CN117519125A

CN117519125A - Control method of self-mobile device

Info

Publication number: CN117519125A
Application number: CN202311337800.8A
Authority: CN
Inventors: 史蒂文·马; 约翰·霍夫曼
Original assignee: Positec Power Tools Suzhou Co Ltd
Current assignee: Positec Power Tools Suzhou Co Ltd
Priority date: 2020-02-19
Filing date: 2020-07-06
Publication date: 2024-02-06
Also published as: WO2021164738A1; EP4066078A1; CN113296495A; CN117519126A; CN113296495B

Abstract

The specification provides a control method of a self-mobile device. In one embodiment, a preset recognition model is called first to recognize and divide the image data containing the target working area and the electronic map data of the related coordinate information, which are acquired from the electronic map database, so as to recognize a plurality of working areas and give out boundary graphs of the working areas; further, marking corresponding GPS reference points in the boundary of each working area; a self-moving device regular moving path is generated based on the reference point and the boundary pattern. These movement paths cover all the divided areas. And the self-mobile equipment autonomously completes the walking according to the divided areas and the set path.

Description

Control method of self-mobile device

The present application is a divisional application of chinese invention patent application with application number 202010642954.8, which is filed by applicant at 7/6/2020 and entitled "method, apparatus and automatic working system for route formation of self-moving device".

Technical Field

The specification belongs to the technical field of robots, and particularly relates to a method and a device for dividing an active area of self-moving equipment and forming a path and an automatic working system.

Background

With the development of related technologies of robots, more and more self-moving working devices are gradually appeared and applied to daily work and life of people.

Often, before a specific application, a user is required to manually guide the self-moving working device to move around the boundary of a working area, so that the self-moving working device determines and records the boundary of the working area in charge, and then the self-moving working device can generate a corresponding moving path for the working area.

There is a need for a method of efficiently and automatically dividing an active area of a self-mobile device and generating a moving path of the self-mobile device that can simplify user operations.

Disclosure of Invention

The specification provides a method, a device and an automatic working system for dividing an active area and forming a moving path of a self-moving device, so as to simplify the operation of a user, improve the use experience of the user, improve the efficiency of dividing the active area and generating the moving path of the self-moving device, and solve the technical problems of low efficiency of dividing the active area and generating the moving path, complicated operation of the user and poor use experience in the existing method.

The path forming method of the self-mobile device provided by the specification comprises the following steps:

acquiring electronic map data from an electronic map database; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information;

invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information;

marking at least one reference point on the semantic map or the electronic map data; and forming a moving path by taking the reference point as a starting point and based on the workable area and/or the workable area boundary in the semantic map.

In one embodiment, marking a reference point on the semantic map or electronic map data includes: and automatically determining points in the target working area as the reference points according to the semantic map.

In one embodiment, the automatically determining, according to the semantic map, a point in the target working area as the reference point includes: and automatically determining corner points of the workable area and/or inflection points of the border of the workable area as the reference points.

In one embodiment, marking a reference point on the semantic map or electronic map data includes: displaying the semantic map or the image data of the electronic map to a user; receiving a selection operation of a user for the semantic map or the image data of the electronic map; and marking a reference point on the semantic map or the electronic map data according to the selection operation.

In one embodiment, further comprising: generating a first drive circuit instruction indicating movement to the reference point or a vicinity of the reference point; the self-mobile device executes the first driving circuit instruction based on coordinate information of a reference point through positioning.

In one embodiment, the invoking the preset recognition model, determining the semantic map based on the electronic map data includes: invoking a preset recognition model, and generating one or more subareas based on the electronic map data; the workable area of the semantic map includes the sub-area; the marking the reference point on the semantic map comprises the following steps: at least one reference point is marked at each of said sub-areas.

In one embodiment, further comprising: and generating a second driving circuit instruction, wherein the second driving circuit instruction instructs to move to the reference point corresponding to the other sub-area after one of the reference points completes the movement of the corresponding sub-area so as to start the movement of the other sub-area.

In one embodiment, the semantic map further comprises: the identified passable non-working area and/or passable non-working area boundary, the passable non-working area and/or passable non-working area boundary being associated with the coordinate information; after completing the movement of one corresponding sub-area based on one of the reference points, moving to the reference point corresponding to the other sub-area, including: and after one of the reference points completes the movement of the corresponding one of the sub-areas, the reference point is moved to the reference point corresponding to the other sub-area through the passable non-working area.

In one embodiment, the method further comprises: identifying a boundary of a workable area through visual identification; based on the identified boundary, a third drive circuit instruction is generated that instructs movement along the workable region boundary and/or steering away from the workable region boundary.

In one embodiment, the method further comprises: identifying a boundary of a workable area through visual identification; operating the self-moving equipment to move along the boundary of the workable area, and recording coordinate information in the moving process through positioning; and generating a working area map of the target working area according to the recorded coordinate information.

In one embodiment, forming the movement path includes forming a regular movement path.

In one embodiment, forming a regular movement path includes: generating a reciprocating path, wherein the reciprocating path comprises a front path section and a rear path section, and the rear path section is offset by a preset distance relative to the front path section.

In one embodiment, further comprising: determining whether an obstacle exists at a position which is positioned in front of the current position by a preset distance through visual identification; in case it is determined that there is an obstacle at a position a preset distance in front of the current position, an adjustment path for avoiding the obstacle is generated.

In one embodiment, further comprising: the movement path is generated based on a fusion of positioning signals from inertial navigation, or odometry, or satellite navigation.

In one embodiment, the electronic map database comprises an online satellite map database.

In one embodiment, invoking a preset recognition model, and determining a semantic map based on the electronic map data includes: and carrying out convolution processing on the image data in the electronic map data through the trained neural network model so as to obtain the semantic map.

In one embodiment, the self-moving device comprises a self-moving lawn-processing device and the target work area comprises a target lawn.

The present specification also provides a computer readable storage medium having stored thereon computer instructions, wherein the instructions, when executed, enable the retrieval of electronic map data from an electronic map database; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information; invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; marking at least one reference point on the semantic map or the electronic map data; and forming a moving path by taking the reference point as a starting point and based on the workable area and/or the workable area boundary in the semantic map.

The present specification also provides a path forming apparatus of a self-moving device, including: the acquisition module is used for acquiring the electronic map data from the electronic map database; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information; the identification module is used for calling a preset identification model and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; the marking module is used for marking at least one reference point on the semantic map or the electronic map data; and the moving path forming module is used for forming a moving path by taking the reference point as a starting point and based on the workable area and/or the workable area boundary in the semantic map.

The present specification also provides a path forming apparatus of a self-moving device, including: a memory storing computer readable instructions; a processor that obtains electronic map data from an electronic map database while processing the computer readable instructions; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information; invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; marking at least one reference point on the semantic map or the electronic map data; and forming a moving path by taking the reference point as a starting point and based on the workable area and/or the workable area boundary in the semantic map.

The present specification also provides an automatic working system comprising: the self-mobile device and the application program installed on the user side; the application program comprises: acquisition procedure: acquiring electronic map data from an electronic map database; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information; identification procedure: invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; marking: marking at least one reference point on the semantic map or the electronic map data; the self-mobile device comprises a communication module which is communicated with a client, and coordinate information of the reference point is obtained from the client after the application program is executed; the self-mobile device further comprises a positioning module, and a moving path is formed based on the coordinate information of the reference point by monitoring the output of the positioning module.

The present specification also provides an automatic working system comprising: a self-moving device; the self-moving device includes a housing; the mobile module is arranged on the shell; the control module is used for controlling the moving module to drive the self-moving equipment to move; the self-mobile device further comprises a positioning module for outputting coordinate information of the self-mobile device; the automatic working system further comprises a storage unit for storing: a semantic map generating program comprising: acquiring electronic map data from an electronic map database; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information; invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; a marking program comprising: marking at least one reference point on the semantic map or the electronic map data; the automatic working system executes the semantic map generating program and the marking program so as to determine the coordinate information of the reference point; the control module controls the moving module to drive the self-moving device to move so as to form a moving path based on the coordinate information of the reference point by monitoring the output of the positioning module.

According to the method, the device and the automatic working system for dividing the moving area and forming the moving path of the intelligent self-moving equipment, electronic map data which are acquired from an electronic map database and contain image data of target working areas associated with coordinate information are identified and divided by calling a preset identification model, a plurality of working areas are identified, and boundary graphs of the working areas are given; further, marking corresponding GPS reference points in the boundary of each working area; a self-moving device regular moving path is generated based on the reference point and the boundary pattern. These movement paths cover all the divided areas. And the self-mobile equipment autonomously completes the walking according to the divided areas and the set path. Therefore, the user does not need to guide the self-mobile device to bypass along the target working area in advance, the active area can be automatically divided according to specific business tasks and corresponding moving paths can be generated, the operation of the user is simplified, the use experience of the user is improved, the efficiency of dividing the active area and generating the moving paths of the self-mobile device is improved, and the technical problems of low efficiency of dividing the active area and generating the moving paths, complicated user operation and poor use experience in the existing method are solved.

