Stacker crane system and method thereof

Abstract

The stacker crane system disclosed in this document comprises a carriage on which an item is loaded, a camera unit positioned on one side inside the carriage and configured to capture an image including the item, and a control unit operatively connected to the camera unit. The control unit may be configured to analyze the image using data learned through machine learning and, based on the analysis results, detect the type of the item and the position where the item is loaded on the carriage or fork.

Description

Technology Field

[0001] The embodiments disclosed in this document relate to stacker crane systems and management thereof. Background Technology

[0002] In automated warehouse systems, which are being widely applied in the field of logistics automation, stacker cranes are utilized to move goods or processed products from within factories or warehouses to cargo racks. For example, stacker cranes can be used in the activation process of secondary batteries. First, the tray (or carriage) to be inspected is fed into the inspection location via the stacker crane, and an activation operation is performed where a charging / discharging device is connected to discharge and charge each battery pack contained within the tray. Once the activation operation is complete, the tray is released by the stacker crane, and this process is repeated until the activation process for all trays to be inspected is finished. In this case, the stacker crane is structured to allow for the input and output of multiple trays, and efficiency can be increased by moving them simultaneously. The problem to be solved

[0003] A stacker crane system requires verification that the items (or bobbins) being moved via the carriage are valid. Additionally, for the stacker crane to move the carriage or items using the forks, the items must be positioned precisely on the forks; this is because even a slight misalignment of the item type or loading position on the forks can cause significant disruptions to the process. Since a single sensor cannot accurately detect items and determine their position, multiple sensors, such as position and limit sensors, may be utilized; however, increasing the number of sensors can lead to a shortage of internal space within the carriage.

[0004] The technical problems of the embodiments disclosed in this document are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0005] The stacker crane system disclosed in this document comprises a carriage on which an item is loaded, a camera unit positioned on one side inside the carriage and configured to capture an image including the item, and a control unit operatively connected to the camera unit. The control unit may be configured to analyze the image using data learned through machine learning and, based on the analysis results, detect the type of the item and the position where the item is loaded on the carriage or fork.

[0006] The method of operation of the stacker crane system disclosed in this document may include the operation of capturing an image including an item using a camera unit disposed on one side inside the carriage, the operation of analyzing the image using data learned through machine learning, and the operation of detecting the type of the item and the position where the item is loaded on the carriage or fork based on the analysis result. Effects of the invention

[0007] A stacker crane system according to one embodiment disclosed in this document can minimize errors caused by the position of an item located on the fork while simultaneously securing internal space of the carriage.

[0008] A battery stacker crane system according to one embodiment disclosed in this document can improve the accuracy of identifying the type and location of items. Brief explanation of the drawing

[0009] FIG. 1 shows a stacker crane system according to various embodiments. Figure 2 is a front view and a side view of a stacker crane. FIG. 3 is a perspective view and a plan view of the carriage and fork. FIG. 4 shows a stacker crane system including a shooting unit according to various embodiments. FIG. 5 illustrates a block diagram of a stacker crane system including a shooting unit according to various embodiments. FIG. 6 illustrates the operation flowchart of a stacker crane system for analyzing images according to various embodiments. FIG. 7 illustrates a flowchart of the operation of a stacker crane system for analyzing images based on learning data according to various embodiments. Specific details for implementing the invention

[0010] Hereinafter, various embodiments disclosed in this document will be described in detail with reference to the attached drawings. In this document, the same reference numerals are used for identical components in the drawings, and redundant descriptions of identical components are omitted.

[0011] With respect to the various embodiments disclosed in this document, specific structural or functional descriptions are provided merely for the purpose of explaining the embodiments, and the various embodiments disclosed in this document may be implemented in various forms and should not be interpreted as being limited to the embodiments described in this document.

