A method for determining a battery processing process, a computing device on which such a method is implemented, and a system comprising the computing device

Abstract

According to one embodiment of the present disclosure, a method of operation of a computing device for controlling a process for processing a battery using at least one robot device may be provided, comprising the steps of: acquiring information about a battery by at least one processor included in the computing device; identifying whether a communication connection is possible with a battery management system (BMS) connected to the battery based on the information about the battery; selecting one of at least two predetermined process sequences based on whether a communication connection is possible; and controlling the at least one robot device to perform a battery processing process according to the selected process sequence.

Description

Technology Field

[0001] The present disclosure relates to a battery processing system. More specifically, it relates to a system for transporting, storing, pre-processing, and dismantling batteries. Background Technology

[0002] Recently, electric vehicles have become widely adopted due to the surge in global interest in the environment. Consequently, the demand for batteries used in electric vehicles is skyrocketing. Meanwhile, automotive batteries need to be replaced within an average of three to four years, leading to an exponential increase in the number of discarded waste batteries.

[0003] Accordingly, there is an increasing demand for dismantling technology that collects and safely dismantles batteries used in electric vehicles. Furthermore, to safely dismantle a battery, it is crucial to accurately diagnose its condition and discharge any remaining current inside it.

[0004] Currently, the disassembly of various battery components is carried out manually. This is because battery disassembly requires a significant level of expertise and precision.

[0005] The prior art literature related to this is as follows.

[0006] Korean Patent Application No. 10-2022-0111328 (March 15, 2023) The problem to be solved

[0007] The present disclosure provides a battery processing system.

[0008] The present disclosure may provide a battery storage method according to battery information.

[0009] The present disclosure may provide a method and system for automatically processing batteries using a robotic device.

[0010] The present disclosure may provide a method for determining a battery processing process based on battery information.

[0011] The present disclosure may provide a method for learning an artificial intelligence engine for controlling a robot device and a method for controlling a robot using the learned artificial intelligence engine.

[0012] Meanwhile, the problems to be solved in this disclosure are not limited to those described above, and problems not mentioned will be clearly understood by those skilled in the art to which the invention included in this disclosure belongs from this specification and the attached drawings. means of solving the problem

[0013] According to one embodiment of the present disclosure, a battery transfer and storage method may be provided, comprising: receiving a first control signal instructing a first battery to be stored in a storage—the storage comprising a plurality of slots—by at least one processor included in an electronic device; obtaining identification information including battery information corresponding to the first battery and a plurality of identifiers corresponding to each of the plurality of slots; determining at least one slot among the plurality of slots as a target slot based on at least one of the identification information or the battery information; transferring the first battery to accommodate the first battery in the target slot; and updating the identification information by associating the battery information with the identifier of the target slot.

[0014] Additionally, according to one embodiment of the present disclosure, a method of operation of a robot system for controlling the operation of at least one robot device may be provided, comprising: controlling the robot device to grasp at least one connector included in the at least one processing device using an end effector by at least one processor included in the robot system communicating with at least one processing device; acquiring sensing data associated with the battery from at least one sensor; setting a position corresponding to at least one port connected to the battery as a target position based on the sensing data; determining a first trajectory for the end effector to reach a first position corresponding to the target position; and determining a second trajectory for connecting the at least one connector grasped by the end effector that has reached the first position to the at least one port.

[0015] Additionally, according to one embodiment of the present disclosure, a method of operation of a computing device for controlling a process for processing a battery using at least one robot device may be provided, comprising the steps of: acquiring information about a battery by at least one processor included in the computing device; identifying whether a communication connection is possible with a battery management system (BMS) connected to the battery based on the information about the battery; selecting one of at least two predetermined process sequences based on whether a communication connection is possible; and controlling the at least one robot device to perform a battery processing process according to the selected process sequence.

[0016] Additionally, according to one embodiment of the present disclosure, a method of operation of a robot system for controlling the operation of at least one robot device implemented to perform at least one task based on a control signal, comprising: receiving a control signal instructing to dismantle a first component of a battery by at least one processor included in the robot system; acquiring sensing data associated with the battery from at least one sensor; identifying the position of a first fastening member for fixing the first component based on the sensing data and setting it as a first target position; determining a first trajectory for a first end effector to reach a first position corresponding to the first target position; controlling the first end effector that has reached the first position to perform a first task of dismantling the first fastening member from the battery; setting a second target position based on the sensing data and determining a second trajectory for a second end effector to reach a second position corresponding to the second target position; and controlling the second end effector that has reached the second position to perform a second task of separating the first component. A method of operation may be provided.

[0017] Additionally, according to one embodiment of the present disclosure, a method of operation of a robot system for controlling the operation of a robot device implemented to perform at least one task based on a control signal may be provided, comprising: receiving a control signal instructing to disconnect at least one wire of a battery by at least one processor included in the robot system; obtaining sensing data associated with the battery from at least one sensor; recognizing a location where a first wire and the battery are connected based on the sensing data and setting a first portion of the wire spaced apart from the connected location at a predetermined distance as a target location; determining a first trajectory for a first end effector to reach a first location corresponding to the target location; and controlling the first end effector that has reached the first location to perform a first task of disconnecting the first wire.

[0018] Additionally, according to one embodiment of the present disclosure, a method of operation of a robot system for controlling the operation of a robot device implemented to perform at least one task based on a control signal may be provided, comprising: receiving a control signal instructing to separate at least one buffer material of a battery by at least one processor included in the robot system; obtaining sensing data associated with the battery from at least one sensor; recognizing at least one adhesion position where at least one buffer material and the surface of the battery are adhered, and setting the at least one adhesion position as a first target position; determining a first trajectory for a first end effector to reach a first position corresponding to the first target position; controlling the first end effector that has reached the first position to perform a first task to remove the adhesion force between the at least one buffer material and the battery; and controlling the first end effector to perform a second task to separate the at least one buffer material from the battery after performing the first task.

[0019] Additionally, according to one embodiment of the present disclosure, a method of operation of a battery processing system comprising at least one robotic device may be provided, comprising the steps of: identifying a profile of a target battery by at least one processor; comparing the identified profile with pre-stored matching information—the matching information including process information for at least one processing process to be performed according to the profile of the battery—determining at least one processing process to be performed on the target battery according to the result of the comparison; and physically processing the target battery based on the determined at least one processing process.

[0020] Additionally, according to one embodiment of the present disclosure, a control method for at least one robot device including at least one subprocessor may be provided, comprising the steps of: transmitting a command to perform a first task to at least one subprocessor by at least one processor; performing at least one unit action for performing the first task using at least one robot device; identifying whether the first task has been completed by at least one processor; and stopping the operation of at least one robot device by at least one processor if the first task has not been completed even though a unit action exceeding a predetermined standard has been performed.

