiment of the present application, a partitioning method is also provided. The method comprises the following steps:

acquiring environment data of a place where the autonomous mobile equipment is located;

identifying venue characteristic information for the venue based on the environmental data;

partitioning the location according to the location characteristic information.

In yet another embodiment of the present application, a partition job method is also provided. The method comprises the following steps:

determining respective partition characteristic information of a plurality of partitions corresponding to the places where the autonomous mobile equipment is located;

determining operation modes matched with partition characteristic information of each partition for the autonomous mobile equipment respectively;

and controlling the autonomous mobile equipment to execute the job task in each partition by adopting a job mode matched with the partition characteristic information of each partition.

In yet another embodiment of the present application, there is provided an autonomous mobile apparatus including:

A traveling device for providing traveling power for the autonomous mobile apparatus;

a memory storing one or more computer instructions;

and a processor coupled to the memory for executing the one or more computer instructions for implementing the steps of the job planning method or the partitioning method or the partitioned job method described above.

According to the technical scheme, the place is divided into a plurality of subareas based on the place characteristic information of the place where the autonomous mobile equipment (or the cleaning robot) is located, the subareas can be more reasonable due to the aid of the subarea scheme, actual operation requirements can be met, operation of the subsequent autonomous mobile equipment is facilitated, and operation efficiency and effect of the autonomous mobile equipment (or the cleaning robot) are improved. Furthermore, partition characteristic information of each partition in the plurality of partitions is determined, and then an operation mode which is adapted when each partition operates is determined for the autonomous mobile device (or the cleaning robot) according to the partition characteristic information of each partition.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the prior art descriptions, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a job planning method according to an embodiment of the present disclosure;

FIGS. 2 a-2 e are schematic views of different areas that may occur in a venue provided in an embodiment of the present application;

FIG. 3 is a schematic diagram of an operation mode setting principle according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of an operation mode setting principle according to another embodiment of the present application;

FIG. 5 is a flow chart of a partitioning method according to an embodiment of the present disclosure;

FIG. 6 is a flow chart of a partitioning method according to an embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of an autonomous mobile apparatus according to an embodiment of the present application;

FIG. 8 is a schematic plan view of a mall according to an embodiment of the present disclosure;

fig. 9 is a schematic structural diagram of a computer program product according to an embodiment of the present application.

Detailed Description

A cleaning robot is a device that can automatically perform a cleaning task in an environment where cleaning is required. At present, cleaning robots are mainly classified into two types according to use environments: a household cleaning robot for cleaning a household environment, a commercial cleaning robot for cleaning a mall environment. Whether it is a home environment or a commercial environment, the cleaning effect and the cleaning efficiency of the cleaning robot are important indicators affecting the user experience, and the two indicators mainly depend on the cleaning planning logic of the cleaning robot, and the cleaning planning logic includes: partition planning and cleaning operation planning strategies; the cleaning operation planning strategy, i.e. the planned cleaning operation logic (also referred to as cleaning operation mode), can be specifically understood as planning the above-mentioned configuration parameters (such as the moving speed, the rolling brush rotating speed, the water spraying amount, etc. during cleaning).

When the existing cleaning robot is used for cleaning the environment to be cleaned according to the subareas, the subareas are simply made to meet the morphological rationality when the environment is subarea, and the same set of cleaning operation planning strategies (namely the same set of configuration parameters) are basically used for cleaning in each subarea in the cleaning process, so that the actual cleaning efficiency and effect are not high, and particularly in complex and variable commercial scenes, the actual cleaning efficiency and effect of the existing cleaning robot are often poorer. For example, when the existing cleaning robot is used for dividing the environment, the obtained division has special shapes, such as irregular shapes and contours, so that the cleaning operation of the cleaning robot is not facilitated; or, because of more static barriers of some partitions, more dynamic barriers of some partitions, special shapes of some partitions, more open partitions and the like, the conventional cleaning robot simply cleans different partitions by using the same set of cleaning operation planning logic for the partitions with different characteristics, and the characteristics of each partition are not considered, so that the cleaning efficiency and the cleaning effect are inevitably low, and the cleaning efficiency and the cleaning effect are difficult to reach expectations.

For this reason, for better efficiency and the effect that improves cleaning robot clean, this application provides a technical scheme that realizes operation planning (including subregion, operation mode) based on environmental characteristic to cleaning robot. Specifically, firstly, partitioning a place where a cleaning robot is located based on place characteristics of the place to obtain a plurality of partitions; and then, combining the partition characteristics of each partition to determine a working mode which is matched with the partition characteristics of each partition and is adopted when the cleaning robot works in each partition, so that the cleaning robot can execute cleaning tasks in each partition by using the matched working mode. The above-mentioned places are target environments which need to be cleaned.

In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application.

In some of the flows described in the specification, claims, and drawings described above, a plurality of operations occurring in a particular order are included, and the operations may be performed out of order or concurrently with respect to the order in which they occur. The sequence numbers of operations such as 101, 102, etc. are merely used to distinguish between the various operations, and the sequence numbers themselves do not represent any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different modes, devices, modules, etc., and do not represent a sequential order, and are not limited to the "first" and "second" being different types. Furthermore, the embodiments described below are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.

