CN114952862A

CN114952862A - Robot cabin door closing method and device

Info

Publication number: CN114952862A
Application number: CN202210758054.9A
Authority: CN
Inventors: 张瑞琪; 曾祥永; 支涛
Original assignee: Henan Yunji Intelligent Technology Co Ltd
Current assignee: Henan Yunji Intelligent Technology Co Ltd
Priority date: 2022-06-29
Filing date: 2022-06-29
Publication date: 2022-08-30

Abstract

The disclosure relates to the technical field of robots, and provides a method and a device for closing a cabin door of a robot. The method comprises the following steps: when the cabin door of the object placing cabin of the robot is in an open state, repeatedly acquiring a newly shot image in the cabin and inputting the image in the cabin into a preset cabin interior emptying model to obtain an emptying result until the number of times that the object placing cabin is empty as represented by the continuous emptying result reaches a preset threshold value, wherein the image in the cabin is obtained after shooting the interior of the object placing cabin by a camera in the cabin of the object placing cabin; and generating a cabin door closing instruction to control the cabin door of the object placing cabin to be closed. By adopting the technical means, the problem that the cabin door of the storage cabin cannot be closed in time after goods in the storage cabin of the robot are taken out in the prior art can be solved.

Description

Robot cabin door closing method and device

Technical Field

The disclosure relates to the technical field of robots, in particular to a method and a device for closing a cabin door of a robot.

Background

In the related art, the delivery robot notifies the customer of the removal of the goods in the storage compartment of the robot after the delivery robot reaches the destination. After the client takes out the goods in the storage cabin, the user needs to manually click the button for closing the cabin door, and the cabin door of the storage cabin is closed.

If the customer does not click the hatch door closing button in time, the robot needs to wait for a long time before automatically closing the hatch door of the storage compartment, so that the delivery efficiency of the robot is reduced, and the delivery cost of the robot is increased.

How to close the cabin door of the object storage cabin of the robot in time after goods are taken out is a technical problem which needs to be solved urgently at present.

Disclosure of Invention

In view of this, the embodiments of the present disclosure provide a method and an apparatus for closing a cabin door of a robot, an electronic device, and a computer-readable storage medium, so as to solve the problem that a cabin door of a storage cabin of a robot cannot be closed in time after goods in the storage cabin are taken out in the prior art.

In a first aspect of the disclosed embodiments, there is provided a robot hatch door closing method, including: when the cabin door of the object placing cabin of the robot is in an open state, repeatedly acquiring a newly shot image in the cabin and inputting the image in the cabin into a preset cabin interior emptying model to obtain an emptying result until the number of times that the object placing cabin is empty as represented by the continuous emptying result reaches a preset threshold value, wherein the image in the cabin is obtained after shooting the interior of the object placing cabin by a camera in the cabin of the object placing cabin; and generating a cabin door closing instruction to control the cabin door of the storage cabin to be closed.

In a second aspect of the embodiments of the present disclosure, there is provided a robot hatch door closing device including: the judging module is used for repeatedly acquiring a newly shot intra-cabin image and inputting the intra-cabin image into a preset intra-cabin emptying model when a cabin door of a storage cabin of the robot is in an open state to obtain an emptying result until the number of times that the storage cabin is empty as represented by the emptying result reaches a preset threshold value, wherein the intra-cabin image is obtained after shooting the inside of the storage cabin by a camera in the cabin of the storage cabin; and the generating module is used for generating a cabin door closing instruction so as to control the cabin door of the storage cabin to be closed.

In a third aspect of the embodiments of the present disclosure, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.

In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored, which when executed by a processor implements the steps of the above-mentioned method.

Compared with the prior art, the embodiment of the disclosure has the following beneficial effects: the images in the cabin are obtained by shooting the inside of the object containing cabin and are input into the cabin air judgment model, the object containing cabin can be determined to be an empty cabin and the cabin door can be closed when the air judgment result represents that the object containing cabin is an empty cabin and the air judgment result represents that the object containing cabin continuously appears for threshold times, so that the empty cabin judgment can be rapidly and accurately carried out according to the image recognition technology, the cabin door can be closed in time, and the working efficiency of robots is improved.

Drawings

To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.