[0021] Additionally, according to one embodiment of the present disclosure, a robot system may be provided comprising at least one power device, a first robot device, a second robot device, a first end effector, at least one sensor, an artificial intelligence model, and at least one processor, wherein the at least one processor inputs input data obtained based on sensing data obtained from the at least one sensor to the artificial intelligence model, obtains at least one control value for the at least one power device based on output data obtained from at least one output layer of the artificial intelligence model, and determines a first trajectory for moving the first end effector connected to the first robot device to a target position based on the at least one control value, wherein the first trajectory is determined by obtaining at least one dynamic parameter corresponding to each of a plurality of points for moving to the target position based on the at least one control value, and the target position and the at least one dynamic parameter are determined by considering the control of the second robot device.

[0022] The means for solving the problem of the present invention are not limited to the means described above, and means not mentioned will be clearly understood by those skilled in the art from this specification and the accompanying drawings. Effects of the invention

[0023] According to the present disclosure, a battery processing system that provides an appropriate storage method according to battery information may be provided.

[0024] According to the present disclosure, a method for efficiently processing a battery using a robotic device may be provided.

[0025] The effects of the present invention are not limited to those described above, and unmentioned effects will be clearly understood by those skilled in the art from this specification and the accompanying drawings. Brief explanation of the drawing

[0026] FIG. 1 is a drawing illustrating an example of implementation of a battery processing automation system (1) according to various embodiments. FIG. 2 is a drawing illustrating systems for implementing a battery processing automation system (1) according to various embodiments. FIG. 3 is a drawing for explaining the detailed configuration of a robot system-based automation system according to various embodiments. FIG. 4 is a diagram illustrating the configuration of a battery transfer and storage system according to various embodiments. FIG. 5 is a drawing illustrating a method for a battery transfer and storage system to store a battery according to various embodiments. FIG. 6 is a diagram illustrating an example of a method for at least one processor to determine a target slot according to various embodiments. FIG. 7 is a drawing illustrating another example of a method for at least one processor to determine a target slot according to various embodiments. FIG. 8 is a drawing illustrating another example of a method for at least one processor to determine a target slot according to various embodiments. FIG. 9 is a drawing illustrating the operation of a battery transport and storage system to take out a stored battery according to various embodiments. FIG. 10 is a diagram illustrating a method for a battery storage system to monitor a stored battery according to various embodiments. FIG. 11 is a drawing illustrating another embodiment of a battery transfer and storage system for storing batteries according to various embodiments. FIG. 12 is a diagram illustrating the configuration of a battery pretreatment system according to various embodiments. FIG. 13 is a drawing illustrating an example of a method of operation of a robot system for controlling the operation of a robot device according to various embodiments. FIG. 14 is a diagram illustrating a method for a processor to control a robot device based on an artificial intelligence engine according to various embodiments. FIG. 15 is a diagram illustrating a method in which a battery processing system determines a process sequence based on information about a battery, according to various embodiments. FIG. 16 is a diagram illustrating an example of a process sequence based on information about a battery according to various embodiments. FIGS. 17 to 19 illustrate a method for a battery processing system to control a robot device to connect and disconnect a battery diagnostic device and a battery, according to various embodiments. FIG. 20 is a drawing illustrating a method for a battery processing system to store a battery according to a battery diagnostic grade, according to various embodiments. FIG. 21 is a diagram illustrating a method for a battery processing system to control a robot device to connect a battery discharge device and a battery according to various embodiments. FIG. 22 is a diagram illustrating a method for a battery processing system to control a robot device to connect a battery charging device and a battery according to various embodiments. FIG. 23 is a diagram illustrating the configuration of a battery dismantling system according to various embodiments. FIGS. 24 and 25 are drawings illustrating the operation method of a robot system for disassembling a battery pack according to various embodiments. FIG. 26 is a drawing illustrating the operation method of a robot system for disassembling the upper cover of a battery pack according to various embodiments. FIG. 27 is a drawing illustrating the operation method of a robot system for disassembling a battery pack module according to various embodiments. FIG. 28 is a diagram illustrating the operation method of a robot system for disassembling a battery pack cell according to various embodiments. FIG. 29 is a drawing illustrating a method for a battery processing system to store parts disassembled from a battery pack according to various embodiments. FIG. 30 is a flowchart illustrating a method for a battery processing system to disassemble a plurality of fastening members according to various embodiments. FIG. 31 is a drawing illustrating a method for a battery processing system to dismantle a plurality of fastening members according to various embodiments. FIG. 32 is a flowchart illustrating a method for a robot system to disconnect a wire connected to a battery according to various embodiments. FIG. 33 is a drawing illustrating a method for a robot system to disconnect a wire connected to a battery according to various embodiments. FIG. 34 is a flowchart illustrating a method for a robot system to dismantle a buffer connected to a battery according to various embodiments. FIG. 35 is a drawing illustrating a method for a robot system to dismantle a buffer connected to a battery according to various embodiments. FIG. 36 is a flowchart illustrating a method for a robot system to perform a task using a robot device according to various embodiments. FIG. 37 is a diagram illustrating a method for a robot system to estimate and correct the attitude of a battery based on sensing data according to various embodiments. FIG. 38 is a flowchart illustrating a method for a battery processing system to determine a processing process based on the profile of a battery, according to various embodiments. FIG. 39 is a diagram illustrating a method for a battery processing system to identify a battery profile according to various embodiments. FIG. 40 is a flowchart illustrating operations performed by a battery processing system after determining a processing process according to various embodiments. FIG. 41 is a diagram illustrating the operation performed by a battery processing system after determining a processing process according to various embodiments. FIG. 42 is a diagram illustrating a method for a battery processing system to determine whether a task can be performed based on sensing data for a battery, according to various embodiments. FIG. 43 is a diagram illustrating a method for a battery processing system to provide feedback during task execution according to various embodiments. FIG. 44 is a diagram illustrating a method in which a battery processing system, according to various embodiments, controls a plurality of robot devices using an artificial intelligence engine. FIG. 45 is a flowchart illustrating an embodiment in which a battery processing system, according to various embodiments, controls a plurality of robot devices using an artificial intelligence engine. FIG. 46 is a flowchart illustrating an embodiment in which a battery processing system, according to various embodiments, controls a plurality of robot devices using an artificial intelligence engine. FIG. 47 is a diagram illustrating an embodiment in which a battery processing system, according to various embodiments, controls a plurality of robot devices using an artificial intelligence engine. FIG. 48 is a drawing illustrating an example of a work chamber implemented in a battery processing system according to various embodiments. FIG. 49 is a drawing illustrating the internal configuration of a working chamber implemented in a battery processing system according to various embodiments. Specific details for implementing the invention

[0027] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In describing the embodiments, technical details that are well known in the art to which the present disclosure belongs and are not directly related to the present disclosure will be omitted. This is intended to convey the essence of the present disclosure more clearly without obscuring it by omitting unnecessary explanations.