Drawings

In order to more clearly illustrate the embodiments of the present disclosure, the drawings that are required for the embodiments will be briefly described below, in which the drawings are only some of the embodiments described in the present disclosure, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic view of a scene based on existing methods employing a self-moving lawn-processing apparatus;

FIG. 2 is a flow chart of a method of path formation from a mobile device provided in one embodiment of the present disclosure;

FIG. 3 is a schematic diagram of the structural composition of a self-moving lawn processing apparatus provided by one embodiment;

FIG. 4 is a schematic diagram of one embodiment of a path forming method for a self-mobile device, as provided by the embodiments of the present disclosure, in one example scenario;

FIG. 5 is a schematic diagram of one embodiment of a path forming method for a self-mobile device, as provided by the embodiments of the present disclosure, in one example scenario;

FIG. 6 is a schematic diagram of one embodiment of a path forming method for a self-mobile device, as provided by the embodiments of the present disclosure, in one example scenario;

FIG. 7 is a schematic diagram of one embodiment of a path forming method for a self-mobile device, as provided by the embodiments of the present disclosure, in one example scenario;

FIG. 8 is a schematic diagram of one embodiment of a path forming method for a self-mobile device, as provided by the embodiments of the present disclosure, in one example scenario;

FIG. 9 is a schematic diagram of one embodiment of a path forming method for a self-mobile device, as provided by the embodiments of the present disclosure, in one example scenario;

FIG. 10 is a schematic diagram of one embodiment of a path forming method for a self-mobile device, as provided by the embodiments of the present disclosure, in one example scenario;

FIG. 11 is a schematic diagram of one embodiment of a path forming method for a self-mobile device, as provided by the embodiments of the present disclosure, in one example scenario;

FIG. 12 is a schematic diagram of one embodiment of a path forming method for a self-mobile device, as provided by the embodiments of the present disclosure, in one example scenario;

FIG. 13 is a schematic diagram of one embodiment of a path forming method for a self-mobile device, as provided by the embodiments of the present disclosure, in one example of a scenario;

fig. 14 is a schematic structural composition diagram of a path forming apparatus of a self-moving device provided in one embodiment of the present specification;

Fig. 15 is a schematic structural composition diagram of a path forming apparatus of a self-moving device provided in one embodiment of the present specification;

FIG. 16 (a) is a schematic diagram of a path from a mobile device provided in one embodiment of the present description;

FIG. 16 (b) is a schematic diagram of a path from a mobile device provided by one embodiment of the present description;

fig. 17 is a motion control flow chart of a self-mobile device provided in one embodiment of the present description.

Fig. 18 (a) - (e) are schematic views of active area division according to one embodiment of the present disclosure.

Fig. 19 is a schematic diagram of a boundary correction process according to an embodiment of the present specification.

Fig. 20 is a schematic view of a charging station in one embodiment of the present description.

Fig. 21 is another angular schematic view of the charging station of fig. 20.

Detailed Description

In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.

Considering that before a specific moving path is generated based on the existing path forming method of the self-moving device, a user is usually required to manually guide the self-moving device to walk around the boundary of a target working area which is responsible for the self-moving device, so that the self-moving device collects and records coordinate information of all points on the boundary of the target working area, and after the boundary of the target working area is determined, the self-moving device can automatically generate a corresponding moving path based on the determined boundary of the target working area to perform specific movement and work.

For example, referring to fig. 1, the self-moving device used by the user is a self-moving lawn processing device. Based on the existing method, a user needs to hold a detachable positioning device arranged on a self-moving lawn processing device, walk around a lawn to be mowed and along an actual lawn boundary, so that the positioning device can acquire coordinate information of points on the passed lawn boundary through satellite positioning in the walking process of the user to determine the lawn needing mowing operation and the lawn boundary. After the above-described processing is completed, the self-moving lawn processing apparatus can automatically generate a moving path covering the lawn in which grass is moved to be mowed. However, the above method requires the user to manually take a circle around the lawn boundary with the positioning device of the self-moving lawn processing apparatus, which is cumbersome for the user and increases the workload of the user. Especially when the lawn area to be mowed is large, as shown in fig. 1, the user needs to consume a lot of time and physical power to complete a circle along the lawn boundary with the positioning device of the self-moving lawn processing device, and the use experience of the user is relatively poor.

Therefore, the existing method can generate a corresponding moving path aiming at the target working area as the method depends on the fact that the user is guided by the mobile equipment in advance to detour along the target working area, so that the technical problems of low moving path generation efficiency, complex user operation and poor use experience exist in specific implementation.

In view of the foregoing, it is contemplated that a more convenient and intelligent method may be employed to generate a corresponding movement path for a self-mobile device. Specifically, it is considered that the image data including the target working area and the electronic map data including the related coordinate information acquired from the electronic map database may be identified by using a preset identification model trained in advance based on the convolutional neural network, so as to obtain a semantic map including the identified workable area and/or the boundary of the workable area. And then, corresponding reference points can be marked in the semantic map, and then, based on the reference points, the moving path of the self-moving equipment is generated. Therefore, the user can automatically generate the moving path aiming at the target working area according to specific business tasks only by performing simple operations of selecting and indicating to acquire the electronic map data without personally guiding the self-moving equipment to detour along the target working area, so that the operation of the user is simplified, the use experience of the user is improved, the generation efficiency of the moving path of the self-moving equipment is improved, and the technical problems of low generation efficiency of the moving path, complex operation of the user and poor use experience existing in the existing method are effectively solved.

Based on the above considerations, the present application provides a path forming method for a self-mobile device. Reference may be made to fig. 2. The method can be applied to the self-mobile device, a client device which is used by a user and is associated with the self-mobile device, and a cloud server of a website platform which is communicated with the self-mobile device. Specifically, the method may include the following.

S201: acquiring electronic map data from an electronic map database; wherein the electronic map data includes: image data of the target working area is included, and the image data is associated with coordinate information.

In one embodiment, the image data is associated with GPS coordinate information.

In one embodiment, the self-mobile device may be specifically referred to as a mobile working device. Specifically, the self-moving working device may include a self-moving lawn processing device (e.g., a robotic lawnmower, etc.), a self-moving cleaning device (e.g., an automatic sweeper, an automatic snowplow, etc.), a self-moving monitoring robot, etc. Of course, the above listed self-mobile devices are only one illustrative type. In specific implementation, the self-mobile device may also include other types of self-mobile devices besides the listed devices according to specific application scenarios. The present specification is not limited to this.

In one embodiment, during specific operation, the user may set the working area for the self-mobile device, or the self-mobile device may identify and determine the working area for the self-mobile device. The self-mobile device can generate a corresponding moving path aiming at the working area, then move according to the moving path through positioning and navigation, and complete corresponding business work.

In one embodiment, taking a self-moving lawn-processing apparatus as an example, reference may be made to fig. 3. The self-moving lawn processing apparatus may specifically include: the device comprises a signal transceiver, a processor, a navigator, a cutting assembly, a moving assembly and the like. Wherein the signal transceiver may be used to interact with a client device used by a user. Further, the signal transceiver can also interact with a cloud server. The navigator described above, such as a GPS module, etc., may be used to interact with a satellite navigation system. The processor described above may be used to perform specific data processing. The movement component may be configured to execute instructions to move from the mobile lawn-processing device. The cutting assembly described above may be used to perform work such as mowing.

Of course, the above-listed constituent structures are only illustrative. In the implementation process, according to specific application scenes and working requirements, other component structures besides the listed component structures can be introduced to form the self-moving lawn processing equipment. For example, some self-moving lawn processing apparatuses may further be provided with: attitude detectors, laser rangefinders, direction sensors, cameras, and the like.

In an embodiment, the self-moving lawn processing apparatus may be further provided with an operation control panel, for example, a touch display screen or an operation panel with function keys. In this way, the user can initiate corresponding instructions to control the self-moving lawn processing equipment to execute specific work tasks by performing related operations on the operation control panel.

In one embodiment, the user may also control the self-moving lawn processing device to perform specific work tasks by using a client device separate from the self-moving lawn processing device described above. See fig. 4. The signal transceiver of the client device and the self-moving lawn processing device may be coupled in a wired or wireless manner, so that the client device and the self-moving lawn processing device may interact with information and instructions in a wired or wireless manner.

The client device may specifically include a front-end device applied to a user side and capable of implementing functions such as data input and data transmission. Specifically, the client device may be, for example, a desktop computer, a tablet computer, a notebook computer, a smart phone, a remote controller, etc. Alternatively, the client device may be a software application capable of running in the electronic device described above. For example, it may be an APP running on a mobile phone, etc.

In one embodiment, a user may use a mobile phone with an APP application associated with the self-moving lawn processing apparatus installed in advance as a client device, and may further control the self-moving lawn processing apparatus conveniently through the mobile phone to perform various specific tasks such as determining a boundary of a lawn to be mowed, automatically generating a map of the lawn to be mowed, or mowing the lawn to be mowed.