FIG. 1 is a scenario diagram of an application scenario of an embodiment of the present disclosure;

fig. 2 is a schematic flow chart of a method for closing a door of a robot according to an embodiment of the present disclosure;

FIG. 3 is a schematic flow chart of a training method for an in-cabin emptying judgment model according to an embodiment of the present disclosure;

fig. 4 is a schematic flow chart of another robot hatch door closing method provided in the embodiments of the present disclosure;

fig. 5 is a schematic structural diagram of a robotic hatch door closing device provided in an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.

A robot hatch door closing method and apparatus according to embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 is a scene schematic diagram of an application scenario of an embodiment of the present disclosure. The application scenario may include

terminal devices

101, 102, and 103, server 104, and network 105.

The

terminal apparatuses

101, 102, and 103 may be hardware or software. When

terminal devices

101, 102, and 103 are hardware, they may be various electronic devices having a display screen and supporting communication with server 104, including but not limited to smart phones, robots, laptop portable computers, desktop computers, and the like (e.g., 102 may be a robot); when the

terminal apparatuses

101, 102, and 103 are software, they can be installed in the electronic apparatus as above. The

terminal devices

101, 102, and 103 may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not limited by the embodiments of the present disclosure. Further, various applications, such as data processing applications, instant messaging tools, social platform software, search-type applications, shopping-type applications, etc., may be installed on the

terminal devices

101, 102, and 103.

The server 104 may be a server providing various services, for example, a backend server receiving a request sent by a terminal device establishing a communication connection with the server, and the backend server may receive and analyze the request sent by the terminal device and generate a processing result. The server 104 may be a server, may also be a server cluster composed of a plurality of servers, or may also be a cloud computing service center, which is not limited in this disclosure.

The server 104 may be hardware or software. When the server 104 is hardware, it may be various electronic devices that provide various services to the

terminal devices

101, 102, and 103. When the server 104 is software, it may be multiple software or software modules that provide various services for the

terminal devices

101, 102, and 103, or may be a single software or software module that provides various services for the

terminal devices

101, 102, and 103, which is not limited by the embodiment of the present disclosure.

The network 105 may be a wired network connected by a coaxial cable, a twisted pair and an optical fiber, or may be a wireless network that can interconnect various Communication devices without wiring, for example, Bluetooth (Bluetooth), Near Field Communication (NFC), Infrared (Infrared), and the like, which is not limited in the embodiment of the present disclosure.

The target user can establish a communication connection with the server 104 via the network 105 through the

terminal devices

101, 102, and 103 to receive or transmit information or the like. It should be noted that specific types, numbers, and combinations of the

terminal devices

101, 102, and 103, the server 104, and the network 105 may be adjusted according to actual needs of an application scenario, which is not limited in this disclosure.

Fig. 2 is a schematic flowchart of a robot hatch door closing method according to an embodiment of the present disclosure. The robot hatch closing method of fig. 2 may be performed by the terminal device or the server of fig. 1. As shown in fig. 2, the robot hatch closing method includes:

step S201, when a cabin door of a storage cabin of the robot is in an open state, repeatedly acquiring a newly shot image in the cabin and inputting the image in the cabin into a preset cabin emptying model to obtain an emptying result until the number of times that the storage cabin is empty as represented by the emptying result continuously reaches a preset threshold value, wherein the image in the cabin is obtained after shooting the inside of the storage cabin by a camera in the cabin of the storage cabin.

Specifically, in the repeating steps, each repeating process includes the following steps: and acquiring a newly shot image in the cabin, and inputting the image in the cabin into an in-cabin air judgment model to obtain an air judgment result. Each repeated process can obtain an empty judgment result, and the empty judgment result can represent that the storage compartment is an empty compartment or the storage compartment is not an empty compartment. And when the preset threshold value is N, if the N times of air judgment results continuously indicate that the object containing cabin is an empty cabin, determining that the object containing cabin is the empty cabin, and ending the current repeated process. Wherein N is a natural number and N is greater than or equal to 2.

Step S202, a cabin door closing instruction is generated to control the cabin door of the storage cabin to close.

Specifically, after step S201 is completed, that is, after the repeating process is finished, step S202 is executed, a hatch door closing instruction is generated and sent to an actuator of the robot for closing the hatch door, so as to control the actuator to close the hatch door of the storage compartment.