[0012] Expressions such as "first," "second," "first," or "second" used in various embodiments may modify various components regardless of order and / or importance and do not limit said components. For example, without departing from the scope of the embodiments disclosed herein, the first component may be named the second component, and similarly, the second component may be renamed the first component.

[0013] The terms used in this document are used merely to describe specific embodiments and are not intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly indicates otherwise.

[0014] All terms used herein, including technical or scientific terms, may have the same meaning as generally understood by those skilled in the art of the embodiments disclosed herein. Terms defined in commonly used dictionaries may be interpreted as having the same or similar meaning as they have in the context of the relevant technology, and are not to be interpreted in an ideal or overly formal sense unless explicitly defined in this document. In some cases, even terms defined in this document shall not be interpreted to exclude the embodiments disclosed herein.

[0015] FIG. 1 shows a stacker crane system (1) according to various embodiments. The stacker crane system (1) shown in FIG. 1 is merely an example, and the positions and shapes of each component are not limited to the example shown in FIG. 1.

[0016] Referring to FIG. 1, the stacker crane system (1) may include a stacker crane (100) for bringing an item (50) into a rack (10) or taking out an item (50) from a rack (10). For example, the stacker crane (100) can load an item (50) onto a carriage (150) and move up, down, left, and right to position it at a designated rack (10). The stacker crane (100) can use a fork to place the item (50) onto the designated rack (10). By the same principle, the stacker crane (100) can take out an item (50) from the rack (10) using a fork.

[0017] FIG. 2 is a front and side view of a stacker crane. FIG. 2(b) may be a side view of the stacker crane (100) shown in FIG. 2(a) viewed from direction A. FIG. 3(a) is a perspective view of the carriage and fork, and FIG. 3(b) is a top view of the carriage and fork.

[0018] Referring to FIGS. 2 and 3, the item (50) may be, for example, a bobbin. The item (50) loaded on the carriage (150) may be moved via a stacker crane (100) and then transferred via a fork (170). At this time, if the type or loading position of the item (50) does not satisfy the specified conditions, a major disruption may occur in the process, so it is necessary to accurately detect the type and loading position of the item (50). For example, to identify the item (50), a product detection sensor may be placed on the side (301, 304, 305, 306, or 307) of the carriage (150). Additionally, to detect the loading position of the item (50), a limit sensor, a position sensor, and a home sensor may be placed on the top (302) or side (303) of the fork (170). However, when these multiple sensors are placed on the fork (170) or carriage (150), the internal space of the carriage (150) may be reduced, and determining the type and loading position of the item (50) by combining data obtained from each of the multiple sensors may require complex logic. In addition, considering the structure of the carriage (150), it may not be easy to attach multiple sensors.

[0019] FIG. 4 shows a stacker crane system including a shooting unit according to various embodiments.

[0020] Referring to FIG. 4, the stacker crane system (1) may include a shooting unit (180) inside the carriage (150). The shooting unit (180) may be, for example, an image sensor or a camera. The shooting unit (180) may acquire an image including an item (50) inside the carriage (150). The shooting unit (180) may be positioned on one side of the carriage (150). For example, referring to FIG. 4 (a), the shooting unit (180) may be positioned on the upper side inside the carriage (150) so as to accurately detect the loading position of the item (150). Alternatively, the shooting unit (180) may be positioned at the upper center of the carriage (150). As another example, referring to FIG. 4 (b), the shooting unit (180) may be positioned on the side of the carriage (150). The stacker crane system (1) can secure internal space of the carriage (150) and simplify the algorithm logic for detecting items (50) by replacing multiple sensors with a shooting unit (180).

[0021] FIG. 5 illustrates a block diagram of a stacker crane system including a shooting unit according to various embodiments.

[0022] Referring to FIG. 5, the stacker crane system (1) may include a shooting unit (180) included inside a carriage (150), a control unit (200) connected to the shooting unit (180) via wired or wireless connection, and a learning unit (220) connected to the control unit (200).