In one embodiment, the electronic map database may specifically include an online satellite map database. For example, the online satellite map database of Google, the online satellite map database of hundred degrees, the online satellite map database of germany, and the like may be specifically mentioned. The electronic map database stores map data of different location areas, wherein the map data of the location areas includes images (e.g., aerial photographs, etc.) of the corresponding location areas and coordinate information of the corresponding location areas. The coordinate information may specifically include longitude and latitude coordinates and the like.

In one embodiment, the electronic map data may specifically include image data including a target working area, and the image data is associated with coordinate information.

The target working area may be specifically understood as a range area where the user requires the self-mobile device to be responsible for working. Specifically, for example, the target working area may be a lawn in front of a user's door for which the self-moving lawn processing apparatus is required to be responsible for mowing. Or may be a road or the like where the user needs to take charge of snow sweeping from the mobile snowplow. Of course, the above-listed target working areas are only one illustrative illustration. In specific implementation, the target working area may also be other types of range areas according to specific application scenarios. The present specification is not limited to this.

The electronic map data may specifically include image data of a target working area. The image data may be, in particular, aerial photographs of the target working area, scan imaging data of the target working area, or the like. The image data may include a target working area and a neighboring area of the target working area. Specifically, the electronic map data may further carry coordinate information of the target working area. Wherein one coordinate information may be associated with one position point in the image data. Of course, it is considered that the electronic map data itself has a certain accuracy error. Therefore, in the present embodiment, a certain range of tolerable offset errors are allowed for the correspondence between the above-described coordinate information and the position points of the image data.

In one embodiment, the user may set identification information for the target work area, such as an address or GPS coordinates of the target work area, etc., by performing a corresponding operation on the client device; and the client device can access the electronic map database according to the identification information, acquire corresponding electronic map data and send the electronic map data to the self-mobile device. The self-mobile device can acquire the electronic map data, and further the processor of the self-mobile device can carry out subsequent processing on the electronic map data. Of course, in the implementation, the electronic map data may be obtained by the client device in the above manner, and then the client device directly performs the subsequent processing on the electronic map data, without sending the electronic map data to the self-mobile device. In addition, the user may control the self-mobile device to actively acquire the electronic map data by performing a corresponding operation on an operation control panel of the self-mobile device.

In one embodiment, it is considered that the electronic map data of the peripheral area other than the target working area may be further included in the obtained electronic map data based on the identification information of the target working area set by the user, for example, the address of the target working area. For example, in the above manner, the obtained electronic map data according to the residence address set by the user may include, in addition to the electronic map data of the lawn where the user resides, the electronic map data of a part of the lawn where the user resides at home. Obviously, the above-mentioned adjacent-household part of the lawn included in the electronic map data is not a target work area that the user really wants to set.

In order to avoid the situation, the obtained electronic map data is relatively more accurate, and only the electronic map data of the target working area is contained as much as possible, so that the interference of the electronic map data of other peripheral areas on subsequent processing is reduced. In the process of specifically acquiring the electronic map data, the electronic map database can be accessed according to the identification information of the target working area, and the corresponding electronic map data can be acquired; querying a correlation database (e.g., a residence information database of a cell in which the user is located, etc.) to obtain reference data associated with the identification information, such as residence area information of the user, residence yard range information of the user, etc.; and the electronic map data of the peripheral area except the target working area in the electronic map data can be filtered according to the reference data, and only the electronic map data of the target working area is reserved and used as the finally obtained accurate electronic map data.

Of course, when map data is specifically acquired, the specified operation data of the range of the target working area by the user may be acquired at the same time, and then the electronic map database may be accessed by combining the specified operation data of the user and the identification information of the target working area set by the user, so as to acquire the matched electronic map data which corresponds to the identification information and only includes the range of the target working area specified by the user. The specified operation data of the user may specifically be operation data in which the user further draws a range of a specific target working area through gesture operation in the displayed electronic map data corresponding to the identification information of the target working area set by the user.

In one embodiment, the user may also directly perform corresponding operations on the operation control panel of the self-mobile device, set identification information for the target working area, and further the self-mobile device may receive and directly retrieve and download corresponding electronic map data from the electronic map database according to the identification information for the target working area set by the user.

Of course, the above-listed ways of acquiring electronic map data are only illustrative. In the implementation, according to specific application scenarios and processing requirements, other suitable manners may be adopted to obtain corresponding electronic map data from the electronic map database. The present specification is not limited to this.

In one embodiment, and in particular, for example, a user may want to utilize a self-moving lawn-treating device to mow a lawn in his home yard, reference may be made to fig. 5. The user may enter the address of his home in the address input interface of the APP application associated with the self-moving lawn processing device on the used client handset. For example, "XXX way YYY No. in A city", and sends the address to the self-moving lawn-processing apparatus. After receiving the address, the self-mobile lawn processing device may access a corresponding electronic map database through a connected network (e.g., wifi network, etc.), and search and download electronic map data corresponding to the address from the electronic map database. Specifically, the electronic map data may be electronic map data including a preset range area (for example, a square range area of 400 square meters centered on the address) with the address as a center point. See fig. 6. In other embodiments, the user may also input the GPS coordinates of the target lawn in the APP to obtain the corresponding electronic map data. When a user is positioned on a lawn of a courtyard of the user, the GPS coordinate of the current position can be directly obtained through the APP. The self-moving lawn-processing device may also be placed on the target lawn, and the GPS coordinates of the self-moving lawn-processing device obtained. The electronic map data obtained based on the above may actually include, for example, the lawn of the user's neighboring house, etc., and the electronic map data of the surrounding area outside the range of the target working area set by the user (i.e., the lawn of the user's own house yard). At this time, the range information of the residential courtyard belonging to the user can be obtained by querying the residential information database of the community in which the user is located, and the electronic map data of the part of the lawn belonging to the adjacent house above in fig. 6 can be filtered and cut by combining the range information of the residential courtyard, so that the electronic map data only retaining the lawn belonging to the user's own residential courtyard below in fig. 6 can be finally obtained.

Of course, the obtained lawn including the residential courtyard of the user and the electronic map data of the peripheral area of the residential courtyard of the user (for example, the adjacent-home part of the lawn) may be displayed to the user, and the user may be prompted to designate the target working area by gesture or other operations in the displayed electronic map data. At this time, according to the prompt information, the user can circle the range area belonging to the user house lawn under fig. 6 in the displayed electronic map data in fig. 6 through gesture operation, so as to more precisely specify the range of the target working area based on the electronic map data. Further, the electronic map data only including the target working area may be acquired and determined from fig. 6 according to the above-mentioned designated operation of the user, and only the portion of the electronic map data may be processed accordingly.

S203: invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information.

In one embodiment, the preset recognition model may specifically include a model that is trained based on a convolutional neural network model in advance and is capable of recognizing a workable region from image data and/or a boundary of the workable region.

The above-mentioned workable area may be specifically understood as an area in the target working area, where the self-mobile device may normally move into and perform a corresponding working operation. For example, a self-moving lawn processing apparatus identified from image data by a preset identification model can move into a lawn to be mowed that performs mowing work. See fig. 7.

The above-described operable region boundary is understood to be, in particular, a region of extent at the boundary position between the operable region and the non-operable region.

In this embodiment, the above-mentioned preset recognition model can identify not only the workable area, but also the boundary of the workable area, and further identify the non-working area in the image data. The non-working area may specifically include an area other than the target working area in the image data. For example, a street, a house, or the like, or an area of a lawn at a neighboring house, or the like, other than the lawn to be mowed, is identified from the image data by a preset identification model. The non-working area may specifically include a non-passable non-working area into which the mobile device cannot normally move. For example, the area where the obstacle such as a rockery, a pool, or the like is located is identified from the image data by a preset identification model. But may also include a passable non-operational area into which the self-mobile device may normally move but not perform specific business tasks. For example, the area where the aisle, the bridge, etc. are located, etc. is identified from the image data by a preset identification model.

In this embodiment, the objects identified from the image data by the preset identification model are classified and labeled, so as to realize region division, and examples are as follows:

in one embodiment, the semantic map may be specifically understood as map data, which is extracted from electronic map data and includes a workable area and/or a workable area boundary identified by a preset identification model, and carries coordinate information associated with the workable area and/or the workable area boundary. Specifically, the processor of the self-mobile device or the client device used by the user may read and determine, according to the semantic map, which range areas in the range areas corresponding to the electronic map data are operable areas, which range areas are non-operable areas, which range areas are operable area boundaries, and so on.

In this embodiment, a plurality of sub-areas, for example, two lawns separated by a pavement, may be divided by invoking a preset recognition model. Each sub-zone (and/or boundary thereof) may also be identified by invoking a preset identification model to identify the entire yard (and/or boundary thereof).

In an embodiment, in the implementation, the preset recognition model is called to process the electronic map data through the client device, or a processor on the mobile device, or a cloud server according to the above mode, so as to obtain the semantic map.

Specifically, the electronic map data may be input as a model, input into a preset recognition model, and run the preset recognition model. When a preset identification model specifically operates, the image data of the electronic map can be subjected to convolution processing through a convolution neural network trained in the model so as to obtain a corresponding semantic map, and the corresponding semantic map is output as the model. Therefore, the corresponding semantic map can be determined based on the electronic map data.