[0028] The embodiments described in this specification are intended to clearly explain the concept of the invention to those skilled in the art to which the invention pertains, and therefore the invention is not limited to the embodiments described in this specification, and the scope of the invention should be interpreted to include modifications or variations that do not deviate from the concept of the invention.

[0029] The terms used in this specification have been selected to be as widely used as possible, taking into account their functions in the present invention; however, these terms may vary depending on the intent of those skilled in the art to which the present invention pertains, case law, or the emergence of new technologies. However, if a specific term is defined and used with an arbitrary meaning, the meaning of that term will be described separately. Accordingly, the terms used in this specification should be interpreted based on their actual meaning and the content throughout this specification, rather than merely their names.

[0030] The drawings attached to this specification are intended to facilitate the explanation of the present invention. The shapes depicted in the drawings may be exaggerated as necessary to aid in understanding the present invention, and therefore the present invention is not limited by the drawings.

[0031] In cases where it is determined that a detailed description of known components or functions related to the present invention in this specification may obscure the essence of the invention, such detailed description shall be omitted as necessary. Furthermore, numbers used in the description of this specification (e.g., First, Second, etc.) are merely identification symbols to distinguish one component from another.

[0032] Furthermore, the suffixes "part" and "substance" used for components in the following description are assigned or used interchangeably solely for the ease of drafting the specification, and do not inherently possess distinct meanings or roles.

[0033] That is, the embodiments of the present disclosure are provided to make the present disclosure complete and to inform those skilled in the art of the scope of the present disclosure, and the invention of the present disclosure is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components.

[0034] Terms such as “first” and / or “second” may be used to describe various components, but said components shall not be limited by said terms. For the sole purpose of distinguishing one component from another, for example, without departing from the scope of rights according to the concept of the present disclosure, the first component may be named the second component, and similarly, the second component may be named the first component.

[0035] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. Conversely, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between. Other expressions describing the relationship between components, such as "between" and "exactly between," or "adjacent to" and "directly adjacent to," should be interpreted in the same way.

[0036] In the drawings, each block of the process flow diagrams and combinations of the flow diagrams can be executed by computer program instructions. Since these computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or other programmable data processing equipment create means to perform the functions described in the flow diagram block(s). Since these computer program instructions can also be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement the function in a specific way, the instructions stored in computer-available or computer-readable memory can also produce a manufactured item containing means of instruction to perform the function described in the flow diagram block(s). Since computer program instructions can be loaded onto a computer or other programmable data processing equipment, instructions that execute a computer or other programmable data processing equipment by performing a series of operation steps on the computer or other programmable data processing equipment to create a process executed by the computer may also provide steps for executing the functions described in the flowchart block(s).

[0037] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specified logical function(s). It should also be noted that in some alternative execution examples, the functions mentioned in the blocks may occur out of order. For instance, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may be executed in reverse order according to their corresponding functions.

[0038] As used in this disclosure, the term “unit” refers to a software or hardware component such as a Field Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC). A “unit” performs specific roles but is not limited to software or hardware. A “unit” may be configured to reside in an addressable storage medium or may be configured to run one or more processors. Accordingly, according to some embodiments, a “unit” includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and “units” may be combined into a smaller number of components and “units” or further separated into additional components and “units.” In addition, the components and 'parts' may be implemented to utilize one or more CPUs within the device or secure multimedia card. Furthermore, according to various embodiments of the present disclosure, the 'parts' may include one or more processors.

[0039] The operating principles of the present disclosure will be described in detail below with reference to the attached drawings. In describing the present disclosure below, specific descriptions of related known functions or configurations will be omitted if it is determined that such detailed descriptions would unnecessarily obscure the essence of the present disclosure. Furthermore, the terms described below are defined in consideration of their functions in the present disclosure, and these may vary depending on the intentions or practices of the user or operator. Therefore, their definitions should be based on the content throughout this specification.

[0040] According to one embodiment of the present disclosure, a method of operation of a computing device for controlling a process for processing a battery using at least one robot device may be provided, comprising the steps of: acquiring information about a battery by at least one processor included in the computing device; identifying whether a communication connection is possible with a battery management system (BMS) connected to the battery based on the information about the battery; selecting one of at least two predetermined process sequences based on whether a communication connection is possible; and controlling the at least one robot device to perform a battery processing process according to the selected process sequence.

[0041] If communication connection with a battery management system is possible, a first process sequence is selected, and the first process sequence may include a battery diagnostic process.

[0042] If communication connection with the battery management system is not possible, a second process sequence is selected, and the second process sequence may include a battery cover disassembly process.

[0043] The second process sequence may further include a battery diagnostic process set to be performed after the battery cover disassembly process described above.

[0044] If a communication connection is possible, the method may further include the step of connecting at least one connector included in the diagnostic device to at least one port connected to the battery management system using the at least one robot device.

[0045] The method may further include the step of diagnosing the battery using a diagnostic device and the step of calculating the diagnostic grade of the battery according to the diagnostic result.

[0046] The method may further include the step of determining a target slot on the storage to store the battery according to the diagnostic grade.

[0047] The step of identifying whether communication is possible may include the step of acquiring sensing data associated with the battery by the at least one processor and the step of identifying at least one port connected to the battery management system based on the sensing data.

[0048] The step of identifying whether communication is possible may include the step of acquiring sensing data associated with the battery by the at least one processor, the step of identifying the degree of damage of at least one port corresponding to the battery management system based on the sensing data, and the step of confirming whether physical connection to the at least one port is possible based on the identified degree of damage.

[0049] The step of identifying whether communication is possible may include the step of identifying whether the battery management system is authorized based on information about the battery by the at least one processor.

[0051] FIG. 1 is a drawing illustrating an example of implementation of a battery processing automation system (1) according to various embodiments. FIG. 1 (a) is a top-view drawing illustrating an example of implementation of the battery processing automation system (1). FIG. 1 (b) is a panoramic view drawing illustrating an example of implementation of the battery processing automation system (1).

[0052] FIG. 2 is a drawing illustrating systems for implementing a battery processing automation system (1) according to various embodiments.

[0053] Referring to FIGS. 1 and 2, the battery processing automation system (1) may include a plurality of automation systems for processing batteries (physically or chemically). In this case, the battery is composed of one or more modules. One module is composed of a plurality of cells. The waste battery pack may include, but is not limited to, an upper cover, bolts (fastening members), modules, cells, and cooling plates. In the present disclosure, the battery may be expressed as a battery pack, an ESS (Energy storage system), or a waste battery.