According to the technical scheme of the embodiment of the disclosure, the camera is installed in the object placing cabin of the robot, images in the cabin collected by the camera in real time are identified to obtain an air judgment result, whether the object placing cabin is an empty cabin is judged according to the relation between the times of representing the object placing cabin as the empty cabin and the threshold value of the object placing cabin according to the air judgment result, and the cabin door of the object placing cabin is controlled to be closed when the object placing cabin is determined to be the empty cabin. This technical scheme adopts computer vision algorithm to come to carry out empty cabin to putting the thing cabin and distinguishes to can automize and detect and put whether the thing cabin is empty cabin, and close the hatch door when putting the thing cabin for empty cabin, realize putting in time closing of thing cabin hatch door, improve robot's work efficiency.

In the embodiment of the disclosure, the in-cabin emptying model can be obtained by training the deep learning model by adopting the sample data set. The deep learning model may be, but is not limited to, a neural network model. The sample dataset in embodiments of the present disclosure may be a sample intra-cabin image dataset.

As shown in fig. 3, a training method for an cabin emptying model provided in an embodiment of the present disclosure includes:

step S301, obtaining an image data set in the sample cabin, wherein the image data set in the sample cabin comprises images in the sample cabin and corresponding empty cabin identification tags, and the empty cabin identification tags are training tags for marking whether storage cabins corresponding to the images in the sample cabin are empty cabins or not.

Specifically, the cabin interior judgment model can be understood as an empty cabin identification model, and an empty cabin identification task of the empty cabin identification model is a process of classifying and identifying images by taking whether the images are empty cabins or not as an identification purpose. When marking whether the storage cabin corresponding to the images in the sample cabin is an empty cabin, the images in the sample cabin corresponding to the storage cabin in the non-empty cabin state and the empty cabin state can be respectively marked in a classified manner. In the model application stage, the cabin interior judgment model can judge whether the object containing cabin in the current cabin interior image is an empty cabin or not by classifying and identifying the current cabin interior image.

And S302, training the deep learning model by using the images in the sample cabin and the corresponding empty cabin identification labels thereof to obtain a trained cabin emptying judgment model.

In the embodiment of the present disclosure, steps S201 to S202 describe an application process of the cabin emptying model, and steps S301 to S302 describe a training process of the cabin emptying model. The in-cabin air judgment model is obtained by training the deep learning model by adopting the image in the sample in-cabin, and the in-cabin image can be subjected to air cabin identification based on a computer vision technology, so that the automation degree and the accuracy of air cabin judgment are improved.

In the training process of the in-cabin air judgment model, in order to facilitate the distinguishing of the images in the sample cabin of the storage cabin in the empty cabin state and the non-empty cabin state, the spatial feature distinguishing degree of the images in the sample cabin can be enhanced through image enhancement operation. Specifically, in order to enhance the spatial feature discrimination of the images in the sample chamber, before step S301, preprocessing operations such as image enhancement may be performed on the images in the sample chamber.

In the embodiment of the present disclosure, step S201 is performed on the premise that the compartment door of the storage compartment is determined to be in the open state. Two ways of determining that the cabin door of the storage cabin is in the open state can be provided, one way is to judge whether the cabin door of the storage cabin is in the open state according to the working state information of the robot, and if the working state of the robot is that the robot reaches a delivery place and opens the cabin door to wait for a customer to take goods, the cabin door of the storage cabin can be determined to be in the open state. Specifically, the working state information of the robot can be acquired, and whether the cabin door of the storage cabin is in an open state or not is judged according to the working state information.

And the other mode is that the cabin door of the storage cabin is determined to be in an opening state according to the cabin door opening instruction. Specifically, the robot generates a cabin door opening instruction when the guest takes a thing and opens the cabin for the guest. The cabin door opening instruction is obtained, and the cabin door of the storage cabin can be determined to be in an opening state according to the cabin door opening instruction.

In the embodiment of the disclosure, images in the cabin can be periodically acquired, and the images acquired in the cabin each time are input into the cabin emptying model for processing to obtain the emptying result. For example, the period for acquiring the images in the cabin may be 0.2 seconds, and is not limited thereto. Specifically, the camera may be periodically controlled to shoot images in the cabin according to a preset time period, and in response to receiving an image in the cabin sent by the camera, the received image in the cabin is input into the cabin air judgment model to obtain an air judgment result. By controlling the camera to shoot images in the cabin every 0.2s and sending the images to the terminal equipment or the server executing the cabin door closing method of the robot in the embodiment of the disclosure, the terminal equipment or the server can perform cabin interior judgment operation every 0.2s to obtain a judgment result. And integrating a plurality of continuous empty judgment results for representing the empty compartment to confirm that the storage compartment is the empty compartment.