In one embodiment, when the foregoing preset recognition model is called to process the electronic map data and a clear boundary is not obtained (for example, the boundary between the electronic map data and the adjacent lawn at home is not clear), the user may also manually select the boundary, for example, define on the APP, select a certain offset compensation in the area without the clear boundary, and so on.

S205: at least one reference point is marked on the semantic map or electronic map data.

In one embodiment, the reference point may be understood as a location point associated with the target work area. Specifically, the reference point may be a position point within the operable area, a position point at a boundary position of the operable area, or a position point in a non-operable area that is accessible outside the operable area. The aforementioned passable non-working area is understood to mean, in particular, a region of a range through which the self-mobile device can move, but is not allowed to carry out working operations.

Specifically, for example, refer to fig. 8. The reference point may comprise a location point a at a central location in the lawn to be mowed. It may also comprise an intersection point between two adjacent border lines in the lawn (or a point at a corner position in the lawn), i.e. an inflection point of a corner point of the workable area and/or of a border of the workable area, e.g. point B in fig. 8, etc. It may also be a point C on the aisle that allows the self-moving lawn processing apparatus to move through, but not allow mowing work to take place, etc.

In one embodiment, one or more reference points may be determined and marked in the semantic map described above when embodied. In particular, for example, when the target working area comprises two separated sub-areas, a reference point may be determined and marked in each sub-area in the semantic map, respectively. For another example, for one target working area, due to the requirement of working operation, the center position point, the upper left corner position point and the lower right corner position point 3 of the target working area need to be detected in the field at the same time, and then 3 reference points and the like can be determined and marked in one target working area in the semantic map.

In one embodiment, the division of the work area is automatically generated based on the recognition of the grass and the environment surrounding the grass during the image recognition process and incorporating artificial intelligence. Depending on the situation of the block, one or more working areas (grasslands) may be automatically generated and at least one GPS reference point is obtained in each sub-area.

In one embodiment, during implementation, according to the semantic map, a processor of the client device or the self-mobile device can automatically determine a point in the target range area as the reference point in combination with specific work operation requirements. Meanwhile, the coordinate information of the reference point can be determined and recorded according to the coordinate information of the semantic map.

In one embodiment, the target range area may specifically be an intersection point between two adjacent working area boundary lines of the operable area, that is, a preset range area where the corner point of the operable area and/or the inflection point position of the operable area boundary is located. The target range area may specifically be a preset range area where the center point of the workable area is located, and the like. The area range size of the preset range area can be flexibly set according to specific situations.

Specifically, for example, as shown in fig. 9, a circular range region with a radius of 1 meter centered on an intersection point D between two boundary lines at the upper right corner of the lawn to be mowed may be used as the target range region.

Of course, it should be noted that the above-listed target area is only a schematic illustration. In specific implementation, the target range area may also be other types of range areas according to specific application scenarios.

In an embodiment, during implementation, a target range area can be automatically determined according to a semantic map, and then a point meeting a preset requirement is further determined from the target range area to serve as the reference point. Of course, it is also possible to directly determine points meeting the preset requirements as the above-mentioned reference points, for example, to determine corner points of the workable area and/or inflection points of the border of the workable area as the reference points.

The point meeting the preset requirement can be a position point reached by the mobile equipment, or a point located in the workable area in the target range area.

Specifically, for example, as shown in fig. 9, a point E located in the workable area is determined as a reference point from the target range area.

In one embodiment, in the implementation, in order to improve the use experience of the user, the user can conveniently and flexibly select the point meeting the requirement as the reference point, and in the implementation, the image data of the semantic map or the electronic map can be displayed to the user first, and the user can receive the selection operation of the image data of the semantic map or the electronic map. And determining the position point selected by the user as a reference point according to the operation, and marking the reference point in the semantic map or the electronic map data. Further, the coordinate information of the reference point may be determined and recorded according to the coordinate information of the map.

In one embodiment, the processor of the self-mobile device or the client device may automatically determine one or more reference points as recommended points. The recommendation points are displayed simultaneously in the semantic map displayed to the user. The user can click on the recommended point in the displayed map to perform operation, and the recommended point selected by the user can be determined to be the final reference point. Of course, the user may perform the operation without clicking any recommended point, but clicking another point in the map, and the point clicked by the user may be determined as the final reference point.

Specifically, for example, as shown in fig. 10, the processor of the self-moving lawn processing apparatus may automatically find a plurality of points (for example, intersections between a plurality of adjacent boundary lines in the lawn) meeting requirements from the lawn, and determine the plurality of points as recommended points, and then send the plurality of recommended points to the client apparatus. Further, the client device may present a map on which a plurality of recommended points are displayed to the user through the screen. At this time, the user may perform corresponding operations in the map displayed by the client device according to his own plan or preference to select a recommendation point that he/she wants to use as a reference point. For example, the user may click on point E of the presented plurality of recommended points on the cell phone display. And the mobile phone can receive and respond to the selection operation of the user, and finally determine the recommendation point E selected by the user as a reference point.

In this embodiment, the self-moving lawn processing apparatus described above may be further provided with a display apparatus such as a display screen. In a specific implementation, the self-moving lawn processing device may also directly display a map with a plurality of recommendation points displayed to the user through the display device. The user may perform a selection operation among a plurality of recommended points presented from the mobile lawn processing apparatus to select a certain recommended point. The self-moving lawn processing device may receive and determine a recommendation point selected by the user from a plurality of recommendation points as a reference point according to a selection operation of the user.

In one embodiment, when the reference point is specifically marked, the reference point may be marked on the semantic map, or the reference point may be marked on the electronic map data. Specifically, two data selections for marking the reference points can be provided for the user according to the preference of the user; and receiving and responding to the selection operation of the user, and marking the reference point on the semantic map selected by the user or the electronic map data selected by the user. Therefore, the method can meet the diversified marking requirements of different users, and further improves the use experience of the users.

S207: and forming a moving path by taking the reference point as a starting point and based on the workable area and/or the workable area boundary in the semantic map.

In one embodiment, the above-mentioned moving path may be understood as a line which moves correspondingly from the mobile device in the operable area or in the passable non-operable area.

In an embodiment, during implementation, a corresponding moving path can be flexibly formed according to the reference point according to a specific application scenario and a specific work task to be executed by the mobile device at present.

In one embodiment, the movement path may be a movement path indicating movement to a reference point or a vicinity of the reference point. The self-moving device can automatically move into the reference point or the adjacent range area of the reference point based on the moving path. The moving path may be a moving path formed by using the reference point as a starting point of the path. For example, a round trip movement path covering the workable area may be started with the reference point. The above-described movement path may also be a movement path for indicating movement from one sub-area to another sub-area that is separated. The movement path may also be a movement path that moves along the boundary of the workable area. The above-described movement path may also be a movement path that indicates steering to move away from the boundary of the workable area, or the like.

Of course, the above listed travel paths are only one illustrative illustration. In specific implementation, the moving paths may specifically include other types of moving paths besides the listed moving paths according to specific application scenarios and work tasks. The present specification is not limited to this.

In one embodiment, in implementation, the driving circuit instruction related to the moving path may be generated according to a specific application scenario and a work task. The self-moving device can acquire the driving circuit instruction, and execute the driving circuit instruction based on the coordinate information of the reference point through positioning so as to control the self-moving device to move according to the moving path.

In one embodiment, referring to FIG. 11 in particular, a first movement path may be generated by a client device or from a process of the mobile device that indicates movement from a current location to a reference point or a proximate range area of the reference point. Correspondingly, a first drive circuit instruction is generated. After the self-moving device receives the first driving circuit instruction, the self-moving device can be controlled to move to the reference point or the adjacent range area of the reference point along the first moving path through navigation positioning based on the coordinate information of the reference point.

In one embodiment, after the self-mobile device is controlled to move to the reference point or the adjacent range area of the reference point by executing the first driving circuit instruction, a new moving path can be formed by taking the reference point as a starting point according to a specific work task.

In one embodiment, if the work task to be performed by the self-moving device needs to cover the entire workable area, for example, the self-moving lawn-treating device needs to perform a mowing process on the area where the lawn to be mowed is located. After moving from the mobile device to the reference point or the vicinity of the reference point, a round trip movement path capable of covering the entire workable area may be generated as a new movement path further based on the reference point. Reference is specifically made to fig. 12. Wherein the movement path may be a parallel straight path. In particular, it may be a parallel straight path that is horizontal (or vertical), as shown in the right-hand region of fig. 12; or a diagonal path along a boundary, as shown in the left-hand region of fig. 12. The starting point of the round trip path may be a reference point, and the round trip path may specifically include one or more sets of round trip path pairs. Wherein each set of round trip path pairs further specifically includes a previous path and a subsequent path. The previous path and the next path in the same round-trip path pair may be offset by a predetermined distance, and the adjacent two paths in the adjacent two round-trip path pairs, for example, the next path in the previous round-trip path pair in the adjacent two round-trip path pairs may be offset by a predetermined distance from the previous path in the next round-trip path pair. See fig. 12. The shuttle path thus obtained can cover the entire workable area more accurately.