[0054] Specifically, referring to FIG. 1, the battery processing automation system (1) may include a plurality of automation facilities for battery processing. Specifically, the system (1) may include, but is not limited to, a battery transfer facility (11), a battery storage facility (12), a battery pre-processing facility (13), a battery dismantling facility (14), and a battery unloading facility (15).

[0055] At this time, multiple facilities may be implemented to perform an automated battery processing process based on multiple automation systems. Specifically, the multiple facilities may be implemented to operate based on the processing results (e.g., calculations, judgments, commands, etc.) of at least one computing device (or electronic device) equipped with an automation system.

[0056] For example, referring to FIG. 2, a computing device (200) including at least one processor may be implemented to control a plurality of automation systems. In this case, the battery processing automation system (1) may include, but is not limited to, a battery transfer system (21) for automatically transferring a battery, a battery storage system (22) for automatically storing a battery, a battery preprocessing system (23) for automatically preprocessing a battery (e.g., diagnosis, charging, discharging, removal of coolant, etc.), and a battery dismantling system (24) for automatically dismantling (or disassembling) a battery.

[0057] Specifically, the computing device (200) can control the battery transfer system (21) to move the battery to a designated location. Additionally, the computing device (200) can control the battery storage system (22) to store or take out the battery in a designated area. Additionally, the computing device (200) can control the battery preprocessing system (23) to preprocess the battery according to a predetermined method. Additionally, the computing device (200) can control the battery dismantling system (24) to dismantle at least one part of the battery.

[0058] Additionally, the computing device (200) can process the dismantled battery in conjunction with the recycling / reuse company system (25). For example, the computing device (200) can be implemented to transport the dismantled battery to a waste battery recycling company so that it can be used for (waste) battery recycling. Additionally, for example, the computing device (200) can be implemented to transport the dismantled battery to a waste battery reuse company so that it can be used for (waste) battery reuse.

[0059] An example of a battery processing process based on a battery processing automation system (1) is as follows.

[0060] For example, referring to FIG. 1(b), a transfer facility (11) in which a battery transfer system is implemented can transfer a battery pack received in a space in which a battery processing automation system (1) is implemented to a storage facility (12) in which a battery storage system is implemented. At this time, batteries transferred from an external company can be unloaded and transferred using a unloading facility (15) and a transfer facility (11). Additionally, the storage facility (12) can store the battery pack in a storage slot determined according to a predetermined standard. Furthermore, at least one of the battery packs stored in the transfer facility (11) and the storage facility (12) can be taken out and transferred to a pre-processing facility (13) in which a battery pre-processing system is implemented. The pre-processing facility (13) can pre-process the battery pack using at least one pre-processing device (e.g., a diagnostic device, a charger, a discharger, etc.). Additionally, the transfer facility (11, e.g., a mobile robot) can transfer the pre-processed battery pack to a dismantling facility (14) in which a battery dismantling system is implemented. The dismantling facility (14) can dismantle the battery pack using at least one dismantling means (e.g., a collaborative robot). The dismantled battery can be stored using a storage system or transported to an external company.

[0061] The automation systems constituting the battery processing automation system (1) can be implemented based on a robot system and a computing device for controlling the robot system.

[0063] FIG. 3 is a drawing for explaining the detailed configuration of a robot system-based automation system according to various embodiments.

[0064] Referring to FIG. 3, the battery processing automation system may include a robot system (320) comprising a plurality of robot devices and at least one computing device (300) for controlling the robot system (320). In this case, the computing device (300) may include a server device, but is not limited thereto.

[0065] The computing device (300) may include a processor (301), memory (302), and communication circuit (303), but is not limited thereto, and may further include configurations of the computing device that are obvious to those skilled in the art.

[0066] The processor (301) may include at least one processor implemented such that at least some parts provide different functions. For example, it may execute software (e.g., a program) to control at least one other component (e.g., a hardware or software component) of a computing device connected to the processor (301) and perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (301) may store instructions or data received from other components in memory (302) (e.g., volatile memory), process the instructions or data stored in the volatile memory, and store the resulting data in non-volatile memory. According to one embodiment, the processor (301) may include a main processor (e.g., a central processing unit or an application processor) or an auxiliary processor (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together with it. For example, if the computing device includes a main processor and an auxiliary processor, the auxiliary processor may be configured to use less power than the main processor or to be specialized for a designated function. An auxiliary processor may be implemented separately from the main processor or as part thereof. The auxiliary processor may control at least some of the functions or states associated with at least one component of the computing device (e.g., communication circuit (303)) on behalf of the main processor while the main processor is in an inactive (e.g., sleep) state, or together with the main processor while the main processor is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., communication circuit (303)).According to one embodiment, an auxiliary processor (e.g., a neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the computing device itself where the artificial intelligence model is executed, or through a separate server. The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, or reverse reinforcement learning, but is not limited to the examples described above. The artificial intelligence model may include a plurality of artificial neural network layers. The artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, a transformer, or a combination of two or more of the above, but is not limited to the examples described above. In addition to the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially. Meanwhile, the operation of the computing device described below may be understood as the operation of the processor (301).

[0067] Memory (302) may store various data output by at least one component of a computing device (e.g., a processor (301)). The data may include, for example, software (e.g., a program) and input or output data for related instructions. Memory (302) may include volatile memory or non-volatile memory. Memory (302) may be implemented to store an operating system, middleware or application, and / or the aforementioned artificial intelligence model.

[0068] The communication circuit (303) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between a computing device and an external electronic device (e.g., a measuring device or a user device), and the performance of communication through the established communication channel. The communication circuit (303) may include one or more communication processors (e.g., a communication chip) that operate independently of the processor (301) (e.g., a program processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication circuit (303) may include a wireless communication module (e.g., a cellular communication module, a short-range wireless communication module, or a GNSS (global navigation satellite system) communication module) or a wired communication module (e.g., a LAN (local area network) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external computing device (e.g., mobile device, wearable device, or server device) through a first network (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN)). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module can identify or authenticate a computing device within a communication network, such as the first network or the second network, using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in a subscriber identification module.The wireless communication module can support 5G networks following 4G networks and next-generation communication technologies, such as new radio access technology (NR access technology). NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low-latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module can support high-frequency bands (e.g., mmWave band) to achieve high data transmission rates, for example. The wireless communication module can support various technologies to secure performance in high-frequency bands, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antenna, analog beamforming, or large-scale antenna. According to one embodiment, the wireless communication module may support a Peak data rate (e.g., 20 Gbps or higher) for realizing eMBB, loss coverage (e.g., 164 dB or lower) for realizing mMTC, or U-plane latency (e.g., downlink (DL) and uplink (UL) each of 0.5 ms or lower, or round trip 1 ms or lower) for realizing URLLC.

[0069] The robot system (320) may include a plurality of robot devices (310a, 310b, 310c, 310d).

