Bin docking method and apparatus, electronic device, and medium

Abstract

This disclosure relates to the field of robotics, providing a rack docking method, apparatus, electronic device, and medium. The method includes: acquiring a rack docking task; determining a target location based on the rack docking task; controlling a chassis robot to move to the target location and acquiring rack images via the chassis robot's camera; determining feature acquisition results based on the rack images; identifying target features based on the feature acquisition results in the rack images; and controlling the chassis robot to perform rack docking based on the target features. This implementation enables rack identification by acquiring features through image recognition when racks cannot be found using docking features, thus avoiding identification errors and improving the identification success rate.

Description

Technical Field

[0001] This disclosure relates to the field of robotics, and in particular to rack docking methods, apparatus, electronic devices, and media. Background Technology

[0002] Currently, the robot body and the cargo compartment can be separated. Depending on the needs of different scenarios, the cargo compartment can be replaced with different functional forms, combining with the robot body to form a multifunctional service robot to meet different customer needs. During the process of the robot chassis searching for the cargo compartment, there may be task failures due to recognition errors, being mistaken for moving, or accidental movement.

[0003] Therefore, how to accurately identify the warehouse shelves so that the robot chassis can accurately acquire the warehouse is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, embodiments of the present disclosure provide a rack docking method, apparatus, electronic device, and medium to solve the problem in the prior art of how to accurately identify the racks of a warehouse so that the robot chassis can accurately acquire the warehouse.

[0005] A first aspect of this disclosure provides a rack docking method, comprising: acquiring a rack docking task; determining a target location based on the rack docking task; controlling a chassis robot to move to the target location and acquiring rack images through the chassis robot's camera; determining feature acquisition results based on the rack images and identifying target features based on the feature acquisition results; and controlling the chassis robot to perform rack docking based on the target features.

[0006] A second aspect of this disclosure provides a rack docking device, comprising: a task acquisition unit configured to acquire a rack docking task and determine a target location based on the rack docking task; an image acquisition unit configured to control a chassis robot to move to the target location and acquire rack images through a camera of the chassis robot; a feature recognition unit configured to determine feature acquisition results based on the rack images and identify target features based on the feature acquisition results; and a rack docking unit configured to control the chassis robot to perform rack docking based on the target features.

[0007] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0008] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0009] The beneficial effects of this disclosed embodiment compared to the prior art are as follows: First, a rack docking task is acquired, and a target location is determined based on the rack docking task; then, a chassis robot is controlled to move to the target location, and rack images are acquired through the chassis robot's camera; subsequently, feature acquisition results are determined based on the rack images, and target features are identified based on the feature acquisition results; finally, based on the target features, the chassis robot is controlled to perform rack docking. The method provided by this disclosure can acquire a rack docking task, determine a target location based on the rack docking task; control a chassis robot to move to the target location, and acquire rack images through the chassis robot's camera; determine feature acquisition results based on the rack images, and identify target features based on the feature acquisition results; and control the chassis robot to perform rack docking based on the target features. This implementation achieves rack identification by acquiring features through image recognition when racks are not found through docking features, thus avoiding identification errors and improving the identification success rate. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of an application scenario of a rack docking method according to some embodiments of the present disclosure;

[0012] Figure 2 These are flowcharts of some embodiments of the rack docking method according to this disclosure;

[0013] Figure 3 These are schematic diagrams of structures of some embodiments of the rack docking device according to this disclosure;

[0014] Figure 4 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0015] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0016] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0017] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0018] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0019] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0020] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] Figure 1 This is a schematic diagram of an application scenario of a rack docking method according to some embodiments of the present disclosure.

[0022] exist Figure 1 In the application scenario, firstly, the computing device 101 can acquire the rack docking task 102 and determine the target position 103 based on the rack docking task 102. Then, the computing device 101 can control the chassis robot to move to the target position 103 and acquire rack images 104 through the chassis robot's camera. The computing device 101 can determine the feature acquisition results based on the rack images 104 and identify target features 105 based on the feature acquisition results. Finally, the computing device 101 can control the chassis robot to perform rack docking based on the target features 105, as shown by reference numeral 106 in the attached figure.