Further, the corresponding driving circuit command may be generated based on the round trip movement path. By executing the driving circuit instruction, the self-moving equipment can be accurately controlled to move along the reciprocating movement path from the reference point, so that the movement capable of covering the whole workable area is completed.

In one embodiment, the driving circuit instruction is executed to control the self-moving device to move along the reciprocating movement path, so that specific work tasks can be performed according to the specific work tasks. For example, with a self-moving lawn processing apparatus, mowing may be performed while moving along a reciprocating path. Thus, after the round-trip movement of the covered lawn is completed, the mowing operation of the lawn is completed at the same time.

In one embodiment, the movement path may also be a circular path gradually converging from the border to the center of the area, or a circular path gradually diverging from the center of the area to the border, such as a circular path gradually diverging from the periphery of the house to the border of the lawn (not shown). In one embodiment, the movement path may also be a random path.

In particular, the user may select one or more of the above-described travel paths as the cutting pattern, or the cutting pattern may be automatically generated by the system according to the shape of the region or other features.

In one embodiment, the pattern may also be cut on the lawn by a path.

In one embodiment, the workable area that the self-mobile device is to be responsible for conducting the work job comprises a plurality of sub-areas. For example, two sub-regions are included. Accordingly, the determined reference points include a plurality of reference points, wherein at least one reference point is in each sub-region. For the above scenario, when the work job for one sub-area is completed from the mobile device, it is necessary to move from the current sub-area to the next sub-area to perform the work job for the next sub-area.

In this case, a second movement path may be generated, wherein the second movement path may indicate a movement path to move from the currently located sub-region to a reference point in the next sub-region. In particular, the second movement path may pass through a passable non-working area in the semantic map and/or a workable area.

Correspondingly, a second drive circuit instruction is generated. After receiving the second driving circuit instruction, the self-mobile device can execute the second driving circuit instruction based on the coordinate information of the current position (which can be determined by a navigator of the self-mobile device), so that the self-mobile device can be accurately controlled to move from the current subarea to the next subarea along the second moving path, through the workable area and/or through the non-working area to perform working operation for the next subarea.

In one embodiment, the boundary coordinates are updated during or after the edge is completed by visually taking a turn along the boundary from the mobile device to the reference point to make it more accurate, and then starting the subsequent work.

In one embodiment, moving from the mobile device to the reference point, first walking along the boundary by vision and cutting, walking along the boundary is accomplished by Robotic Operating System (ROS) PID control. Next, the kerf is identified, converted to a line, moved along the line and cut, and offset by about the width of the fuselage. The above process is repeated until the area is cut, and then the next area is entered.

The moving path is shown in fig. 16 (a) or 16 (b).

In the above embodiment, the motion control flow is shown with reference to fig. 17.

In one embodiment, if the work task to be taken care of from the mobile device is a field measurement to determine a more accurate workable area boundary, the opposing reference point may be a point in the target area between adjacent workable area boundary lines of the workable area. Upon moving from the mobile device to the reference point or to a vicinity of the reference point, a workable region boundary in the real environment may be visually identified. And then a corresponding moving path can be generated according to the boundary of the workable area in the identified real environment. For example, a third path of movement that moves along the boundary of the workable region. And generates a corresponding third drive circuit instruction based on the third movement path. Based on the coordinate information of the reference point, the above-described third driving circuit instruction may be executed to control the self-moving device to move along the boundary of the operable area in the real environment recognized by the visual recognition, and to control the self-moving device to move within the operable area range by continuously correcting the third movement path in accordance with the boundary of the operable area in the real environment recognized by the visual recognition during the movement, so as to avoid the self-moving device from moving to an area outside the boundary of the operable area.

Further, the boundary of the workable area in the semantic map can be corrected by using the boundary of the workable area obtained based on the real environment.

In one embodiment, the working area map for the target working area with higher precision can also be generated through the coordinate information recorded when the working area map moves along the boundary of the workable area in the real environment. And then, the corresponding work operation can be carried out by moving in the target work area according to the work area map.

In one embodiment, the work area map is obtained by fusion of vision, encoders and IMU.

In one embodiment, the camera captured image is perspective transformed so that the camera coordinates (Camera Coordinate) are converted to World coordinates (World coordinates) to obtain a map of the work area.

In an embodiment, for example, the self-moving lawn processing apparatus is further configured to be provided with a camera capable of rotating 360 degrees, which may be shown in fig. 3. In specific implementation, the self-moving lawn processing device may use the reference point as a starting point, first select a preset initial direction (for example, forward east) as a starting direction, rotate the camera at preset intervals (for example, at intervals of 5 degrees) according to a preset rotation direction (for example, in a counterclockwise direction), so as to obtain an environmental picture including surrounding environment, and record an included angle between the camera and the starting direction when the environmental picture is photographed. Further, the processor can process and identify the obtained picture of the environment, and find the boundary position between the lawn and the surrounding area in the environment. For example, an intermediate position between a green block and other colors in the picture can be found by color recognition as the above-described boundary position. According to a certain proportion, the distance from the demarcation position in each environmental picture to the reference point is calculated according to the environmental pictures. And the boundary outline surrounding the lawn can be determined according to the distance between each boundary position and the reference point, the included angle between the boundary positions and the initial direction when the environment picture is taken, and the reference point. Then, a walking path starting from the reference point can be determined from the boundary contour using the reference point as the origin, thereby obtaining a walking path capable of covering the boundary of the target lawn.

In this embodiment, in order to ensure that the determined lawn boundary has higher precision and smaller error, the self-moving lawn processing apparatus may first move to a corresponding position according to the determined walking path based on the contour boundary as a guide, and then detect and correct the moved position to ensure that the position is the boundary. After correction, the coordinate information of the current position can be obtained through the navigator. After coordinate information of the current position is acquired; further, the above-described operation may be repeated by moving to a next position along the lawn boundary according to the walking path. And after the coordinate information in the moving process is recorded one by the self-moving lawn processing equipment, determining the lawn boundary.

In this embodiment, the self-moving lawn processing apparatus moves to the determined boundary profile according to the above-described travel path when embodied. And then judging whether the position is a real lawn boundary or not by acquiring the ground image acquired at the position. If the position is determined to be a real lawn boundary, the coordinate information of the current position (for example, longitude and latitude coordinate information of the current position point and the like) can be recorded, and the coordinate information of the point on the lawn boundary can be further determined.

In this embodiment, when implemented, the true lawn boundary may also be determined in other manners according to the specific situation and processing requirements.

Specifically, for example, after determining the reference point according to the target image data, the processor may determine a contour line surrounding the target lawn from the target image data by means of image recognition or the like, and then generate a walking path using the reference point as a starting point according to the contour line and the reference point. The self-moving lawn processing equipment can move along the lawn boundary according to the walking path, and continuously adjust the position point where the correction is positioned in the moving process so as to ensure that the self-moving lawn processing equipment moves along the lawn boundary, so that the coordinate information of the point on the real lawn boundary can be obtained by recording the coordinate information in the moving process.

As another example, consider that typically the boundaries of a lawn are many made up of straight lines. In case the reference point comprises an intersection point between two adjacent border lines on the lawn border. The self-moving lawn processing equipment is also provided with an inertial navigation system in advance. After the self-moving lawn processing apparatus reaches the reference point by navigation, the self-moving lawn processing apparatus may first select a straight line along the boundary line 1 as the first path, which may be denoted as S1. And can then be moved along the first path S1 to the next intersection, i.e. the intersection between the boundary lines 2 and 3, by means of the inertial navigation system. The self-moving lawn processing apparatus may acquire the coordinate information of each point on the passed border line 2 through the navigator during the movement, thereby obtaining the coordinate information of each point on the lawn border line 1. After reaching the intersection between the boundary lines 2 and 3 again, the straight line along the boundary line 3 may be selected as the second path in the above-described manner, denoted S2. And then can move along the second path S2 by using the inertial navigation system and acquire the coordinate information of each point on the boundary line 3 through which the record passes. In the above manner, the self-moving lawn processing apparatus may automatically move through the respective boundary lines around the target lawn, and acquire coordinates of points on the respective boundary lines, thereby determining the lawn boundary of the target lawn.

In this embodiment, after determining the lawn boundary of the target lawn, the user may input a mowing instruction through the client device. After receiving the mowing instruction sent by the client device, the self-moving lawn processing device can respond to the mowing instruction and generate a mowing path which covers the target lawn according to the determined target boundary and does not exceed the lawn boundary. And further mowing can be moved in the target lawn according to the mowing path. In the mowing process, the self-moving lawn processing equipment can specifically acquire the coordinate information of the current position point of the self-moving lawn processing equipment through the navigator in real time, and compare the coordinate information of the current position point with the determined coordinate information of the point on the lawn boundary to judge whether the current position point of the self-moving lawn processing equipment is a point in an area outside the lawn boundary. If it is determined that the current position point is a point in an area other than the lawn boundary, it may be determined that the lawn robot has left the range area where the target lawn is located, and the self-moving lawn processing apparatus may stop mowing for safety. Further, the self-moving lawn processing apparatus may also search for and move to the reference point through the navigator to return to the range area where the target lawn is located. In one embodiment, after identifying the boundary of the workable area from the mobile device by visual recognition, a third drive circuit instruction may also be generated indicating to turn away from the boundary of the workable area.