[0070] At this time, the plurality of robot devices (310a, 310b, 310c, 310d) may include various types of robots. For example, the plurality of robot devices (310a, 310b, 310c, 310d) may include, but are not limited to, collaborative robots, industrial robots, mobile robots with autonomous driving functions, automatic transfer robots, or autonomous forklifts.

[0071] For example, the first robot device (310a) may include, but is not limited to, at least one robot arm (311), at least one drive device (312) for providing power to the robot arm, at least one sensor (313), at least one processor (314) and memory (315). In this case, the at least one processor (314) may be a sub-processor for communicating with a main processor to control the components of the robot device, but is not limited thereto.

[0072] A robot device may be a device comprising one or more actuators and one or more parts. In this case, an actuator may refer to various devices that convert electrical energy into kinetic energy based on a control signal. For example, the actuator may be any one of a direct current (DC) servo motor, an alternating current (AC) servo motor, a stepping motor, a linear motor, a hydraulic cylinder, a hydraulic motor, a pneumatic cylinder, and a pneumatic motor. However, this is exemplary and the scope of the invention is not limited thereto.

[0073] The robot system (320) may further include at least one end effector for performing a specific task. In this case, the at least one end effector may be implemented to be connected to the end of a robot device (or a robot arm included in the robot device) to perform a specific task.

[0074] For example, end effectors may include, but are not limited to, grippers used to pick up or move objects (e.g., mechanical grippers that pick up objects using fixed fingers and vacuum (or pneumatic) grippers that pick up objects using vacuum), welding torches designed to enable a robot to perform welding operations, paint spray guns that enable a robot to automatically perform painting or coating operations, cameras and sensors used for quality inspection, precision measurement, or visual monitoring of the work environment, tool holders equipped with drills, screwdrivers, or other tools that enable a robot to perform various assembly operations, electrical and electronic component manipulators used to handle or assemble precision electronic components, or scrapers used to remove material attached to a surface.

[0075] Additionally, the battery processing automation system may further include at least one sensor (330) capable of communicating with a computing device (300), a worker terminal (340), or at least one processing device (350), etc.

[0076] At this time, at least one sensor (330) may include, but is not limited to, a camera, radar sensor, ultrasonic sensor, depth measuring sensor (e.g., lidar, TOF camera, etc.), motion detection sensor, temperature detection sensor, or thermal imaging camera for obtaining visual information about the workspace.

[0077] The worker terminal (340) may be an electronic device capable of communication connection to the computing device (300). For example, the worker terminal (340) may be a mobile device such as a smartphone or tablet PC, or a display device connected to an automation system, but is not limited thereto. In this case, the computing device (300) may control the automation system based on commands received from the worker terminal (340).

[0078] At least one processing device (350) may include a device for physically or chemically processing the battery. For example, at least one processing device (350) may include, but is not limited to, a diagnostic device for diagnosing the performance of the battery, a charger for charging the battery, or a discharger for discharging the battery.

[0079] The battery processing automation system may further include at least one light for controlling the brightness inside the working cell, a foreign matter suction device for sucking in foreign matter such as dust in the working space, and an air circulation device for controlling the temperature and humidity in the working space.

[0080] The processor (301) can train the robot system (320) based on at least one predetermined robot artificial intelligence learning method.

[0081] For example, the processor (301) can train the robot artificial intelligence based on the imitation learning method. In this case, imitation learning is a method in which the robot learns by observing and imitating the behavior of a human or another robot (e.g., a human demonstration). Through imitation learning, the processor (301) can train the robot to quickly acquire the skills necessary to perform complex tasks.

[0082] As another example, the processor (301) can train the robot artificial intelligence based on the reinforcement learning method. In this case, reinforcement learning is a method that allows the robot to learn through trial and error, in which the robot selects an action in a given environment and learns based on the reward obtained from the result. The processor (301) can train the robot to find the optimal sequence of actions to achieve a specific goal through reinforcement learning.

[0083] As another example, the processor (301) can train the robot artificial intelligence based on the Inverse Reinforcement Learning method. In this case, Inverse Reinforcement Learning is a learning method that infers a reward function from observed behavior. That is, it is a method of training the robot not to learn what actions it must take to achieve a certain goal, but to understand what the observed behavior is intended to achieve a certain goal. Through Inverse Reinforcement Learning, the processor (301) can train the robot to observe human behavior and understand its purpose so that it can achieve similar goals in similar situations.

[0085] Below, a robot system-based automation system for providing battery processing automation is described in detail.

[0087] [Battery Transfer and Storage System]

[0088] FIG. 4 is a diagram illustrating the configuration of a battery transfer and storage system according to various embodiments.

[0089] Referring to FIG. 4, the battery transfer and storage system may include at least one transfer device (410), storage (420) including a plurality of slots, memory, and a computing device (400) including at least one processor.

[0090] At least one transfer device (410) may include a first transfer device (410a, e.g., an unmanned forklift) for loading or taking out a battery into a storage (420) and a second transfer device (410b, e.g., a mobile robot or conveyor) for transferring the battery to a workspace. At least one transfer device (410) may be equipped with an autonomous driving function and implemented to transfer the battery without manual operation by a person.

[0091] The storage (420) may include a plurality of slots for storing batteries. The storage (420) may further include at least one sensing device for monitoring batteries stored in the slots. Specifically, the storage (420) may include at least one temperature sensor for detecting a fire risk of the batteries.

[0092] Additionally, the storage (420) may further include a transfer module (not shown) for moving the battery within the storage (420). In this case, the transfer module may be implemented to move the battery between slots included in the storage. For example, the transfer module may include at least one rail and a moving module for moving the battery along the rail, but is not limited thereto.

[0093] The battery (10) can be loaded onto a pallet (not shown) and transported or stored. At least one transport device (410) can transport the battery loaded onto the pallet, and the storage (420) can be implemented to accommodate the battery loaded onto the pallet in at least one slot.

[0094] At least one processor included in the computing device (400) may be configured to perform the task of transporting the battery and storing it in storage based on a control signal instructing the storage of the battery. Additionally, at least one processor may be configured to perform the task of transporting the battery and taking it out of storage based on a control signal instructing the removal of the battery.

[0095] The memory included in the computing device (400) can store identification information including a plurality of identifiers corresponding to each of the plurality of slots of storage. Specifically, the computing device (400) can store an identifier corresponding to the slot that reflects information regarding whether a battery is accepted and / or information about the accepted battery.

[0096] At least one processor can set conditions for batteries to be accommodated in a plurality of slots included in the storage (420). In this case, at least one processor can control at least one transfer device (410) and the storage (420) to store or retrieve batteries according to the set conditions. In this case, identification information stored in memory may include condition information indicating the conditions of the batteries to be stored in the target slot.

[0097] For example, at least one processor can set battery storage conditions based on whether multiple processing processes to be performed on the battery have been performed. Specifically, at least one processor can determine the slot to be accommodated by identifying the battery based on whether a diagnosis / discharge process or a dismantling process is being performed.