[0023] The control unit (200) can control the overall operation of the stacker crane system (1). In various embodiments, the control unit (200) may include a single processor core or a plurality of processor cores. For example, the control unit (200) may include a multi-core such as a dual-core, quad-core, or hexa-core. According to embodiments, the control unit (200) may further include a cache memory located internally or externally. According to embodiments, the control unit (200) may be configured with one or more processors. For example, the control unit (200) may include at least one of an application processor, a communication processor, or a GPU (graphical processing unit). All or part of the control unit (200) may be electrically or operably coupled with or connected to other components within the stacker crane system (1) (e.g., the imaging unit (180) or the learning unit (220)). The control unit (200) may receive commands from other components of the stacker crane system (1), interpret the received commands, and perform calculations or process data according to the interpreted commands. The control unit (200) may process data or signals generated or produced by a program. For example, the control unit (200) may request commands, data, or signals from a memory (not shown) to execute or control a program.

[0024] The learning unit (220) may be, for example, a learning processor for performing machine learning. The learning unit (220) may learn data through an artificial neural network model and store the learned data and learning history. The artificial neural network model may be stored in a space allocated in memory. The space allocated in memory stores the artificial neural network model that is being learned or has been learned through the learning unit (220), and when the artificial neural network model is updated through learning, the updated artificial neural network model may be stored. The space allocated in memory may store the learned model by classifying it into multiple versions according to the learning time or learning progress, etc. When a specific operation (e.g., image analysis) is performed, the learning unit (220) may analyze historical information indicating the execution of the specific operation through data analysis and machine learning algorithms (or techniques), and perform an update of previously learned information based on the analyzed information. The learning unit (220) may improve the accuracy of the data analysis and machine learning algorithms and performance based on the updated information.

[0025] The control unit (200) can analyze an image acquired from the shooting unit (180) using data learned through the learning unit (220) and determine the type and loading location of the item (50) based on the analyzed result. For example, the control unit (200) can extract features of an object included in the image using a convolutional neural network (CNN) algorithm and determine whether the object is of a specified type of item (50) through the extracted features. Additionally, the control unit (200) can determine whether the recognized item (50) is located at a specified location (e.g., a fork) or is outside of that location through binary classification using a logistic algorithm. The control unit (200) can store history information regarding the analysis in memory. The control unit (200) can determine the best combination for executing a specific function through the stored history information and predictive modeling.

[0026] Although not shown in FIG. 5, the stacker crane system (1) may further include an output unit to notify the user if the item (50) does not satisfy a specified condition. The output unit may be, for example, a display capable of outputting the current status as a graphic user interface (GUI), a speaker capable of outputting sound, or a haptic module capable of outputting vibration.

[0027] FIG. 6 illustrates a flowchart of the operation of a stacker crane system for analyzing images according to various embodiments. The operations shown in FIG. 6 or FIG. 7 below may be implemented by the stacker crane system (1) or through some components (e.g., a control unit (200)).

[0028] Referring to FIG. 6, in operation 610, the stacker crane system (1) can acquire an image of the inside of the carriage (150). According to an embodiment, the stacker crane system (1) can acquire an image at designated intervals or by user input. As another example, the stacker crane system (1) can acquire an image when an item (50) is placed on the fork or the carriage (150). In this case, the stacker crane system (1) can determine the time of image acquisition through the movement or position coordinates of the fork.

[0029] In operation 620, the stacker crane system (1) can analyze the acquired image using the learned data.

[0030] In operation 630, the stacker crane system (1) can detect the type of item (50) and the location where the item (50) is loaded on the carriage (150) or the fork based on the result of image analysis. For example, the stacker crane system (1) can determine whether the recognized item (50) is a bobbin through feature point extraction.

[0031] FIG. 7 illustrates a flowchart of the operation of a stacker crane system for analyzing images based on learning data according to various embodiments.

[0032] Referring to FIG. 7, in operation 710, the stacker crane system (1) can obtain an image of the inside of the carriage (150).