Fig. 1 is a flow chart illustrating a job planning method according to an embodiment of the present application. The subject of execution of the method may be an autonomous mobile device, and more particularly, a processor within an autonomous mobile device, which may be a device having data processing, computing capabilities, such as a CPU (Central Processing Unit ), microprocessor, or the like. The autonomous mobile device may be an intelligent device with a cleaning function, such as a household or commercial cleaning robot; alternatively, the robot may be an intelligent inspection robot, a public service robot (such as a guidance robot), etc., which is not particularly limited in this application. As shown in fig. 1, the job planning method provided in this embodiment includes the following steps:

101. acquiring site characteristic information of a site where the autonomous mobile equipment is located;

102. dividing the place into a plurality of subareas according to the place characteristic information;

103. determining partition characteristic information of each partition in the plurality of partitions;

104. and respectively determining the operation modes adapted in each partition for the autonomous mobile equipment according to the partition characteristic information of each partition.

In 101, the location is an environment area where the autonomous mobile apparatus needs to operate. Specifically, the venue may be a home venue; alternatively, the system may be a small, medium or large commercial place, such as a mall, a bank hall, a hospital area, an office building, or the like, which is not limited herein.

The autonomous mobile device may be provided with one or more sensors for acquiring data of the autonomous mobile device itself and data related to its surroundings during travel of the autonomous mobile device. For example, in an application scenario, a camera is installed on the autonomous mobile device, and an environmental image in the traveling process can be acquired through the camera, and environmental features are identified based on the environmental image, a corresponding map is constructed, and the like. For another application scenario, a laser radar is further installed on the autonomous mobile device, three-dimensional point cloud data of the surrounding environment in the traveling process can be collected through the laser radar, and environmental features are identified and corresponding maps are constructed based on the collected three-dimensional point cloud data. The autonomous mobile device may construct a map of the corresponding area using, but not limited to, SLAM (Simultaneous Localization and Mapping, instant localization and mapping) technology. Based on this, in one implementation technical solution, the 101 "obtaining location feature information of a location where the autonomous mobile apparatus is located" may specifically include:

1011. Acquiring environmental data of the place;

1012. and identifying location feature information of the location based on the environmental data.

In a specific implementation, the environmental data may be at least one of an environmental image and environmental three-dimensional point cloud data. And identifying the environmental data by using an adaptive identification analysis method, such as an identification model obtained through training of a deep learning neural network, so as to identify the site characteristic information of the site, and carrying out subsequent auxiliary partitioning on the site.

The location characteristic information may include, but is not limited to, at least one of the following: obstacle information, boundary information, ground material information, and area information. The obstacle information may include distribution information (which can reflect the position of the obstacle), quantity information, type, etc., and the obstacle may be a dynamic obstacle (such as a walking person, an animal, a moving mechanical device, etc.), a static obstacle (such as a wall, a foreground, a simulated flower bed, a fixed seat, etc.). The boundary information may include information of an outer boundary, an inner boundary, etc. of the location, where the inner boundary may refer to a boundary formed by an outer contour of a fixed object in the location, such as: a simulated flower bed is arranged in the place, and the outer outline of the simulated flower bed is the inner boundary of the place.

For example, taking environmental data as environmental three-dimensional point cloud data, where the environmental three-dimensional point cloud data includes ground three-dimensional point cloud data of a place, then: based on the ground three-dimensional point cloud data, the ground characteristic information of the place can be identified, so that the ground material information of the place is determined according to the ground characteristic information; the ground characteristic information may be any information capable of reflecting ground characteristics in a place, such as ground texture characteristics. Based on the determined ground material information, the ground material types adopted by the ground in different areas in the place can be determined, wherein the ground material types can be but are not limited to: floor tiles (e.g., tiles), flooring, concrete floors, stone floors, plastic floors, carpeting, and the like. The ground materials are different, the representation areas are different, and the ground material type can be used as characteristic information of the place for assisting the subsequent division of the place.

Further, boundary line information, obstacle information, and the like of the ground can also be identified using the above-described environmental three-dimensional point cloud data in the example.

After the location feature information of the location is obtained, the location can be partitioned by further combining the partitioning rule preset in the embodiment. The partition rules may be preset in advance based on, but not limited to, an area, a boundary shape, the number of obstacles, a ground material type, and the like. Based on this, that is, in one implementation technical solution, the above 102 "dividing the location into a plurality of partitions according to the location feature information" may specifically include:

1021. Acquiring at least one partition rule;

1022. and dividing the place into a plurality of subareas according to the at least one subarea rule according to the place characteristic information.

Wherein the at least one partitioning rule includes, but is not limited to, at least one rule of:

rule (1): dividing the area of the area in the place larger than or equal to a first threshold value into a single partition, and combining the area smaller than the first threshold value with the adjacent area into a partition;

rule (2): dividing different areas of the place, in which the density of the barriers meets different second threshold conditions, into different subareas;

rule (3): dividing a region of the place, the boundary shape of which meets the preset shape condition, into a partition;

rule (4): determining a maximum inscription geometric figure in a region of the place where the boundary shape does not meet the preset shape condition; dividing the area surrounded by the maximum inscribed geometric figure into a partition;

rule (5): dividing a region in the place, the ratio of the length to the width of which is greater than a third threshold value, into a partition;

rule (6): and dividing the area which is not of the same ground material type in the place into a partition.