The images of the scene in the cabin are continuously acquired for multiple times, and the air judgment result obtained by judging the air in the cabin is repeatedly confirmed for the images acquired every time, so that the object storage cabin can be confirmed to be an empty cabin only when the air judgment result is continuously empty for multiple times, the accuracy of empty cabin judgment is improved, and the judgment error of the empty cabin and the operation error of closing the cabin door are reduced.

In the multiple repeated steps in step S201, the number of cases where the empty result is the empty cabin continuously occurs may be counted. If the empty result is not the empty cabin in the counting process, the counting value is cleared, and the repeating step and the recounting in the step S201 are repeatedly executed until the counting value reaches the set threshold value. Specifically, the number of times that the object storage compartment is empty can be counted, and if the object storage compartment is not empty, the count value is cleared.

As shown in fig. 4, a robot hatch door closing method in an embodiment of the present disclosure includes the following steps.

Step S401, a camera shoots an internal image of the storage compartment.

Specifically, after the operation of capturing the image in the storage compartment by the camera is completed, the operation may be triggered to be performed again by the completion of step S404 and step S406. For example, if the object does not exist in the storage compartment in each time of the judgment result, and the judgment result indicating that the object does not exist in the storage compartment continuously appears for 5 times, the step S407 can be skipped, and the operation of shooting the image in the storage compartment by the camera only needs to be executed for 5 times, so that the workload of the camera is smaller compared with the operation of periodically shooting the image in the storage compartment.

And S402, inputting the internal image of the storage compartment into an in-compartment emptying model to obtain an emptying result.

And S403, judging whether an object exists in the storage compartment according to the air judgment result. If yes, step S404 and step S401 are executed, and if no, step S405 is executed.

In step S404, the counter is cleared.

In step S405, the counter is incremented by 1.

Specifically, if the object placing cabin is not empty once, the counter is cleared, step S401 is executed again, the image of the interior of the object placing cabin is shot and input into the cabin empty judgment model, and when the empty judgment result represents that the object placing cabin is empty, the counting is repeated.

In step S406, it is determined whether the count value of the counter is greater than the threshold. If yes, go to step S407, otherwise go to step S401.

Specifically, the loop process of steps S401 to S406 ends when the count value of the occurrence counter is greater than the threshold value in step S406. When the count value of the counter is greater than the threshold value, the object placing cabin can be confirmed to be an empty cabin, and the cabin door can be further closed.

In step S407, a hatch closing instruction is generated.

All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described in detail herein.

According to the robot cabin door closing method, the images in the cabin are obtained by shooting the interior of the object containing cabin and are input into the cabin emptying judgment model, the object containing cabin can be determined to be empty and the cabin door can be closed when the emptying result represents that the object containing cabin is empty and the emptying result represents that the empty cabin continuously appears for the threshold times, so that the empty cabin judgment can be rapidly and accurately carried out according to the image recognition technology, the cabin door can be closed in time, and the working efficiency of a robot is improved.

The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. The robot hatch closing device described below and the robot hatch closing method described above may be referred to with respect to each other. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.

Fig. 5 is a schematic view of a robotic hatch door closing device provided in an embodiment of the present disclosure. As shown in fig. 5, the robot hatch closing device includes:

the determining module 501 may be configured to repeatedly obtain a newly-photographed intra-cabin image and input the intra-cabin image into a preset intra-cabin emptying model when a cabin door of a storage cabin of the robot is in an open state, to obtain an emptying result until the number of times that the storage cabin is empty, which is represented by the emptying result, reaches a preset threshold value, where the intra-cabin image is obtained by photographing the interior of the storage cabin by a camera in the cabin of the storage cabin.

Specifically, in the repeating steps, each repeating process comprises the following steps: and acquiring a newly shot image in the cabin, and inputting the image in the cabin into an in-cabin air judgment model to obtain an air judgment result. Each repeated process can obtain an empty judgment result, and the empty judgment result can represent that the storage compartment is an empty compartment or the storage compartment is not an empty compartment. And when the preset threshold value is N, if the N times of air judgment results continuously indicate that the object containing cabin is an empty cabin, determining that the object containing cabin is the empty cabin, and ending the current repeated process. Wherein N is a natural number and N is greater than or equal to 2.