After receiving the third driving circuit instruction, the self-moving device can execute the third driving circuit instruction to control the self-moving device to move along a third moving path, turn and move in a direction away from the boundary of the working area. Therefore, the mobile device can be prevented from moving to a non-working area beyond the boundary of the working area, and the mobile device is ensured to move to work in the working area.

In an embodiment, during implementation, the corresponding moving path can be generated according to a specific scene and processing requirements through the client device or a processor on the mobile device or through the cloud server in the above manner.

In one embodiment, the obstacle is also determined by visual recognition as it moves from the mobile device along the path of movement, at a location a preset distance in front of the current location. Wherein the above-mentioned obstacle may be a person, an animal, a rockery, a building, or the like. In the case where it is determined that an obstacle exists at a position a preset distance ahead of the current position, an adjustment path including a movement path bypassing the obstacle may be timely generated. And further can control the self-moving device to move along the adjustment path so as to avoid the obstacle and move smoothly. In one embodiment, obstacles are identified and avoided based on semantic segmentation.

In one embodiment, objects identified by the preset identification model are classified and labeled, and actions of the self-mobile device are set accordingly to achieve navigation. The classification may be identified in the semantic map, or may be visually identified during the movement of the mobile device.

One type of classification is used to identify a particular object, including a person, animal, garage or garage door, etc., to cause a particular action to be performed from a mobile device. For example, when the owner or pet is identified from the mobile device, the active area of the owner or pet is avoided from being cut; when a stranger or wild animal is identified from the mobile device, a signal is sent to expel. For another example, when a garage or garage door is identified from a mobile device, the garage door is controlled to automatically open to access the garage, and so on.

Another classification may be used to control the operation of the self-mobile device, including walking and/or cutting, examples of which are listed below.

/>

In one embodiment, when forming a specific moving path based on the reference point, a positioning signal may be specifically acquired, and a specific moving path is generated through fusion of the positioning signals.

In one embodiment, the positioning signal may be derived from inertial navigation, or odometry, or satellite navigation, among others. Of course, the sources of positioning signals listed above are only one illustrative example. In specific implementation, the positioning signal may also originate from other positioning devices according to specific application scenarios. The present specification is not limited to this.

According to the path forming method of the self-mobile device, through calling a preset identification model, electronic map data, which are obtained from an electronic map database and contain image data of a target working area associated with coordinate information, are subjected to identification processing, and a semantic map containing automatically identified workable areas and/or workable area boundaries is obtained; and the corresponding reference point can be marked in the semantic map or the electronic map data, and then the moving path of the self-moving equipment is generated based on the reference point. Therefore, the corresponding moving path can be automatically generated according to specific business tasks without the need of the user to guide the self-moving equipment to bypass along the target working area in advance, the operation of the user is simplified, the use experience of the user is improved, the generating efficiency of the moving path of the self-moving equipment is improved, and the technical problems of low generating efficiency of the moving path, complex operation of the user and poor use experience in the existing method are solved.

In one embodiment, the preset recognition model may be a process model that is trained in advance by Deep Learning (Deep Learning) or the like.

In implementation, a plurality of electronic map data including the target working area may be acquired as sample data. And marking the electronic map data, and marking the workable area and/or the range area where the boundary of the workable area is positioned in the image data of the electronic map data. Thereby obtaining the marked sample data. Further, a neural network or other type of model for performing image data processing may be established as an initial model. And then, the marked sample data is used as model training data, and the marked sample data is used for continuously learning and training the initial model, so that model parameters in the initial model are continuously adjusted and modified, and the adjusted model parameters currently used by the model are used for determining model parameters of a preset recognition model until the error rate of the model based on the adjusted model parameters is lower than a preset error rate threshold value when the electronic map data is recognized, so that the preset recognition model with higher accuracy is obtained.

The initial model may specifically include a convolutional neural network (CNN, convolutional Neural Network) model, and the like.

Of course, the above-listed ways of obtaining the preset recognition model are only illustrative. In the implementation, other learning training modes can be adopted to perform model training besides deep learning, so that a preset lawn identification model meeting the requirements is obtained. For example, the collected sample data may be learned by an unsupervised learning algorithm or a reinforcement learning algorithm without marking, so as to obtain a corresponding preset recognition model, etc.

In one embodiment, the workable region comprises in particular at least two divided sub-regions; correspondingly, marking at least two reference points on the semantic map, wherein the reference points respectively correspond to a sub-region. Wherein each sub-region comprises at least one reference point.

In this embodiment, the target working area is sometimes relatively complex, for example, a large workable area may include two or more separated sub-areas.

See fig. 13. The target working area where the self-mobile device needs to work may specifically include 5 different sub-areas, which are respectively: sub-area 1, sub-area 2, sub-area 3, sub-area 4 and sub-area 5. Among these, between sub-area 1 and sub-area 2, between sub-area 2 and sub-area 3, between sub-area 4 and sub-area 5, and between sub-area 2 and sub-area 4 is an aisle that the self-mobile device can freely pass through but does not allow operation, i.e. a passable non-operating area. Between the subareas 3 and 4 is a wall, i.e. an impenetrable non-working area. According to the instruction of the user, the self-mobile device can sequentially complete the business work aiming at each subarea.

Based on the method provided in this embodiment, reference points in the sub-region 1 may be marked on the semantic map first: reference point number 1, reference point in sub-region 2: reference point No. 2, reference point in sub-region 3: reference point No. 3, reference point in sub-region 4: reference point No. 4, reference point in sub-region 5: reference point No. 5, and reference points in the passable inactive area between sub-area 1 and sub-area 2: reference point No. 6, reference point in the passable inactive area between sub-area 2 and sub-area 3: reference point No. 7, reference point in the passable inactive area between sub-areas, 4 and sub-area 5: reference point No. 8, reference point in the passable inactive area between sub-area 2 and sub-area 4: reference point No. 9.

In this embodiment, in the implementation, the self-mobile device may first use the reference point No. 1 in the sub-area 1 as a starting point, and after completing the service work covering the sub-area 1, use the point where the position is located when the service work of the sub-area 1 is completed in the sub-area 1 as the starting point, and use the reference point No. 2 in the sub-area 2 as the ending point to generate the cross-area moving path. Further, the self-moving device may move from sub-region 1 to reference point No. 2 in sub-region 2 according to the above-described transregional movement path. And then taking the reference point No. 2 as a starting point to finish the business work covering the sub-area 2. And so on, the self-mobile device can sequentially complete the business work of each sub-region in the target work region.

In another embodiment, in the implementation, after completing the business work of the sub-area 1, the self-mobile device may first return to the reference point No. 1, and then move from the sub-area 1 to the sub-area 2 according to a moving path from the reference point No. 1 to the reference point No. 2 through the reference point No. 6, so as to perform the business work for the sub-area 2. After the business work of the subarea 2 is completed, the system returns to the No. 2 reference point, and then moves from the subarea 2 to the subarea 3 according to the moving path from the No. 2 reference point to the No. 3 reference point through the No. 7 reference point, so that the business work aiming at the subarea 3 is performed. After completing the business work of sub-area 3, it is an unvarying non-working area due to the interval between sub-areas 3 and 4. Therefore, the self-mobile device cannot directly move from the sub-area 2 to the sub-area 3, and can return to the reference point No. 3, and then returns from the sub-area 3 to the sub-area 2 according to a moving path from the reference point No. 3 to the reference point No. 2 through the reference point No. 7; and further, the mobile equipment moves from the sub-area 2 to the sub-area 4 according to a moving path from the reference point No. 2 to the reference point No. 4 through the reference point No. 9, so that the mobile equipment can smoothly enter and perform specific business work on the sub-area 4. After the business work of the sub-area 4 is completed, the self-mobile device can return to the reference point No. 4, and then moves from the sub-area 4 to the sub-area 5 according to the moving path from the reference point No. 4 to the reference point No. 5 through the reference point No. 8, so that the business work aiming at the sub-area 5 is performed. According to the mode, the self-mobile equipment can sequentially move to each subarea according to the user instruction and the numbering sequence of the subareas to finish business work for each subarea.

The slave mobile device needs to move to each sub-area in turn to perform work. After the work operation of each sub-area is completed, the work business of the workable business is finally completed.

In one embodiment, when the work of the current sub-area is completed, a moving path of the reference point moving to the next sub-area needs to be generated, so that the mobile device can be controlled to move from the current sub-area to the next sub-area along the moving path to perform the work of the next sub-area.

In one embodiment, the moving path is formed based on the reference point, and when implemented, the method may further include the following: generating a second driving circuit instruction, wherein the second driving circuit instruction comprises a step of moving to a reference point corresponding to another sub-area after indicating that the moving path of the corresponding sub-area is completed based on one of the reference points, so as to start the moving path of the other sub-area.