[0098] Additionally, for example, at least one processor may set battery storage conditions based on the processing priority for batteries to be accommodated in multiple slots included in the storage. Specifically, at least one processor may set the processing priority based on the stability or degree of damage of the batteries. That is, the battery storage system may be configured so that batteries with low stability and high degree of damage are quickly removed and processed.

[0099] Additionally, for example, the battery storage system can be implemented to store batteries by separating them according to manufacturer. The battery storage system can be implemented to store batteries by separating them according to type (for recycling or reuse). The battery storage system can determine the slot for storing batteries based on user input.

[0100] In addition, for example, a battery storage system can be implemented to store batteries classified by diagnostic grade.

[0101] The storage (420) may include a plurality of zones partitioned according to predetermined battery storage conditions. In this case, the zone may include at least one slot.

[0102] For example, the first section (421) of the storage may be configured to accommodate a battery that satisfies a first condition (e.g., the performance of a diagnostic and discharge process). In this case, at least one processor may be configured to accommodate a battery that satisfies the first condition in the nearest slot among the plurality of slots included in the first section (421), but is not limited thereto.

[0104] FIG. 5 is a drawing illustrating a method for a battery transfer and storage system to store a battery according to various embodiments.

[0105] Referring to FIG. 5, at least one processor included in the electronic device (or computing device) can receive a first control signal instructing the storage of the first battery (S510).

[0106] At least one processor may be configured to perform the task of transporting the first battery and storing it in storage (S520). The task of storing the battery may further include the following detailed operations.

[0107] Specifically, at least one processor can acquire battery information corresponding to the first battery (S521). At this time, the battery information may be information indicating the state or performance of the battery. For example, the battery information may include, but is not limited to, battery profile information indicating the manufacturer, type, specifications, etc. of the battery; battery diagnostic information indicating the result of a battery diagnosis; battery charging information indicating the battery charge state (e.g., State of Charge (SOC)); battery stability information indicating the stability or risk level of the battery; or battery performance information indicating the performance of the battery (e.g., State of Health (SOH)).

[0108] At least one processor can obtain battery information based on sensing data associated with a battery obtained from at least one sensor. Specifically, at least one processor can obtain battery information by analyzing the sensing data and determining the state of the battery.

[0109] In addition, at least one processor can obtain battery information by receiving battery information that is stored in advance on a server. Specifically, the server can receive and store battery information in advance from an external source (e.g., a battery supplier server, etc.).

[0110] Additionally, at least one processor may determine at least one slot among a plurality of slots as a target slot based on at least one of identification information or battery information (S523). A specific method for at least one processor to determine a target slot is described in detail in the description of FIGS. 6 to 8.

[0111] According to one embodiment, at least one processor can determine a target slot based on whether a plurality of slots included in the storage accommodate a battery.

[0112] FIG. 6 is a diagram illustrating an example of a method for at least one processor to determine a target slot according to various embodiments.

[0113] Referring to FIG. 6, at least one processor can identify multiple identifiers corresponding to multiple slots stored in identification information (S601).

[0114] Additionally, at least one processor can check whether a battery is accepted for each of the multiple slots based on multiple identifiers (S603). Specifically, at least one processor can check whether a battery is accepted by checking battery acceptance information recorded in the multiple identifiers.

[0115] In addition, at least one processor can select at least one slot among multiple slots that does not accept a battery based on whether a battery is accepted (S605).

[0116] Additionally, at least one processor may determine at least one selected slot as the target slot (S607). In this case, at least one processor may determine the slot with the highest priority among the at least one slot as the target slot. Specifically, at least one processor may determine the slot with the shortest transfer distance among the at least one slot as the target slot. Alternatively, at least one processor may determine the slot corresponding to the battery storage condition among the at least one slot as the target slot. Alternatively, at least one processor may arbitrarily select one of the at least one slot to determine as the target slot.

[0117] According to one embodiment, at least one processor can determine a target slot based on the battery storage conditions of the slot.

[0118] FIG. 7 is a drawing illustrating another example of a method for at least one processor to determine a target slot according to various embodiments.

[0119] Referring to FIG. 7, at least one processor can check condition information based on identification information (S701). Here, the condition information represents a condition for a battery set to be stored in a slot, and is a concept corresponding to the battery storage condition described in the description of FIG. 4.

[0120] Additionally, at least one processor can compare the state and condition information of the battery included in the battery information (S703). Specifically, at least one processor can compare the degree of matching between the state (or performance) of the battery identified based on the battery information and the condition information stored for each of the plurality of slots.

[0121] Additionally, at least one processor can determine at least one slot corresponding to matching condition information as a target slot (S705). Specifically, at least one processor can specify a condition associated with the battery state based on battery information, and can determine at least one slot to which condition information corresponding to the specified condition is assigned as a target slot.

[0122] According to one embodiment, at least one processor can determine a target slot to store the battery based on the safety state of the battery.

[0123] FIG. 8 is a drawing illustrating another example of a method for at least one processor to determine a target slot according to various embodiments.

[0124] Referring to FIG. 8, at least one processor can acquire a battery profile (S801). Additionally, at least one processor can acquire sensing data from at least one sensor (S803). At this time, at least one sensor may be a machine vision sensor, but is not limited thereto.

[0125] Additionally, at least one processor can determine battery stability by determining the level of damage based on sensing data (S805). Specifically, at least one processor can extract external features of the battery based on sensing data and determine the state of the battery based on the extracted features. For example, at least one processor can obtain information regarding whether the battery is damaged and the location of the battery damage by analyzing external information of the battery based on a machine vision sensor.

[0126] Additionally, at least one processor can assign a priority to the battery (S807). Here, the priority refers to the priority for which processing steps (e.g., diagnosis, discharge, or dismantling, etc.) must be performed after storage, and may correspond to the priority for battery removal.

[0127] Specifically, at least one processor may assign priorities based on at least one of a battery profile or battery stability. At least one processor may assign priorities based on battery information identified based on the battery profile. Additionally, at least one processor may assign priorities by determining the urgency of battery processing based on battery stability.

[0128] Additionally, at least one processor may designate a battery with low stability as a primary monitoring target. In this case, at least one processor may store the target battery in at least one slot equipped with monitoring functions (e.g., fire detection and emergency ejection functions).

[0129] Additionally, at least one processor can determine at least one slot corresponding to a determined priority as a target slot (S809). Specifically, at least one processor can determine the target slot by selecting at least one slot corresponding to a priority assigned to the target battery.

[0130] According to an embodiment, by providing a battery storage solution based on battery information and slot identification information, the computational efficiency of the processor is enhanced, thereby enabling the efficient automation of the task of storing a large number of batteries.