[0023] It should be noted that the aforementioned computing device 101 can be either hardware or software. When the computing device 101 is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device 101 is software, it can be installed in the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0024] It should be understood that Figure 1 The number of computing devices shown is merely illustrative. Any number of computing devices can be used depending on implementation needs.

[0025] Figure 2 This is a flowchart of some embodiments of the rack docking method according to the present disclosure. Figure 2 The rack docking method can be provided by Figure 1 The computing device 101 performs the operation. For example... Figure 2 As shown, the rack docking method includes:

[0026] Step S201: Obtain the rack docking task and determine the target location based on the rack docking task.

[0027] In some embodiments, the entity performing the rack docking method (such as...) Figure 1 The computing device 101 shown can acquire the rack docking task and determine the target position based on the rack docking task. Here, the rack can be the rack of a warehouse that is matched with the robot chassis.

[0028] To elaborate further, obtaining the rack docking task and determining the target location based on the rack docking task includes: obtaining the rack docking task, determining the task information based on the rack docking task, and parsing the task information to obtain the target location.

[0029] For example, after receiving the warehouse docking task, the task information is obtained: "Go to Area C to retrieve Warehouse No. 11". Based on this task information, the target location can be determined to be Area C.

[0030] Step S202: Control the chassis robot to move to the target location and acquire images of the warehouse rack through the chassis robot's camera.

[0031] In some embodiments, controlling a chassis robot to move to a target location includes: determining the current location, and performing route planning based on the current location and the target location to determine at least one initial route; determining a target route based on the at least one initial route; and controlling the chassis robot to move to the target location based on the target route.

[0032] Continuing with the previous example, upon receiving the warehouse docking task, the task information is obtained: "Proceed to Area C to retrieve Warehouse No. 11." Based on this task information, the target location is determined to be Area C. The current position of the robot chassis is then obtained, and route planning is performed based on the current position, listing at least one route that can reach the target location. If there are more than two routes, the target route can be determined based on preset rules, such as the route with the shortest travel time. After determining the target route, the robot chassis is controlled to move to Area C.

[0033] In some embodiments, acquiring shelf images via the camera of the chassis robot includes: sending a camera command to the camera of the chassis robot and controlling the chassis robot to perform a rotation operation; acquiring shelf images via the camera of the chassis robot while the chassis robot is rotating.

[0034] Using the previous example, after arriving at area C, the robot chassis acquires images of the surroundings through a camera device. By rotating the robot chassis, images of the warehouse rack in all directions can be obtained.

[0035] Step S203: Determine the feature acquisition results based on the warehouse shelf image, and identify the target features based on the feature acquisition results in the warehouse shelf image.

[0036] In some embodiments, determining the feature acquisition result based on the rack image includes: performing feature matching based on the rack image to determine the feature matching result; if the feature matching result indicates that there is no docking feature, determining that the feature acquisition result is not acquired; if the feature matching result indicates that there is a docking feature, determining that the feature acquisition result is acquired.

[0037] For example, after obtaining the warehouse shelf image, the QR code on the warehouse shelf is identified to determine warehouse number 11. If warehouse number 11 is identified, the feature acquisition result is determined to be "acquired". If warehouse number 11 is not identified, the feature acquisition result is determined to be "not acquired".

[0038] Step S204: Based on the target features, control the chassis robot to dock with the warehouse rack.

[0039] In some embodiments, identifying target features based on feature acquisition results in a warehouse rack image includes: if the feature acquisition result is not obtained, inputting the warehouse rack image into a preset image recognition model to obtain target features.

[0040] For example, when the robot arrives at the location where it needs to dock with the upper warehouse and does not find any docking features, it rotates in place to identify the reflective dot code features through AI vision, locks onto the upper warehouse to dock with, and moves to dock.