In the above description, the density of the obstacles in a region reflects the number of obstacles in the region. The first threshold, the second threshold condition, the third threshold, the preset shape condition, etc. may be flexibly set according to the actual situation, which is not limited herein. For example, the first threshold may be set according to a size of a body of the autonomous mobile apparatus; the second threshold condition may include at least one second threshold; the preset shape condition is mainly used to restrict the boundary shape to be a more regular shape, for example, the preset shape condition may include, but is not limited to, triangle, quadrangle (e.g., rectangle, square, parallelogram, trapezoid), hexagon, circle, etc. A boundary shape is considered irregular if it is similar to a scattering shape (e.g., the boundary shape shown in fig. 2d is a ten-hexagon).

The division of the venue into a plurality of zones may be achieved by zoning a map of the venue based on the venue characteristic information in combination with the at least one zoning rule.

For ease of understanding, the specific implementation of step 1022 above is detailed below with respect to at least one partitioning rule described above, to name a few examples.

Example 1, according to the boundary information of a venue, an autonomous mobile device may map the boundary information corresponding to the venue on a map of the venue. Based on the above drawn boundary information, assuming that the map of the location includes three areas as shown in fig. 2a, namely, an area a, an area B, and an area C, wherein the areas of the area a and the area C are both smaller than the first threshold value, and the area of the area C is larger than the first threshold value, then: when dividing the division, even if the boundary shapes of the region a and the region B are regular shapes (i.e., the preset shape condition is satisfied), the region a and the region B are not separately divided into one region due to the small area, but the region a and the region B are combined with the region C adjacent thereto, so that the combined region a, region B and region C are divided into one division.

Example 2, assuming that the second threshold condition in the above-described partitioning rule (2) includes a high density threshold and a low density threshold, the high density threshold is greater than the low density threshold. When the division rule (2) divides a place, a map of the place can be rasterized first; then determining the barrier density of each grid according to the barrier information of the place; if the barrier density of a certain grid is larger than the high density threshold value, the grid belongs to a high density area (or dense area); if the density of the barrier of a certain grid is smaller than or equal to a high density threshold value and is larger than or equal to a low sealing threshold value, the grid belongs to a common density region (or a medium density region or a small amount of density region); if the density of the obstacles of a certain grid is less than the low density threshold, the grid belongs to a low density area (or called idle area). If the densities of the obstacles of two adjacent grids meet the same density threshold condition, for example, the densities of the obstacles of two adjacent grids are both greater than the high density threshold, the two adjacent grids may be combined into one grid. By the method, after the barrier density attribute of each area in the map is analyzed, the areas with different barrier density attributes can be independently divided into different subareas, so that the subsequent operation mode planning is facilitated.

For example, referring to fig. 2b, assuming that the barrier density properties of the region D and the region E in the map are a low density region and a high density region, respectively, the region D is divided into one partition and the region E is divided into the other partition during the division. The hexagram shown in fig. 2b represents an obstacle.

As shown in example 3 and referring to fig. 2c, according to the boundary information of the location included in the map, the boundary shape of the area G is determined to be rectangular (meeting the requirement of the preset shape condition), and the length of the area G is much greater than the width thereof, that is, the ratio of the length to the width of the area G is greater than the third threshold value, and the area G is generally a long corridor area, when the area G is partitioned, the area G is separately partitioned into one partition, and the area F and the area H connected with the area G are not merged and partitioned into one partition. In addition, if the areas of the region H and the region F are both larger than the first threshold and the boundary shape also satisfies the preset shape condition, the region H and the region F are also separately divided into one partition.

In example 4, referring to fig. 2d, according to the boundary information of the location included in the map, it is determined that the area of the region K is greater than the first threshold, but the boundary shape is a ten-hexagon, and the preset shape condition is not satisfied, when dividing the region, the largest inscribed circle (such as the largest inscribed circle shown by the dotted line in fig. 2 d) of the region K is determined first, and then the region surrounded by the largest inscribed circle is divided into one region.

As shown in example 5 and referring to fig. 2e, assuming that the floor material type of the subarea M included in the area L in the map of the location is determined to be a carpet and the remaining area other than the subarea M is a tile according to the floor material information of the location, the subarea M is divided into one subarea and the remaining area other than the subarea M in the area L is divided into another subarea at the time of subarea.

Examples 1 to 5 given above mainly describe partitioning a venue according to a single partitioning rule, and in practice, there is a logic order between at least one partitioning rule, and the logic order of at least one partitioning rule is combined when partitioning. That is, 1022 "dividing the location into a plurality of partitions according to the at least one partition rule according to the location characteristic information" may be implemented by the following specific steps:

determining a logical order of the at least one partitioning rule;

and dividing the place into a plurality of partitions according to the place characteristic information and the logic sequence of the at least one partition rule.

In specific implementation, the logic sequence is as follows: rules relating to area and shape take precedence over rules relating to obstacle density and ground material type; with respect to the logical order between the area-related rule (such as rule (1) described above) and the shape-related rule (such as rule (3), rule (4), rule (5)) and the logical order between the obstacle density-related rule (such as rule (2)) and the ground material type-related rule (such as rule (6)) the present embodiment is not particularly limited, and for example, the area-related rule may take precedence over the shape-related rule and the obstacle density-related rule may take precedence over the ground material type-related rule.

As described above, in the present embodiment, when dividing a place into partitions, the division logic is: the method is characterized in that the method meets the rules of area and shape, and then meets the rules of barrier density and ground material type.