[0131] Additionally, referring again to FIG. 5, at least one processor can transport the first battery to accommodate it in a target slot (S525). Specifically, at least one processor can transport the battery to a predetermined location in storage using at least one transport device. Additionally, the battery can be transported from a predetermined location to a determined target slot using a transport module included in the storage. Additionally, not limited thereto, at least one processor can transport the battery to a location corresponding to the target slot in storage using at least one transport device. In this case, the battery can be accommodated into the slot by the at least one transport device transporting a pallet loaded with the battery into the slot.

[0132] Additionally, at least one processor may perform an operation to update identification information by associating battery information with the identifier of the target slot (S527). Specifically, at least one processor may update identification information by recording information that a battery is stored in the target slot in the identifier corresponding to the target slot. Additionally, at least one processor may update identification information by recording battery information in the identifier. For example, at least one processor may record battery acceptance information, battery profile, battery status information, or battery performance information, etc., in the identifier of the target slot, but is not limited thereto.

[0134] FIG. 9 is a drawing illustrating the operation of a battery transport and storage system to take out a stored battery according to various embodiments.

[0135] Referring to FIG. 9, at least one processor included in the battery transfer and storage system can receive a second control signal instructing the removal of the first battery (S910).

[0136] Additionally, at least one processor can perform the operation of removing the first battery from storage (S920). At this time, the above-described removal operation may further include the following detailed operations.

[0137] Specifically, at least one processor can identify a first slot in which a first battery is accommodated (S921). At least one processor can identify a first slot in which a first battery is accommodated based on identification information including a plurality of identifiers that record information regarding battery storage for a plurality of slots.

[0138] Additionally, at least one processor may transfer the first battery to remove the battery from the first slot (S923). In this case, at least one processor may be implemented to remove the first battery by removing the pallet on which the battery is placed. Alternatively, at least one processor may be implemented to remove the first battery by removing the first slot.

[0139] At this time, a battery removal priority may be set, and at least one processor may control the removal of batteries according to said priority. Additionally, the battery removal priority may be assigned based on information about the battery. It may be implemented so that batteries with a higher priority for the battery processing process (diagnosis, discharge, or dismantling, etc.) are assigned a higher priority.

[0141] FIG. 10 is a diagram illustrating a method for a battery storage system to monitor a stored battery according to various embodiments.

[0142] Referring to FIG. 10, at least one processor connected to a battery storage system can store a battery in at least one slot included in the storage (S1010).

[0143] Additionally, at least one processor can monitor multiple slots based on sensor values ​​obtained by a predetermined method (S1020). At this time, at least one processor may be configured to obtain sensor values ​​from at least one sensor installed in the storage. Specifically, at least one processor may obtain sensor values ​​from at least one sensor implemented to sense the interior of multiple slots included in the storage. At this time, at least one sensor may be placed to determine the stability of a battery accommodated in a slot. For example, at least one sensor may include, but is not limited to, a thermal imaging camera, a temperature sensor, a smoke sensor, or a heat detection sensor.

[0144] Additionally, at least one processor may determine that at least one of the plurality of slots is in a dangerous state based on the monitoring results (S1030). For example, at least one processor may determine that the battery is in a dangerous state if the temperature of the battery rises above a threshold. Additionally, for example, at least one processor may determine that the battery is in a dangerous state if a fire is detected in the battery.

[0145] Additionally, at least one processor may remove at least one slot determined to be in a dangerous state to the outside (S1040). In this case, the slot accommodating the battery may be equipped with a fire extinguishing device and may be implemented to extinguish a fire occurring in the battery by operating the fire extinguishing device, but is not limited thereto. Alternatively, at least one processor may be implemented to remove the battery determined to be in a dangerous state from the slot. In this case, at least one processor may use a transfer device to transfer the battery to an emergency space equipped with fire extinguishing equipment.

[0146] According to the embodiment, the battery storage solution is implemented to continuously monitor the state of the battery, thereby enabling the implementation of an automated system with ensured stability.

[0148] FIG. 11 is a drawing illustrating another embodiment of a battery transfer and storage system for storing batteries according to various embodiments.

[0149] Referring to FIG. 11, at least one processor connected to a battery transfer and storage system can acquire a slot image in which at least some of the multiple slots are captured based on sensing data acquired from a sensor (S1110).

[0150] In addition, at least one processor can acquire a battery image in which at least some of the incoming batteries are photographed (S1120).

[0151] In addition, at least one processor can determine at least one empty slot by analyzing a slot image using an artificial intelligence model (S1130). At this time, the artificial intelligence model may include a deep learning model for image analysis, such as a Convolutional Neural Network (CNN).

[0152] In addition, at least one processor can determine battery information by analyzing a battery image using an artificial intelligence model (S1140). At this time, the artificial intelligence model may include a deep learning model for image analysis, such as a Convolutional Neural Network (CNN).

[0153] Additionally, at least one processor may determine one of at least one empty slot as the target slot based on battery information (S1150). At this time, the method for determining the target slot has been described above and will be omitted.

[0154] In addition, at least one processor can use a transfer device to transfer the battery to a position corresponding to the target slot (S1150).

[0156] [Battery Pre-processing System]

[0157] FIG. 12 is a diagram illustrating the configuration of a battery pretreatment system according to various embodiments.

[0158] Referring to FIG. 12, the battery preprocessing system may include at least one device (1210), at least one robot (1220), and a computing device (1200) including memory and at least one processor.

[0159] At least one device (1210) may include at least one of a charging device for charging a battery, a discharging device for discharging a battery, or a diagnostic device for diagnosing a battery.

[0160] The computing device (1200) can preprocess the battery using at least one device (1210).

[0161] More specifically, the computing device (1200) can diagnose the battery using a battery diagnostic device.

[0162] The computing device (1200) can obtain state of charge (SOC) information by measuring the amount of charge remaining in the battery using a battery diagnostic device.

[0163] Additionally, the computing device (1200) can obtain state of health (SOH) information indicating the battery's lifespan and health indicators by using a battery diagnostic device.

[0164] Additionally, the computing device (1200) can diagnose whether the battery is operating within normal parameters by measuring the voltage and current of the battery using a battery diagnostic device.

[0165] Additionally, the computing device (1200) can diagnose the degree of aging of the battery by measuring the internal resistance using a battery diagnostic device. Here, the internal resistance of the battery increases as the battery ages.

[0166] Additionally, the computing device (1200) can measure the temperature data of the battery using a battery diagnostic device. At this time, the past temperature data can be used to diagnose the past state of the battery's performance and lifespan.

[0167] Additionally, the computing device (1200) can determine the remaining battery life by obtaining the charging and discharging cycles of the battery using a battery diagnostic device.