The generating module 502 may be configured to generate a hatch door closing instruction to control the hatch door of the storage compartment to close.

Specifically, after the judging module completes the repeating process, the generating module generates a cabin door closing instruction and sends the cabin door closing instruction to an executing mechanism of the robot, wherein the executing mechanism is used for closing the cabin door, so that the executing mechanism is controlled to close the cabin door of the storage cabin.

In the embodiment of the disclosure, the robot cabin door closing device may further include a training module, which may be configured to acquire a sample cabin interior image data set, where the sample cabin interior image data set includes a sample cabin interior image and an empty cabin identification tag corresponding to the sample cabin interior image, and train the deep learning model with the sample cabin interior image and the empty cabin identification tag corresponding to the sample cabin interior image, so as to obtain a trained cabin interior judgment model. The empty cabin identification label is a training label for marking whether the object containing cabin corresponding to the image in the sample cabin is the empty cabin.

The in-cabin air judgment model is obtained by training the deep learning model by adopting the image in the sample in-cabin, and the in-cabin image can be subjected to air cabin identification based on a computer vision technology, so that the automation degree and the accuracy of air cabin judgment are improved.

In an embodiment of the present disclosure, the robotic hatch door closing device may further comprise a pre-processing module, which may be used for image enhancement of images within the sample chamber. In the training process of the in-cabin air judgment model, in order to facilitate the distinguishing of the images in the sample cabin of the storage cabin in the empty cabin state and the non-empty cabin state, the spatial feature distinguishing degree of the images in the sample cabin can be enhanced through image enhancement operation.

In the embodiment of the present disclosure, the determining module may be further configured to periodically control the camera to shoot the images in the cabin according to a preset time period, and in response to receiving the images in the cabin sent by the camera, input the received images in the cabin into the cabin air judgment model to obtain the air judgment result. The period for acquiring the images in the cabin may be 0.2 seconds, and is not limited thereto. Specifically, the camera may be periodically controlled to shoot images in the cabin according to a preset time period, and in response to receiving an image in the cabin sent by the camera, the received image in the cabin is input into the cabin air judgment model to obtain an air judgment result. And integrating a plurality of continuous empty judgment results for representing the empty compartment to confirm that the storage compartment is the empty compartment.

In the embodiment of the present disclosure, the robot cabin door closing device may further include an opening state obtaining module, which may be configured to obtain working state information of the robot, and determine whether the cabin door of the storage cabin is in an opening state according to the working state information.

In the embodiment of the disclosure, the opening state obtaining module may be further configured to obtain a hatch opening instruction, and determine that the hatch of the storage compartment is in an opening state according to the hatch opening instruction.

In this disclosure, the determining module may be further configured to count the number of times that the object storage compartment is empty when the empty result indicates that the object storage compartment is continuously present, and clear the count value if the empty result indicates that the object storage compartment is not empty during the counting process.

As the respective functional modules of the robot hatch closing device of the exemplary embodiment of the present disclosure correspond to the steps of the exemplary embodiment of the robot hatch closing method described above, for details not disclosed in the embodiments of the device of the present disclosure, please refer to the embodiments of the robot hatch closing method described above of the present disclosure.

According to the robot cabin door closing device disclosed by the embodiment of the disclosure, the images in the cabin are obtained by shooting the inside of the object containing cabin and are input into the in-cabin air judgment model, the object containing cabin can be determined to be an empty cabin and the cabin door can be closed when the air judgment result represents that the object containing cabin is the empty cabin and the air judgment result represents that the empty cabin continuously appears for the threshold times, so that the empty cabin is judged quickly and accurately according to the image recognition technology and the cabin door is closed in time, and the working efficiency of a robot is improved.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

Fig. 6 is a schematic diagram of an electronic device 600 provided by an embodiment of the disclosure. As shown in fig. 6, the electronic apparatus 600 of this embodiment includes: a processor 601, a memory 602, and a computer program 603 stored in the memory 602 and executable on the processor 601. The steps in the various method embodiments described above are implemented when the computer program 603 is executed by the processor 601. Alternatively, the processor 601 realizes the functions of each module/unit in the above-described apparatus embodiments when executing the computer program 603.

Illustratively, the computer program 603 may be partitioned into one or more modules/units, which are stored in the memory 602 and executed by the processor 601 to complete the disclosure. One or more modules/units may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program 603 in the electronic device 600.