For example, one can continue to see the three regions contained in the venue shown in fig. 2 a: the area of the area A and the area of the area C are smaller than a first threshold value, the area of the area C is larger than the first threshold value, and if the ground of the area A is a carpet, the ground of the area B is a ceramic tile and the ground of the area C is a wood floor, the floor is: according to the following logic sequence of the partitioning rule: in the case of dividing the place, the above-mentioned area-related rule is prioritized over the shape-related rule and the shape-related rule is prioritized over the ground material-related rule, and the ground material of the above-mentioned area a, area B and area C are different, and the boundary shape of the area a and area B is a regular shape (i.e., the preset shape condition is satisfied), but because of the small area, the area a and area B are not separately divided into one division, but the area a and area B are combined with the adjacent area C, so that the combined area a, area B and area C are divided into one division. Although the above-mentioned combined partition includes three floor materials, since the ratio of the carpet floor and the tile floor to the wooden floor is relatively small, when the partition characteristic information of the partition is determined for determining the operation mode of the partition for the autonomous mobile device, the partition type of the partition can be determined as the wooden floor according to the type of the floor materials included in the partition, which does not have great influence on the overall operation efficiency and effect of the autonomous mobile device.

After the division is performed on the location to obtain a plurality of partitions, the characteristic information of each partition can be determined according to the area, the boundary shape, the number of obstacles, the ground material type and the like of each partition, so as to be used as a measurement index for planning the operation mode in each partition for the autonomous mobile equipment. The boundary shape of the partition is the outer boundary shape of the partition. The characteristic information of the partition may include, but is not limited to: area level, obstacle density level, zone shape, zone type (reflecting the type of floor material of the zone). Based on this, if the target partition is one of the plurality of partitions, a specific implementation manner of "determining partition characteristic information of the target partition" in 103 is:

1031. determining the partition shape of the target partition according to at least one of the boundary shape, the ratio of the length to the width of the target partition;

1032. determining the area grade of the target partition according to the area of the target partition;

1033. according to the number of obstacles in the target zone. Determining an obstacle density level of the target zone;

1034. and determining the partition type of the target partition according to the ground material type of the target partition.

In 1031 above, the partition shape of the target partition may be determined as, but not limited to, any one of the following according to at least one of the boundary shape, the ratio of the length to the width of the target partition: circular, long corridor-like, polygonal (e.g., rectangular, six-morph, etc.).

For example, if the boundary shape is regular, resembling a circle, the partition shape of the target partition is determined to be a circle; if the boundary shape is rectangular and the ratio of the length to the width is smaller than or equal to the third threshold value, reflecting that the difference between the length and the width of the partition is smaller, and judging that the partition shape of the target partition is rectangular; if the boundary shape is rectangular, but the ratio of the length to the width is greater than the third threshold value described above, reflecting that the partition has a large difference in length and width, it may be determined that the partition shape of the target partition is a long corridor shape. The boundary shape is a polygon (the number of sides is greater than 4) which can be approximately determined as other shapes than the above.

In 1032, the area levels of the partitions may be divided into a plurality of area levels according to at least one preset area threshold, if two preset area thresholds are preset, the area levels may include the following three levels: big, medium and small. Based on this, it is assumed that two area thresholds are preset in the present embodiment: namely a first area threshold and a second area threshold, wherein the first area threshold is smaller than the second area threshold, and then: if the area of the target partition is smaller than the first area threshold, determining that the area level of the target partition is small; if the area of the target partition is larger than or equal to the first area threshold and smaller than or equal to the second area threshold, judging that the area grade of the target partition is the middle; and if the area of the target partition is larger than the second area threshold, judging that the area grade of the target partition is large.

In 1033, the barrier density level of the partition may be divided into a plurality of levels according to at least one preset barrier number threshold value and the number of barriers in the partition. If two barrier number thresholds are preset, the barrier density levels may include the following three levels: idle, small, dense. Based on this, it is assumed that two obstacle number thresholds are preset in the present embodiment: namely a first barrier number threshold value and a second barrier number threshold value, wherein the first barrier number threshold value is smaller than the second barrier number threshold value, and then: if the number of the barriers in the target partition is smaller than the first barrier number threshold, judging that the barrier density level of the target partition is idle; if the number of the barriers of the target partition is larger than or equal to the first barrier number threshold value and smaller than or equal to the second barrier number threshold value, judging that the barrier density grade of the target partition is small; and if the number of the barriers of the target partition is larger than the second barrier number threshold, judging that the barrier density level of the target partition is dense. Wherein, in determining the obstacle in the subarea, the type of the obstacle is not limited to a static obstacle, but also includes a dynamic obstacle (such as a walking person).

In 1034, the partition type is the reflected type of the ground material of the time division. For example, if the floor material type of the target zone is carpet, the zone type of the target zone is carpet zone; if the floor material type of the target partition is wood floor, the partition type of the target partition is wood floor, and so on.

In the above 104, a job mode matching the partition characteristic information of each partition may be determined for the autonomous mobile apparatus based on the determined partition characteristic information of each partition. That is, partition characteristic information of a partition determines a work mode of an autonomous mobile apparatus in the partition. In the implementation, the determining of the operation mode matched with the partition characteristic information of each partition for the autonomous mobile device can be completed by the autonomous mobile device through calling the corresponding relation between the preset partition characteristic and the operation parameters in the autonomous mobile device; alternatively, the partition characteristic information of each partition may be displayed to the user, and may be manually set by the user, which is not limited herein. Based on this, in step 103, if the target partition is one of the plurality of partitions, then:

in one implementation technical solution, the "determining, for the autonomous mobile apparatus, the operation mode adapted when the target partition operates according to the partition characteristic information of the target partition" in the above 104 may specifically include:

1041. Acquiring a corresponding relation between preset partition characteristics and operation parameters;

1042. and determining the operation parameters matched with the partition characteristic information of the target partition for the autonomous mobile equipment based on the corresponding relation.