In one embodiment, the semantic map may specifically further include: the identified passable non-working area and/or passable non-working area boundary is associated with the coordinate information. The passable non-working area may specifically include an area in the non-working area that allows the self-mobile device to move through.

Correspondingly, after completing the moving path of one sub-area corresponding to one of the reference points, moving to the reference point corresponding to another sub-area, and when implementing, the method may include the following steps: and after completing the moving path of one corresponding sub-area based on one of the reference points, moving to the reference point corresponding to the other sub-area through the passable non-working area.

In one embodiment, the marking of the reference point on the semantic map or the electronic map data may include the following when implemented: and automatically determining points in the target working area as the reference points according to the semantic map.

In one embodiment, automatically determining points within the target work area as the reference points according to the semantic map includes: and automatically determining corner points of the workable area and/or inflection points of the border of the workable area as the reference points.

In one embodiment, marking the reference point on the semantic map or the electronic map data may include, when implemented: displaying the semantic map or the image data of the electronic map to a user; receiving a selection operation of a user for the semantic map or the image data of the electronic map; and marking a reference point on the semantic map or the electronic map data according to the selection operation.

In one embodiment, forming the moving path based on the reference point may include, when implemented: generating a first drive circuit instruction indicating a movement path to the reference point or a vicinity of the reference point; the self-mobile device executes the first driving circuit instruction based on coordinate information of a reference point through positioning.

In one embodiment, after executing the driving circuit instruction based on the coordinate information of the reference point, the method may specifically further include: and forming a new moving path by taking the reference point as a starting point.

In one embodiment, when the method is implemented, the method may further include: identifying a boundary of a workable area through visual identification; based on the identified boundary, a third drive circuit instruction is generated that instructs movement along the workable region boundary and/or steering away from the workable region boundary.

In one embodiment, when the method is implemented, the method may further include: identifying a boundary of a workable area through visual identification; operating the self-moving equipment to move along the boundary of the workable area, and recording coordinate information in the moving process through positioning; and generating a working area map of the target working area according to the recorded coordinate information.

In one embodiment, the moving path is formed based on the reference point, and when implemented, the moving path may include the following: and generating a reciprocating movement path based on the reference point, wherein the reciprocating movement path comprises a front path section and a rear path section, and the rear path section is offset by a preset distance relative to the front path section.

In one embodiment, forming the moving path based on the reference point may include, when implemented: determining whether an obstacle exists at a position which is positioned in front of the current position by a preset distance through visual identification; in case it is determined that there is an obstacle at a position a preset distance in front of the current position, an adjustment path for avoiding the obstacle is generated.

In one embodiment, forming the moving path based on the reference point may include, when implemented: the movement path is generated based on a fusion of positioning signals from inertial navigation, or odometry, or satellite navigation.

In one embodiment, the electronic map database may specifically include an online satellite map database. Of course, the above-listed electronic map databases are only one illustrative example. In the implementation, other electronic map databases may also be used according to specific application scenarios. The present specification is not limited to this.

In one embodiment, invoking a preset recognition model, and determining the semantic map based on the electronic map data may include: and carrying out convolution processing on the image data in the electronic map data through the trained neural network model so as to obtain the semantic map.

In one embodiment, the self-moving device may specifically include a self-moving lawn processing device, and correspondingly, the target work area may specifically include a target lawn to be mowed. The self-moving equipment can specifically further comprise a self-moving monitoring robot, and correspondingly, the target working area can specifically comprise a range area and the like of the self-moving monitoring robot responsible for monitoring and management. Of course, the above list of self-moving devices and target work areas is only one schematic illustration. In specific implementation, according to a specific application scenario, the self-mobile device may further include other types of devices, and correspondingly, the target working area may further include other types of areas. The present specification is not limited to this.

Fig. 18 (a) - (e) are schematic views of active area division according to an embodiment of the present disclosure. In this embodiment, the initial map is obtained by defining boundaries by *** map. As shown in fig. 18 (a), electronic map data, such as *** map data, is first obtained. Based on the electronic map data, deep learning is performed using a neural network model, and a semantic map is determined, as shown in fig. 18 (b). The possible working areas are identified by means of a semantic map. In the map shown in fig. 18 (b), the semantic division divides the tree and the shadow, and in this embodiment, the tree and the shadow are regarded as the workable area, resulting in the map shown in fig. 18 (c). As shown in fig. 18 (d), the boundary is extracted, and as described above, the boundary may be defined manually where the boundary is unclear. Finally, as shown in fig. 18 (e), an initial map in the world coordinate system is obtained for navigation from the mobile device.

Fig. 19 is a schematic diagram of a boundary correction process according to an embodiment of the present disclosure. After the initial boundary is obtained, the self-mobile device walks around the boundary by vision and visually recognizes the boundary. In the walking process, position information is obtained through initial position and inertial navigation, and boundaries are established. The initial boundary is compared with the boundary established by walking to correct the boundary. For example, the boundary is corrected by corner coincidence. In one embodiment, boundaries are visually identified and corrected during steering from the project where the mobile device traversed the work area. As shown in fig. 19, when the actual boundary (shown by a solid line in the figure) is visually recognized from the time the mobile device walks to the boundary before reaching the initial boundary (shown by a broken line in the figure), a new boundary position is determined. And steering from the mobile equipment, recording the actual boundary position, and correcting the initial map by using the actual boundary position to obtain a corrected map.

As can be seen from the above, in the path forming method of the self-mobile device provided in the embodiments of the present disclosure, by calling a preset recognition model, recognition processing is performed on electronic map data including image data of a target working area associated with coordinate information, which is acquired from an electronic map database, so as to obtain a semantic map including a working area and/or a boundary of the working area that is automatically recognized; and the corresponding reference point can be marked in the semantic map or the electronic map data, and then the moving path of the self-moving equipment is generated based on the reference point. Therefore, the corresponding moving path can be automatically generated according to specific business tasks without the need of the user to guide the self-moving equipment to bypass along the target working area in advance, the operation of the user is simplified, the use experience of the user is improved, the generating efficiency of the moving path of the self-moving equipment is improved, and the technical problems of low generating efficiency of the moving path, complex operation of the user and poor use experience in the existing method are solved. And when the generated moving path moves in the workable area, whether an obstacle exists at a preset distance in front of the mobile equipment is detected in real time through visual recognition, the executed moving path is adjusted in time under the condition that the obstacle is detected, and then the moving path moves according to the adjusted moving path, so that the obstacle can be intelligently found and avoided, and the safe movement of the mobile equipment is ensured. And the real workable area boundary is identified based on visual identification, and then the moving path is adjusted according to the identified real workable area boundary, so that the self-moving equipment can move to work within the workable area boundary more accurately based on the adjusted moving path, and the self-moving equipment is prevented from moving to the outside of the workable boundary.

The embodiment of the present disclosure further provides a self-moving lawn processing apparatus, specifically referring to fig. 3, where the self-moving lawn processing apparatus may at least include: processor, signal transceiver, navigator etc. The signal transceiver may be specifically configured to acquire image data including the target lawn and associated electronic map data including coordinate information. The processor can be specifically used for calling a preset identification model and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; marking at least one reference point on the semantic map or the electronic map data; based on the reference point, a movement path is formed.

In this embodiment, the self-moving lawn processing apparatus further includes a moving assembly and a cutting assembly. Wherein the moving assembly may be used to move a self-moving lawn-treating apparatus. The cutting assembly described above may be used to perform specific mowing operations.

Of course, the above-listed constituent structures are only illustrative. In the implementation process, according to specific application scenes and working requirements, other component structures besides the listed component structures can be introduced to form the self-moving lawn processing equipment. For example, some self-moving lawn processing devices may further include: attitude detectors, laser rangefinders, direction sensors, cameras, and the like.

In this embodiment, the navigator may specifically include a GPS antenna or the like, so that coordinate information of a position point is received by the navigator.

In one embodiment, the processor may be further configured to control the self-moving lawn processing apparatus to move along a movement path within the target lawn boundary and perform a mowing operation.

Referring to fig. 14, on a software level, the embodiment of the present disclosure further provides a device for determining a lawn boundary, where the device may specifically include the following structural modules.

The acquiring module 1401 may be specifically configured to acquire electronic map data from an electronic map database; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information;

the recognition module 1402 may be specifically configured to invoke a preset recognition model, and determine a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information;

a marking module 1403, specifically configured to mark at least one reference point on the semantic map or the electronic map data;

The movement path forming module 1404 may be specifically configured to form a movement path based on the reference point.

In one embodiment, the target image data may specifically include satellite map data including a target lawn, and the like.

It should be noted that, the units, devices, or modules described in the above embodiments may be implemented by a computer chip or entity, or may be implemented by a product having a certain function. For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, when the present description is implemented, the functions of each module may be implemented in the same piece or pieces of software and/or hardware, or a module that implements the same function may be implemented by a plurality of sub-modules or a combination of sub-units, or the like. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.