[0041] Furthermore, the method of this application also includes: acquiring a target warehouse shelf image, determining target warehouse shelf features based on the target warehouse shelf image; establishing a correlation between the target warehouse shelf features and the target warehouse shelf image; and storing the correlation in a database.

[0042] Specifically, the reflective dot code features of each warehouse rack can be associated with the warehouse rack image in advance and stored in the database, which can improve the accuracy of recognition.

[0043] The beneficial effects of this disclosed embodiment compared to the prior art are as follows: First, a rack docking task is acquired, and a target location is determined based on the rack docking task; then, a chassis robot is controlled to move to the target location, and rack images are acquired through the chassis robot's camera; subsequently, feature acquisition results are determined based on the rack images, and target features are identified based on the feature acquisition results; finally, based on the target features, the chassis robot is controlled to perform rack docking. The method provided by this disclosure can acquire a rack docking task, determine a target location based on the rack docking task; control a chassis robot to move to the target location, and acquire rack images through the chassis robot's camera; determine feature acquisition results based on the rack images, and identify target features based on the feature acquisition results; and control the chassis robot to perform rack docking based on the target features. This implementation achieves rack identification by acquiring features through image recognition when racks are not found through docking features, thus avoiding identification errors and improving the identification success rate.

[0044] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0045] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0046] Figure 3 These are structural schematic diagrams of some embodiments of the rack docking device according to this disclosure. For example... Figure 3 As shown, the rack docking device includes: a task acquisition unit 301, an image acquisition unit 302, a feature recognition unit 303, and a rack docking unit 304. The task acquisition unit 301 is configured to acquire a rack docking task and determine a target location based on the task. The image acquisition unit 302 is configured to control a chassis robot to move to the target location and acquire rack images using the chassis robot's camera. The feature recognition unit 303 is configured to determine feature acquisition results based on the rack images and identify target features based on these results. The rack docking unit 304 is configured to control the chassis robot to perform rack docking based on the target features.

[0047] In some optional implementations of some embodiments, the task acquisition unit 301 of the rack docking device is further configured to: acquire rack docking tasks, determine task information based on rack docking tasks, and parse the task information to obtain the target location.

[0048] In some alternative implementations of some embodiments, the image acquisition unit 302 of the rack docking device is further configured to: determine the current position, and perform route planning based on the current position and the target position to determine at least one initial route; determine the target route based on the at least one initial route; and control the chassis robot to move to the target position based on the target route.

[0049] In some alternative implementations of some embodiments, the image acquisition unit 302 of the rack docking device is further configured to: send a camera command to the camera of the chassis robot and control the chassis robot to perform a rotation operation; and acquire rack images through the camera of the chassis robot while the chassis robot is rotating.

[0050] In some optional implementations of some embodiments, the feature recognition unit 303 of the rack docking device is further configured to: perform feature matching based on the rack image to determine the feature matching result; if the feature matching result indicates that there is no docking feature, determine that the feature acquisition result is not acquired; if the feature matching result indicates that there is a docking feature, determine that the feature acquisition result is acquired.

[0051] In some alternative implementations of some embodiments, the rack docking unit 304 of the rack docking device is further configured to: input the rack image into a preset image recognition model to obtain target features when the feature acquisition result is not obtained.

[0052] In some optional implementations of some embodiments, the rack docking unit 304 of the rack docking device is further configured to: acquire a target rack image, determine target rack features based on the target rack image; establish an association between the target rack features and the target rack image; and store the association in a database.

[0053] The following is for reference. Figure 4 It illustrates electronic devices suitable for implementing some embodiments of this disclosure (e.g., Figure 1 A schematic diagram of the structure of the computing device 101)400. Figure 4 The server shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0054] like Figure 4As shown, electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from storage device 408 into random access memory (RAM) 403. RAM 403 also stores various programs and data required for the operation of electronic device 400. Processing device 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0055] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic device 400 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device 400 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 4 Each box shown can represent a device or multiple devices as needed.