[0033] In operation 720, the stacker crane system (1) can extract features (or feature points) of an item (50) in an image based on a CNN algorithm. The stacker crane system (1) can determine the type of item (50) based on the extracted features.

[0034] In operation 730, the stacker crane system (1) can determine the location of the recognized item (50) based on a logistic algorithm.

[0035] In operation 740, the stacker crane system (1) can determine whether the item (50) moves out of the designated position. If the item (50) does not move out of the designated position, the stacker crane system (1) can repeat operations 710 through 740. If the item (50) moves out of the designated position, in operation 750, the stacker crane system (1) can output a notification.

[0036] In the foregoing, although all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purposes of the embodiments disclosed in this document, all components may be selectively combined in one or more ways to operate.

[0037] Furthermore, terms such as "include," "compose," or "have" as described above, unless specifically stated otherwise, mean that the relevant component may be inherent; thus, they should be interpreted as allowing for the inclusion of additional components rather than excluding them. All terms, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as those defined in advance, should be interpreted in accordance with their contextual meanings in the relevant technology and, unless explicitly defined in this document, should not be interpreted in an ideal or overly formal sense.

[0038] The foregoing description is merely an illustrative explanation of the technical concept disclosed in this document, and a person skilled in the art to which the embodiments disclosed in this document pertain can make various modifications and variations within the scope of the essential characteristics of the embodiments disclosed in this document. Accordingly, the embodiments disclosed in this document are intended to explain, not limit, the technical concept of the embodiments disclosed in this document, and the scope of the technical concept disclosed in this document is not limited by such embodiments. The scope of protection of the technical concept disclosed in this document shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of this document.

Claims

Claim 1 A stacker crane system used in a battery activation process comprises: a carriage on which an item is loaded; a shooting unit disposed on one side inside the carriage and configured to capture an image including the item; and a control unit operatively connected to the shooting unit; wherein the control unit is configured to analyze the image using data learned through machine learning and, based on the analysis result, detect the type of the item and the position where the item is loaded on the carriage or fork, extract features of the item using a convolutional neural network (CNN) algorithm, determine the type of the item based on the extracted features, determine whether the item deviates from a designated position based on a logistic algorithm, and, based on whether the item is a battery and whether the item deviates from a designated position, connect a charging / discharging device to the battery and proceed with the activation process. Claim 2 A stacker crane system used in the activation process of a battery according to claim 1, wherein the control unit is configured to output a notification when the article is detected to deviate from a designated position based on the analysis result. Claim 3 A stacker crane system used in the activation process of a battery according to claim 1, wherein the control unit is configured to learn the data based on the convolutional algorithm and the logistic algorithm. Claim 4 delete Claim 5 In claim 3, the imaging unit comprises at least one camera, a stacker crane system used in the activation process of the battery. Claim 6 A method of operation of a stacker crane system used in a battery activation process, comprising: an action of capturing an image including an item using a shooting unit disposed on one side inside a carriage; an action of analyzing the image using data learned through machine learning; and an action of detecting the type of the item and the position where the item is loaded on the carriage or fork based on the analysis result; wherein the action of analyzing the image includes an action of extracting features of the item using a convolutional neural network (CNN) algorithm and determining the type of the item based on the extracted features, and an action of determining whether the item deviates from a designated position based on a logistic algorithm; and further comprising an action of connecting a charging / discharging device to the battery and proceeding with the activation process based on whether the item is a battery and whether the item deviates from a designated position. Claim 7 A method of operation of a stacker crane system used in the activation process of a battery, further comprising, in claim 6, an operation of outputting a notification when the article is detected to have deviated from a designated position based on the analysis result. Claim 8 A method of operation of a stacker crane system used in the activation process of a battery, further comprising, in claim 6, an operation of learning the data based on the convolutional algorithm and the logistic algorithm. Claim 9 delete

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