In order to facilitate understanding of the steps 1041 to 1042, the following table 1 is used to illustrate an autonomous mobile device as a cleaning robot, to name a few examples. In the following examples, it is assumed that the movement speed, the rotational speed of the brush (front/rear brush), the water jet amount, the suction power, and the cleaning liquid addition amount supported by the cleaning robot are as follows:

the moving speed includes the following speeds which are sequentially increased: 0, a first moving speed, a second moving speed and a third moving speed. The rotating speed of the rolling brush comprises the following rotating speeds which are sequentially increased: 0, a first rotation speed, a second rotation speed and a third rotation speed. The water spray quantity comprises the following water quantity values which are sequentially increased: 0. a first water content value, a second water content value, a third water content value; the induced draft power comprises the following power values which are sequentially increased: 0. a first power value, a second power value and a third power value; the cleaning solution addition amount comprises the following liquid amount values which are sequentially increased: 0. a first fluid quantity value, a second fluid quantity value and a third fluid quantity value. Based on the above-mentioned assumption preconditions,

For example, if the partition characteristic information of the target partition includes: the area grade is large, the partition shape is rectangular, the obstacle density grade is idle, the ground material type is floor tiles such as ceramic tiles, and the moving speed of the cleaning robot can be high so as to ensure that the cleaning efficiency is relatively high; in addition, the cleaning machine can clean floor tiles in modes of mopping, dust pushing, sucking and the like, so that the water spraying amount, the rotating speed of the rolling brush and the adding amount of cleaning liquid can be high, and the floor can be ensured to be effectively cleaned of dirt; the air suction power can be moderate without being too high; the roller brush needs to descend to ensure that the roller brush contacts the ground so as to clean the ground by the roller brush. The suction rake needs to be in a lowered state to be in contact with the ground, thereby creating a sealed space so that some of the sewage scraped from the ground, etc. can be recovered. In other words, the above determination of the plurality of operation parameters 1 matching the partition characteristic information of the target partition for the cleaning robot may be: the moving speed is higher third moving speed, the rotating speed of the rolling brush is higher third rotating speed, the rolling brush and the water absorbing harrow are in a descending state, the water spraying amount is higher third water amount, the air absorbing power is moderate second power value, and the adding amount of the cleaning liquid is higher third liquid amount. That is, the operation mode adapted for the target partition operation determined by the cleaning robot is mode 1, and the mode 1 includes the plurality of operation parameters 1 described above.

For another example, if the partition characteristic information of the target partition includes: the area grade is in, the partition shape is circular, the barrier density grade is a small amount, the ground material type is a carpet, then in order to reduce the risk of falling down in the walking process of the cleaning robot, the moving speed of the cleaning robot can be too high, the cleaning robot can not spray water, can not add cleaning liquid and the like, only the dust pushing and sucking mode can be adopted for cleaning, the rolling brush is in a descending state and has high rotating speed and high air suction power, and the water suction harrow is in an ascending state. In the above, determining a plurality of operation parameters 2 matching the partition characteristic information of the target partition for the cleaning robot may be: the moving speed is the first moving speed with lower moving speed, the rotating speed of the rolling brush is the third rotating speed with higher rotating speed, the rolling brush is in a descending state, the water absorbing harrow is in an ascending state, the water spraying amount is none (namely 0), the air suction power is the third power value with higher moving speed, and the adding amount of the cleaning solution is none (namely 0). That is, the operation mode adapted for the target partition operation determined by the cleaning robot is mode 2, and the mode 2 includes the plurality of operation parameters 2.

For another example, if the partition characteristic information of the target partition includes: the area grade is middle, the partition shape is long corridor shape, the obstacle density grade is idle, the ground material type is floor (wooden floor), then: although the obstacles are fewer, the moving speed of the cleaning robot cannot be too high, because the corridor is narrow, the obstacle cleaning robot cannot avoid rapidly when suddenly appearing, but the moving speed can be moderate to ensure the cleaning efficiency; floor cleaning can be performed, but the water spray amount, the cleaning liquid acceleration amount and the like cannot be too large for protecting the floor. In the above, determining a plurality of operation parameters 3 matching the partition characteristic information of the target partition for the cleaning robot may be: the moving speed and the rotating speed of the rolling brush are respectively moderate second moving speed and second rotating speed, the rolling brush and the water absorbing harrow are in a descending state, and the water spraying amount, the air absorbing power and the cleaning liquid adding amount are respectively moderate second water content value, second power value and second liquid content value. That is, the operation mode adapted for the target partition operation determined by the cleaning robot is mode 3, and the mode 3 includes the plurality of operation parameters 3.

It should be noted here that, in determining the operation parameters matching the partition characteristic information of the target partition, in addition to the parameters such as the moving speed, the rotational speed of the roller brush (front/rear roller brush), the lifting of the roller brush, the lifting of the suction rake, the water jet amount, the suction power, the addition amount of the cleaning liquid, and the like described in the above examples, other operation parameters such as the traveling manner in the target partition, such as traveling along the "bow" -shaped traveling route, traveling along the "back" -shaped traveling route, and the like, may be determined, and the present invention is not limited thereto.