[0056] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 409, or installed from storage device 408, or installed from ROM 402. When the computer program is executed by processing device 401, it performs the functions defined above in the methods of some embodiments of this disclosure.

[0057] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0058] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0059] The aforementioned computer-readable medium may be included in the aforementioned device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire a rack docking task; determine a target location based on the rack docking task; control a chassis robot to move to the target location and acquire rack images through the chassis robot's camera; determine feature acquisition results based on the rack images and identify target features based on the feature acquisition results in the rack images; and control the chassis robot to perform rack docking based on the target features.

[0060] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0061] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0062] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a task acquisition unit, an image acquisition unit, a feature recognition unit, and a rack docking unit. The names of these units do not necessarily limit the specific unit; for example, a task acquisition unit may also be described as "a unit that acquires rack docking tasks and determines target locations based on the rack docking tasks."

[0063] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0064] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A bin rack docking method, characterized by, include: Obtain the rack docking task, and determine the target location based on the rack docking task; Control the chassis robot to move to the target location and acquire images of the warehouse rack through the chassis robot's camera; Based on the rack image, the feature acquisition result is determined, and the target feature is identified based on the feature acquisition result; Based on the target characteristics, the chassis robot is controlled to perform rack docking; The acquisition of rack images via the camera of the chassis robot includes: Send a camera command to the camera of the chassis robot and control the chassis robot to perform a rotation operation; While the chassis robot is rotating, images of the warehouse rack are acquired through the camera of the chassis robot; The determination of feature acquisition results based on the shelf image includes: Based on the shelf image, feature matching is performed to determine the feature matching result; If the feature matching result indicates that no matching feature exists, the feature acquisition result is determined to be unacquired. If the feature matching result indicates the existence of the docking feature, the feature acquisition result is determined to be acquired. The step of identifying and determining target features based on the feature acquisition results of the warehouse shelf image includes: If the feature acquisition result is not obtained, the warehouse rack image is input into a preset image recognition model to obtain the target features.

2. The cartridge dock method of claim 1, wherein, The process of acquiring the rack docking task and determining the target location based on the rack docking task includes: Obtain rack docking tasks and determine task information based on the rack docking tasks; The task information is parsed to obtain the target location.

3. The rack docking method as described in claim 1, characterized in that, The control of the chassis robot to move to the target location includes: Determine the current location, and perform route planning based on the current location and the target location to determine at least one initial route; Determine the target route based on the at least one initial route; Based on the target route, the chassis robot is controlled to move to the target location.

4. The rack docking method as described in claim 1, characterized in that, The method further includes: Acquire an image of the target warehouse rack, and determine the characteristics of the target warehouse rack based on the image; Establish a correlation between the target warehouse shelf features and the target warehouse shelf image; The relationships are stored in the database.

5. A rack docking device, characterized in that, include: The task acquisition unit is configured to acquire rack docking tasks and determine the target location based on the rack docking tasks; The image acquisition unit is configured to control the chassis robot to move to the target location and acquire rack images through the chassis robot's camera; The feature recognition unit is configured to determine the feature acquisition result based on the warehouse shelf image, and to identify the target features of the warehouse shelf image based on the feature acquisition result; The rack docking unit is configured to control the chassis robot to perform rack docking based on the target features; The acquisition of rack images via the camera of the chassis robot includes: Send a camera command to the camera of the chassis robot and control the chassis robot to perform a rotation operation; While the chassis robot is rotating, images of the warehouse rack are acquired through the camera of the chassis robot; The determination of feature acquisition results based on the shelf image includes: Based on the shelf image, feature matching is performed to determine the feature matching result; If the feature matching result indicates that no matching feature exists, the feature acquisition result is determined to be unacquired. If the feature matching result indicates the existence of the docking feature, the feature acquisition result is determined to be acquired. The step of identifying and determining target features based on the feature acquisition results of the warehouse shelf image includes: If the feature acquisition result is not obtained, the warehouse rack image is input into a preset image recognition model to obtain the target features.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4.

Images