The electronic device 600 may be a desktop computer, a notebook, a palm computer, a cloud server, or other electronic devices. The electronic device 600 may include, but is not limited to, a processor 601 and a memory 602. Those skilled in the art will appreciate that fig. 6 is merely an example of an electronic device 600 and does not constitute a limitation of the electronic device 600 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.

The Processor 601 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 602 may be an internal storage unit of the electronic device 600, for example, a hard disk or a memory of the electronic device 600. The memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device 6. Further, the memory 602 may also include both internal storage units and external storage devices of the electronic device 600. The memory 602 is used for storing computer programs and other programs and data required by the electronic device. The memory 602 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other ways. For example, the above-described apparatus/electronic device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, and multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method in the above embodiments, and may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the above methods and embodiments. The computer program may comprise computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain suitable additions or additions that may be required in accordance with legislative and patent practices within the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals or telecommunications signals in accordance with legislative and patent practices.

The above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present disclosure, and are intended to be included within the scope of the present disclosure.

Claims

1. A method of robotic hatch closing, the method comprising:

when a cabin door of a storage cabin of the robot is in an open state, repeatedly acquiring a newly shot image in the cabin and inputting the image in the cabin into a preset cabin emptying model to obtain an emptying result until the number of times that the storage cabin is empty as represented by the continuous emptying result reaches a preset threshold value, wherein the image in the cabin is obtained after shooting the interior of the storage cabin by a camera in the cabin of the storage cabin;

and generating a cabin door closing instruction to control the cabin door of the storage cabin to be closed.

2. The method of claim 1, wherein the training method of the cabin emptying model comprises the following steps:

acquiring an image data set in a sample cabin, wherein the image data set in the sample cabin comprises images in the sample cabin and empty cabin identification labels corresponding to the images in the sample cabin, and the empty cabin identification labels are training labels for marking whether object containing cabins corresponding to the images in the sample cabin are empty cabins or not;

and training the deep learning model by using the sample cabin images and the corresponding empty cabin identification labels thereof to obtain the trained cabin interior emptying model.

3. The method of claim 2, wherein prior to the acquiring the sample capsule image dataset, the method further comprises:

and performing image enhancement on the images in the sample cabin.

4. The method according to claim 1, wherein the step of repeatedly acquiring a newly-shot cabin image and inputting the cabin image into a preset cabin emptying model to obtain an emptying result comprises:

periodically controlling the camera to shoot images in the cabin according to a preset time period;

and responding to the received images in the cabin sent by the camera, and inputting the received images in the cabin into the judgment model in the cabin to obtain a judgment result.

5. The method according to claim 1, wherein before repeating the steps of obtaining a newly-shot cabin image and inputting the cabin image into a preset cabin emptying model to obtain an emptying result, the method further comprises:

acquiring the working state information of the robot;

and judging whether the compartment door of the storage compartment is in an open state or not according to the working state information.

6. The method according to claim 1, wherein before repeating the steps of obtaining a newly-shot cabin image and inputting the cabin image into a preset cabin emptying model to obtain an emptying result, the method further comprises:

acquiring a cabin door opening instruction;

and determining that the compartment door of the storage compartment is in an open state according to the compartment door opening instruction.

7. The method according to claim 1, wherein the step of repeatedly acquiring a newly-shot intra-cabin image and inputting the intra-cabin image into a preset intra-cabin emptying model to obtain an emptying result until the number of times that the emptying result indicates that the object accommodating compartment is empty reaches a preset threshold value comprises:

counting the times of representing the object placing cabin as an empty cabin by the continuous occurrence of the air judgment result;

and if the judgment result shows that the object containing cabin is not an empty cabin in the counting process, resetting the count value.

8. A robotic hatch closing device, comprising:

the judging module is used for repeatedly acquiring a newly shot intra-cabin image and inputting the intra-cabin image into a preset intra-cabin emptying model when a cabin door of a storage cabin of the robot is in an open state to obtain an emptying result until the number of times that the storage cabin is empty as represented by the emptying result reaches a preset threshold value, wherein the intra-cabin image is obtained after shooting the interior of the storage cabin by a camera in the cabin of the storage cabin;

and the generating module is used for generating a cabin door closing instruction so as to control the cabin door of the storage cabin to be closed.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of a method according to any one of claims 1 to 7.