TABLE 1

In another implementation manner, the determining, in the above 104, "determining, for the autonomous mobile apparatus, the operation mode adapted when the target partition operates according to the partition characteristic information of the target partition", includes:

1041' display partition characteristic information of target partition

1042', determining a set job mode in response to a setting operation triggered by a user according to partition characteristic information of the target partition;

1043', determining the set operation mode as an operation mode adapted by the autonomous mobile apparatus when the target partition operates.

In the implementation, multiple operation modes supported by the autonomous mobile equipment can be displayed to a user for the user to select, so that operation mode setting is realized. Alternatively, the user may input the corresponding operation parameters through the corresponding parameter input control, so as to set the operation mode. I.e. the one that is the one. The 1042' "determining the set operation mode in response to the setting operation triggered by the user according to the partition characteristic information of the target partition" may be implemented in any one of the following manners:

Mode one: displaying a plurality of operation modes supported by the autonomous mobile equipment; and in response to a selection operation triggered by a user for the plurality of job modes, determining the selected job mode as the job mode set for the target partition.

Displaying a parameter input control; acquiring operation parameters input by a user through the parameter input control; and determining a set operation mode according to the operation parameters.

For example, referring to fig. 3, assuming that a user triggers a clicking operation on a target partition in a plurality of partitions displayed on a partition display interface of the autonomous mobile device, in response to the clicking operation, partition characteristic information of the target partition may be displayed near the target partition, after the user further clicks a job mode configuration control in a partitionable display page on the partition characteristic information, the user switches to a configuration page, a plurality of job modes supported by the autonomous mobile device, such as job mode 1, job mode 2, job mode 3, and the like, are displayed in the configuration page, and when the plurality of job modes are displayed, a recommendation identifier may be displayed beside the job mode recommended to the user, such as a word "recommended this mode" displayed beside the job mode 2 in the figure, so as to assist the user in quickly selecting a more suitable job mode for the target partition for the autonomous mobile device. Based on the partition characteristic information of the target partition, the user selects any one of the plurality of displayed operation modes as an operation mode set for the autonomous mobile apparatus for the target partition, for example, operation mode 2 with a recommended identification may be selected as a set operation mode, and operation mode 1, operation mode 3, or the like may be selected as a set mode.

For another example, referring to fig. 4, assume that, for a plurality of partitions displayed on a partition display interface of the autonomous mobile apparatus, a clicking operation is triggered on a target partition in the plurality of partitions, at this time, partition feature information of the target partition is displayed in the vicinity of the target partition, after the user further clicks a job mode configuration control in the partition display page with respect to the partition feature information, the user switches to a job parameter configuration page, and the user can configure various job parameters through the job parameter configuration page. For example, taking the moving speed configured on the target partition for the autonomous mobile device as an example, the user may directly input a corresponding moving speed value in the input text box 10, or may click on a triangle identifier on the input text box 10, and at this time, several moving speed level options, such as several choices of high, medium and low, may be displayed, where, when displaying, a recommendation identifier may also be displayed beside the moving speed recommended to the user based on the partition characteristic information of the target partition, such as the word "recommend this parameter" shown beside "in fig. 4, so as to assist the user in quickly selecting a more suitable moving speed for the autonomous mobile device for the target partition. Based on the partition characteristic information of the target partition, the user can select any one of the presented plurality of moving speed level options as the set moving speed value. According to each operation parameter configured by the user aiming at the target partition, the operation mode set by the user aiming at the target partition for the autonomous mobile equipment can be determined.

After determining the operation mode matched with the partition characteristic information of each partition for the autonomous mobile apparatus, the subsequent autonomous mobile apparatus can execute corresponding operation tasks (such as cleaning tasks) in each partition according to the operation mode matched with the partition characteristic information of each partition.

In summary, the technical scheme provided by the embodiment divides the place into a plurality of partitions based on the place characteristic information of the place where the autonomous mobile device is located, and the partition scheme can enable the partitions to be more reasonable, can meet actual operation requirements, is beneficial to the operation of the subsequent autonomous mobile device, and improves the operation efficiency and effect of the autonomous mobile device. Furthermore, partition characteristic information of each partition in the plurality of partitions is determined, and then an operation mode which is adapted when each partition operates is determined for the autonomous mobile device according to the partition characteristic information of each partition.

Fig. 5 shows a flowchart of a partitioning method according to another embodiment of the present application. The subject of execution of the method may be an autonomous mobile device, and more particularly, a processor within the autonomous mobile device, as described in more detail with respect to the autonomous mobile device and the processor above. As shown in fig. 5, the partitioning method provided in this embodiment includes the following steps:

201. acquiring environmental data of a location where an autonomous mobile device is located

202. Identifying venue characteristic information for the venue based on the environmental data;

203. partitioning the place according to the place characteristic information.

For the description of the specific implementation of 201-202, reference may be made to text application related to other embodiments.

In one implementation technical solution, the 203 "partitioning the location according to the location feature information" may specifically include:

2031. acquiring at least one partition rule;

2032. partitioning the place according to the place characteristic information and the at least one partitioning rule.