The path forming device of the self-mobile device provided by the embodiment of the specification invokes a preset recognition model through the recognition module, and performs recognition processing on the image data containing the target working area and the electronic map data of the related coordinate information, which are acquired from the electronic map database by the acquisition module, so as to obtain a semantic map containing recognized workable areas and/or workable area boundaries; and the corresponding reference point can be marked in the semantic map through the marking module, and then the moving path of the self-moving equipment is generated based on the reference point through the moving path forming module. Therefore, the corresponding moving path can be automatically generated according to specific business tasks without the need of guiding the self-moving equipment along the target working area in advance by a user, the operation of the user is simplified, the use experience of the user is improved, the generating efficiency of the moving path of the self-moving equipment is improved, and the technical problems of low generating efficiency of the moving path, complex operation of the user and poor use experience in the existing method are solved.

The embodiment of the specification also provides a client device, which comprises a processor and a memory for storing instructions executable by the processor, wherein the processor can execute the following steps according to the instructions when being concretely implemented: acquiring electronic map data from an electronic map database; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information; invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; marking at least one reference point on the semantic map or the electronic map data; based on the reference point, a movement path is formed. And the self-mobile equipment moves along the moving path in the target working area by navigation according to the moving path to perform corresponding working operation.

In this embodiment, the client device may specifically include a front-end device applied to a user side and capable of implementing functions such as data input and data transmission. Specifically, the client device may be, for example, a desktop computer, a tablet computer, a notebook computer, a smart phone, a remote controller, etc. Alternatively, the client device may be a software application capable of running in the electronic device described above. For example, it may be an APP running on a mobile phone, etc.

In order to more accurately complete the above-mentioned instructions, referring to fig. 15, the embodiment of the present disclosure further provides a path forming apparatus for a self-mobile device, where the client device includes a memory 1501 and a processor 1502, and the above-mentioned structures are connected by an internal cable, so that each structure may perform specific data interaction.

The memory 1501 may be used to store computer readable instructions.

The processor 1502 may be configured to obtain electronic map data from an electronic map database when processing the computer readable instructions; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information; invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; marking at least one reference point on the semantic map or the electronic map data; based on the reference point, a movement path is formed.

In this embodiment, the processor 1501 may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor, and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable logic controller, and an embedded microcontroller, among others. The description is not intended to be limiting.

In this embodiment, the memory 1502 may include multiple levels, and in a digital system, may be a memory as long as binary data can be stored; in integrated circuits, circuits with memory functions, such as RAM, FIFO, etc., that are not in physical form are also called memories; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card, and the like.

The embodiment of the specification also provides an automatic working system, which comprises: the self-mobile device and the application program installed on the user side; the application program comprises: acquisition procedure: acquiring electronic map data from an electronic map database; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information; identification procedure: invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; marking: marking at least one reference point on the semantic map or the electronic map data; the self-mobile device comprises a communication module which is communicated with a client, and coordinate information of the reference point is obtained from the client after the application program is executed; the self-mobile device further comprises a positioning module, and a moving path is formed based on the coordinate information of the reference point by monitoring the output of the positioning module.

The embodiment of the specification also provides an automatic working system, which comprises: a self-moving device; the self-moving device includes a housing; the mobile module is arranged on the shell; the control module is used for controlling the moving module to drive the self-moving equipment to move; the self-mobile device further comprises a positioning module for outputting coordinate information of the self-mobile device; the automatic working system further comprises a storage unit for storing: a semantic map generating program comprising: acquiring electronic map data from an electronic map database; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information; invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; a marking program comprising: marking at least one reference point on the semantic map or the electronic map data; the automatic working system executes the semantic map generating program and the marking program so as to determine the coordinate information of the reference point; the control module controls the moving module to drive the self-moving device to move so as to form a moving path based on the coordinate information of the reference point by monitoring the output of the positioning module.

The embodiments of the present specification also provide a computer-readable storage medium storing computer program instructions that when executed implement a path forming method based on the self-mobile device described above: acquiring electronic map data from an electronic map database; wherein the electronic map data includes: image data including a target working area, the image data being associated with coordinate information; invoking a preset identification model, and determining a semantic map based on the electronic map data; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information; marking at least one reference point on the semantic map or the electronic map data; based on the reference point, a movement path is formed.

The embodiments of the present specification also provide a solution for returning to a docking station using machine vision. Specifically, as shown in fig. 20 and 21, for example, the self-moving lawn processing apparatus is provided with a camera and is disposed at a charging end in a front or rear direction of movement of the apparatus. An image tag, specifically, for example, a two-dimensional code pattern such as a bar code, a two-dimensional code, or the like is provided on a charging station of the self-moving lawn processing apparatus. The self-moving lawn processing equipment captures continuous image streams of bar codes, two-dimensional codes or other image labels based on the camera to realize alignment in 6 degrees of freedom (6 DOF), so that the aim of accurate butt joint is fulfilled. Specifically, for example, the self-moving lawn processing device continuously determines the relationship of the self-moving lawn processing device relative to the charging station in 6 degrees of freedom according to the shape and the size of the feature points of the images of the bar codes or the two-dimensional codes, and continuously adjusts the coordinates and the azimuth of the self-moving lawn processing device according to the determination result so as to approach the charging station and realize accurate butt joint of the charging end of the device and the charging end of the charging station.

In the present embodiment, the storage medium includes, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.

In this embodiment, the functions and effects of the program instructions stored in the computer storage medium may be explained in comparison with other embodiments, and are not described herein.

Although the present description provides method operational steps as described in the examples or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When implemented by an apparatus or client product in practice, the methods illustrated in the embodiments or figures may be performed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed data processing environment). The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, it is not excluded that additional identical or equivalent elements may be present in a process, method, article, or apparatus that comprises a described element. The terms first, second, etc. are used to denote a name, but not any particular order.

Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller can be regarded as a hardware component, and means for implementing various functions included therein can also be regarded as a structure within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.

The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

From the above description of embodiments, it will be apparent to those skilled in the art that the present description may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solutions of the present specification may be embodied essentially in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and include several instructions to cause a computer device (which may be a personal computer, a mobile terminal, a server, or a network device, etc.) to perform the methods described in the various embodiments or portions of the embodiments of the present specification.

Various embodiments in this specification are described in a progressive manner, and identical or similar parts are all provided for each embodiment, each embodiment focusing on differences from other embodiments. The specification is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

Although the present description has been described by way of example, it will be appreciated by those skilled in the art that there are many variations and modifications to the description without departing from the spirit of the description, and it is intended that the appended claims encompass such variations and modifications as do not depart from the spirit of the description.

Claims

1. A control method of a self-mobile device, comprising:

acquiring coordinate information, wherein the coordinate information is GPS coordinates of a client, GPS coordinates of self-mobile equipment, an address input by a user at the client or GPS coordinates input by the user at the client;

acquiring electronic map data; wherein the electronic map data includes: image data including a target working area, the image data being associated with the coordinate information;

invoking a preset identification model, and determining a semantic map based on the electronic map data, wherein the electronic map data is used as model input, and the semantic map is used as model output; the semantic map includes identified workable areas and/or workable area boundaries associated with coordinate information.

2. The method of claim 1, wherein the electronic map data is map data of a satellite map database, aerial photographs of a work area, or scanned imaging data of a work area.

3. The method of claim 1, wherein before invoking the preset recognition model, the method further comprises:

acquiring a cutting instruction or a delineating instruction input by a user;

and processing the electronic map data according to the clipping instruction or the delineating instruction, and determining the electronic map data only comprising the target working area.

4. The method of claim 1, wherein the semantic map comprises a non-working area comprising a non-passable non-working area and a passable non-working area.

5. The method according to claim 1, wherein the method further comprises:

identifying a boundary of a workable area through visual identification;

controlling the self-mobile equipment to move along the boundary of the workable area, and recording coordinate information in the moving process or coordinate information of points on the boundary through positioning;

and correcting the semantic map according to the recorded coordinate information to generate a working area map of the target working area.

6. The method according to claim 1, wherein the method further comprises:

marking at least one reference point on the semantic map or the electronic map data;

Generating a first movement path characterizing a path moved by a current location of the self-moving device to the reference point or a vicinity of the reference point;

and controlling the self-moving device to move to a reference point or a near range area of the reference point along the first moving path.

7. The method of claim 1, wherein the workable region comprises a plurality of sub-regions;

the method further comprises the steps of:

marking reference points on the semantic map or the electronic map data, wherein at least one reference point exists in each sub-area;

generating a second movement path characterizing a path moving from one sub-area to a reference point in another sub-area;

and controlling the self-moving device to move to the next sub-area along the second moving path from the current sub-area, through the operable area and/or through the non-operable area.

8. The method of claim 1, wherein marking a reference point on the semantic map or the electronic map data comprises:

determining recommended points from the semantic map or the electronic map data;

Displaying the recommended points;

and determining the recommended point selected by the user as the reference point.

9. The method according to claim 1, wherein the method further comprises:

acquiring a manual boundary selection instruction input by a user;

and processing the semantic map according to the manual boundary selection instruction.

10. The method of claim 9, wherein the manually selecting a boundary instruction comprises: demarcating boundary instructions and/or boundary compensation offset instructions.