In the foregoing, the location feature information includes at least one of: obstacle information, boundary information, ground material information, and area information;

The at least one partitioning rule includes at least one of:

dividing a region with the area larger than or equal to a first threshold value in the place into a partition, and combining the region with the area smaller than the first threshold value with an adjacent region into a partition;

dividing different areas of the place, in which the density of the barriers meets different second threshold conditions, into different subareas;

dividing a region of the outer boundary shape in the place meeting the preset shape condition into a partition;

determining a maximum inscription geometric figure in a region of the place where the boundary shape does not meet the preset shape condition; dividing the area surrounded by the maximum inscribed geometric figure into a partition;

dividing a region in the place, the ratio of the length to the width of which is greater than a third threshold value, into a partition;

dividing the area of the same ground material type in the place into a partition.

For a description of the specific implementation of the steps 2031 to 2032, reference may be made to the related content in other embodiments of the above text application, and the description is omitted here.

What needs to be explained here is: for more details of each step in the method provided in this embodiment, reference may be made to the corresponding descriptions in other embodiments of the application for text, which are not repeated here.

Fig. 6 is a schematic flow chart of a partition operation method according to another embodiment of the present application. The subject of execution of the method may be an autonomous mobile device, and more particularly, a processor within the autonomous mobile device, as described in more detail with respect to the autonomous mobile device and the processor above. As shown in fig. 6, the partition operation method provided in this embodiment includes the following steps:

301. determining respective partition characteristic information of a plurality of partitions corresponding to the places where the autonomous mobile equipment is located;

302. determining operation modes matched with partition characteristic information of each partition for the autonomous mobile equipment respectively;

303. and controlling the autonomous mobile equipment to execute the job task in each partition by adopting a job mode matched with the partition characteristic information of each partition.

For a description of the specific implementation of 301-303, see the text above for related content in other embodiments of the application.

In one implementation technical solution, when the target partition is one of multiple partitions, the "determining partition characteristic information of the target partition" in the above 301 may specifically include:

determining the partition shape of the target partition according to at least one of the boundary shape, the ratio of the length to the width of the target partition;

Determining the area grade of a target partition according to the area of the target partition;

determining the barrier density grade of the target partition according to the number of barriers in the target partition;

and determining the partition type of the target partition according to the ground material type of the target partition.

In one implementation technical solution, the step 302 of determining, for the autonomous mobile device, a working mode that matches partition characteristic information of a target partition, may specifically include:

determining an operation mode matched with partition characteristic information of a target partition for the autonomous mobile equipment based on a corresponding relation between preset partition characteristic information and the operation mode; or alternatively

And displaying partition characteristic information of the target partition, and determining the set operation mode as an operation mode matched with the partition characteristic information of the target partition in response to a setting operation triggered by a user according to the partition characteristic information of the target partition.

The technical solution provided by the embodiments of the present application is mainly described from the software perspective, and the technical solution provided by the embodiments of the present application is described from the hardware perspective.

Fig. 7 shows a schematic structural diagram of an autonomous mobile apparatus according to an embodiment of the present application. As shown in fig. 7, the autonomous mobile apparatus includes: a travel device 45, a memory 41, and a processor 42; wherein, the liquid crystal display device comprises a liquid crystal display device,

the travelling device 45 is used for providing travelling power for the autonomous mobile equipment;

the memory 41 storing one or more computer instructions;

the processor 42 is coupled to the memory for executing the one or more computer instructions for carrying out the steps of the method embodiments provided herein above.

The memory may be configured to store various other data to support operations on the autonomous mobile device. Examples of such data include instructions for any application or method operating on the autonomous traveling device. The memory may be implemented by any type of volatile or nonvolatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

Further, as shown in fig. 7, the autonomous traveling apparatus further includes: a power supply assembly 44, a communication assembly 43, a display (not shown), and the like. Only some of the components are schematically shown in fig. 7, which does not mean that the autonomous mobile apparatus comprises only the components shown in fig. 7.

The autonomous mobile apparatus described in the above-described embodiment may be a cleaning robot (may be a household cleaning robot, or may be a commercial robot), a cargo robot, a guidance service robot, a patrol robot, or the like, which is not particularly limited in this embodiment. Preferably, the autonomous mobile apparatus described above is a cleaning robot.

The specific implementation of the cleaning device thereon will vary for different types of cleaning robots. For example, the cleaning robot is a sweeping robot, and accordingly, the cleaning device may include: rolling brushes, side brushes, etc. The cleaning robot is a sweeping and dragging integrated robot, and accordingly, the cleaning device may include: rolling brushes, side brushes, rags, water tanks, etc.

What needs to be explained here is: for specific executable functions of the processor of the autonomous mobile apparatus, reference may be made to the descriptions of the embodiments of the method provided in the present application, and details are not repeated herein. In addition, the functional structures of the autonomous mobile apparatus provided in this embodiment are not detailed, and reference may also be made to the content related to the foregoing embodiments, which is not described herein in detail.

The technical schemes provided in the embodiments of the present application are described below with reference to specific application scenarios.

Taking autonomous mobile devices as an example of a cleaning robot for cleaning a large mall. Assuming that the place where the cleaning robot is located is a mall, the map of the mall is shown in fig. 8, and the map mainly includes a hall, a corridor, some shops and the like, the cleaning robot determines that the hall and the corridor are all floor tiles according to the acquired characteristic information of the obstacles, boundaries, floor materials and the like of the mall, the floor materials in the hall are more obstacles in a part of areas (such as areas shown by dotted lines) in the hall, the length of the corridor is much larger than the width (i.e., the ratio of the length to the width exceeds a preset threshold), and the corridor is divided into one partition 2 and two partitions (i.e., partition 11 and partition 12) according to at least one partition rule preset in the cleaning robot. Then, the area level, the partition shape, the obstacle density level, the floor material type, and other partition characteristic information of each of the partitions 11, 12, and 2 are determined, and further, the operation mode matching the partition characteristic information of the partition 11 is determined as a mode a, the operation mode matching the partition characteristic information of the partition 12 is determined as a mode b, and the operation mode matching the partition characteristic information of the partition 2 is determined as a mode c.

After the above-mentioned subareas and the operation mode setting for each subarea are completed, the cleaning robot responds to the cleaning instruction triggered by the user subsequently, and when the cleaning task is executed according to the subarea based on the map of the market, the cleaning task is executed in the mode 1 when the subarea 11 is used; when the partition 12 is detected to be entered, the operation mode is switched to the mode b, and when the partition 12 is detected to be entered into the partition 2, the operation is again performed, and the operation mode is switched from the mode b to the mode c. In addition, the cleaning robot partitions the market based on the characteristic information of the market, so that partition division is more reasonable, and the actual cleaning requirement can be met. In addition, the cleaning robot performs cleaning tasks in different partitions by adopting the operation modes matched with the partition characteristic information of the partitions, so that the cleaning efficiency and the cleaning effect are improved.

With respect to the detailed implementation description of the partition rule and the job mode that is determined to match the partition characteristic information of the partition in the above examples, reference may be made to the related content in the other embodiments, and the detailed description of this example will be omitted.

In addition to the embodiments provided herein above described, yet another embodiment of the present application provides a computer-readable storage medium storing a computer program; which when executed by a processor as shown in fig. 7 is capable of carrying out the steps or functions in the various method embodiments provided herein.

In particular implementations, the computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device that integrates one or more available media. The usable medium may be a magnetic medium, e.g., floppy disk, hard disk, tape; but also optical media such as digital video discs; but also semiconductor media such as solid state disks. The computer readable storage medium may be volatile or nonvolatile storage medium, or may include both volatile and nonvolatile types of storage medium.

The method embodiments provided in this application may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. Fig. 9 schematically shows a block diagram of a computer program product provided by the present application. The computer program product comprises one or more computer programs/instructions 51 which, when loaded and executed on a processor as shown in fig. 7, perform in whole or in part the steps or functions in the various method embodiments provided herein.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims

1. A job planning method, comprising:

2. The method of claim 1, wherein dividing the venue into a plurality of zones based on the venue characteristic information comprises:

acquiring at least one partition rule;

and dividing the place into a plurality of subareas according to the at least one subarea rule according to the place characteristic information.

3. The method of claim 2, wherein the venue characteristic information comprises at least one of: obstacle information, boundary information, ground material information, and area information.

4. The method of claim 2, wherein the at least one partitioning rule comprises at least one of:

dividing a region of the place, the boundary shape of which meets the preset shape condition, into a partition;

5. The method of any one of claims 1 to 4, wherein a target partition is one of the plurality of partitions;

determining partition characteristic information of the target partition, including:

determining the area grade of the target partition according to the area of the target partition;

6. The method of claim 5, wherein determining, for the autonomous mobile device, a job mode adapted when working within the target partition based on partition characteristic information of the target partition, comprises:

acquiring a corresponding relation between preset partition characteristics and operation parameters;

and determining the operation parameters matched with the partition characteristic information of the target partition for the autonomous mobile equipment based on the corresponding relation.

7. The method of claim 5, wherein determining, for the autonomous mobile device, a job mode adapted at the time of the target partition job based on partition characteristic information of a target partition, comprises:

Displaying partition characteristic information of the target partition;

responding to a setting operation triggered by a user according to partition characteristic information of the target partition, and determining a set operation mode;

and determining the set operation mode as the operation mode adapted by the autonomous mobile equipment when the target partition operates.

8. The method of claim 7, wherein determining the set job mode in response to a set operation triggered by a user according to partition characteristic information of the target partition comprises:

displaying a plurality of operation modes supported by the autonomous mobile equipment; in response to a selection operation triggered by a user for the plurality of job modes, determining the selected job model as a job mode set for a target partition; or alternatively

9. A partitioning method, comprising:

partitioning the location according to the location characteristic information.

10. The method of claim 9, wherein partitioning the venue based on the venue characteristic information comprises:

acquiring at least one partition rule;

partitioning the place according to the place characteristic information and the at least one partitioning rule.

11. The method of claim 10, wherein the venue characteristic information comprises at least one of: obstacle information, boundary information, ground material information, and area information;

the at least one partitioning rule includes at least one of:

12. A method of partitioning operations, comprising:

13. The method of claim 12, wherein the target partition is one of a plurality of partitions;

and determining partition characteristic information of the target partition, including:

14. The method of claim 12 or 13, wherein determining, for the autonomous mobile device, a job pattern matching partition characteristic information of a target partition, respectively, comprises:

determining an operation parameter matched with partition characteristic information of a target partition for the autonomous mobile equipment based on a corresponding relation between preset partition characteristics and operation parameters; or alternatively

15. An autonomous mobile device, comprising:

a memory storing one or more computer instructions;

a processor coupled to the memory for executing the one or more computer instructions for implementing the steps in the job planning method of any one of the preceding claims 1 to 8, or implementing the steps in the partitioning method of any one of the preceding claims 9 to 11, or implementing the steps in the partitioning job method of any one of the preceding claims 12 to 14.

16. The autonomous mobile device of claim 15, wherein the autonomous mobile device is a cleaning robot.