[0168] Additionally, the computing device (1200) can provide information on internal chemical and electrical processes of the battery by applying electrochemical impedance spectroscopy (EIS) using a battery diagnostic device. Through this, the computing device (1200) can identify performance degradation patterns or defects of the battery.

[0169] Additionally, the computing device (1200) can obtain information about the actual capacity of the battery by fully charging and discharging the battery using a battery charging device and a battery discharging device. At this time, the computing device (1200) can check whether there is performance degradation by comparing the measured actual capacity with the rated capacity of the battery.

[0170] Additionally, the computing device (1200) can measure the balance status of the cells constituting the battery pack using a battery diagnostic device. Specifically, the computing device (1200) can check whether the voltage of individual cells is balanced.

[0171] Additionally, the computing device (1200) can charge a battery module (e.g., a battery that supplies power to an electric vehicle) using a battery charging device.

[0172] The computing device (1200) can use at least one robot (1220) to connect or disconnect at least one device (1210) from the battery (10). Specifically, the at least one robot (1220) can operate to connect or disconnect at least one connection part (e.g., a connector, etc.) included in at least one device (1210) by applying physical force to at least one connection part to the battery (10).

[0173] Detailed information on how to link the battery and the processing unit using a robotic device is explained below.

[0175] FIG. 13 is a drawing illustrating an example of a method of operation of a robot system for controlling the operation of a robot device according to various embodiments.

[0176] Referring to FIG. 13, the robot system can connect at least one connector included in at least one processing unit to a battery by controlling the operation of the robot device based on a control algorithm performed by at least one processor.

[0177] Specifically, at least one processor included in the robot system can acquire sensing data associated with the battery from at least one sensor (S1301).

[0178] In step S1301, sequentially or independently, at least one robot device may perform the operation of grasping at least one connector included in at least one processing device using an end effector (S1311).

[0179] Additionally, at least one processor can set a location corresponding to at least one port connected to a battery as a target location based on sensing data (S1302). Specifically, at least one processor can extract feature values ​​from the sensing data and determine the target location by specifying at least a portion of the area having feature values ​​corresponding to a predetermined condition. In this case, at least one processor may use at least one artificial intelligence model (e.g., CNN, etc.) for processing the sensing data, but is not limited thereto.

[0180] At least one processor can set a target location based on predetermined conditions. Specifically, at least one processor can set a target location based on whether communication is possible through at least one port. At least one processor can identify multiple ports based on sensing data and determine at least one port among the identified multiple ports that is determined to be capable of communication connection with a processing device. At this time, at least one processor can set a location corresponding to the determined at least one port as the target location.

[0181] In addition, at least one processor can control an alarm to operate if a communication connection through at least one port identified based on sensing data is impossible.

[0182] Additionally, optionally, at least one processor may acquire battery information and determine operation parameters associated with the operation of at least one processing device based on the battery information. For example, at least one processor may set at least one operation parameter (e.g., voltage or current, etc.) for a battery diagnostic device to diagnose based on the battery state information, but is not limited thereto. Additionally, for example, at least one processor may set at least one operation parameter (e.g., charging time, charging speed, etc.) for a battery charging device to charge based on the battery charge state information, but is not limited thereto.

[0184] At least one processor can control the operation of the robot device by generating a trajectory for controlling the robot device.

[0185] Specifically, at least one processor can generate a trajectory by determining dynamic parameters (e.g., velocity, acceleration, force, torque, etc.) at multiple points until at least a part of the robot device (e.g., multiple joints included in the robot device) reaches a specific destination.

[0186] FIG. 14 is a diagram illustrating a method for a processor to control a robot device based on an artificial intelligence engine according to various embodiments.

[0187] At least one processor can control a robot device by training an artificial intelligence engine (or model) to perform a specific task. In this case, the specific method for training the robot device has been previously described and will therefore be omitted.

[0188] Referring to FIG. 14, at least one processor can generate a trajectory of a robot device based on sensing data using an artificial intelligence engine. Specifically, at least one processor can acquire input data based on sensing data and input the input data to the artificial intelligence engine. In this case, at least one processor can acquire output data from at least one layer included in the artificial intelligence model. Specifically, at least one processor can acquire dynamic parameters (e.g., velocity, acceleration, force, torque, etc.) corresponding to multiple points where at least one robot device is heading toward a target position. That is, the trajectory can be composed of a set of dynamic parameters, and the set of dynamic parameters can be determined differently depending on the purpose of operation of the robot device.

[0189] At least one processor can generate a trajectory for at least one robot device to operate based on the dynamic parameters.

[0190] In addition, at least one processor can input the generated trajectory to a motion controller of at least one robot device, and the motion controller can drive the robot device based on the trajectory.

[0191] In addition, but not limited to this, at least one processor can use an artificial intelligence model to obtain dynamic parameters for the end effector to perform a specific action (e.g., screw action, gripping action, etc.).

[0192] According to an embodiment, at least one processor learns and utilizes an artificial intelligence engine to control the trajectory or operation of at least one robot device or end effector, thereby enabling the task to be achieved with minimal computational cost.

[0193] Referring again to FIG. 13, at least one processor can determine a first trajectory for the end effector to reach a first position corresponding to a target position (S1303). In addition, in this case, at least one robot device can perform an operation to move the end effector to the first position based on the first trajectory (S1312).

[0194] Specifically, at least one processor can determine a first trajectory based on the physical relationship between at least one robot device (or an end effector included in the robot device) and a target location based on sensing data.

[0195] At least one processor can determine at least one of the position coordinates of a target position, a direction from at least one robot device (or an end effector included in the robot device), and a distance from the robot device (or an end effector included in the robot device) based on sensing data. In this case, at least one processor can determine a first trajectory based on at least one of the position coordinates of a target position, a direction from at least one robot device (or an end effector included in the robot device), and a distance from the robot device (or an end effector included in the robot device).

[0196] At this time, the first position may be a position spaced apart from the target position by a predetermined distance. At least one processor may be trained to position an end effector at the first position spaced apart from the target position by a predetermined distance. For example, at least one processor may measure the distance between the end effector and the target position using at least one sensor for distance measurement, and determine the first position based on the measured distance.

[0197] Additionally, at least one processor can determine a second trajectory for connecting at least one connector grasped by an end effector to at least one port (S1304). In addition, in this case, at least one robot device can perform the operation of connecting at least one connector to at least one port based on the second trajectory (S1313).

[0198] Specifically, at least one processor can train an artificial intelligence model such that at least a portion of at least one connector (e.g., a protrusion for connection) is inserted into at least a portion of at least one port (e.g., a receiving portion for connection). At least one processor can use the trained artificial intelligence model to control at least one robotic device to provide a force to connect at least one connector to at least one port.

[0199] At least one processor may determine that the connection operation is complete if at least one connector does not move. At least one processor may determine that the connection between the connector and the port is complete if at least one connector is no longer inserted into the port even after applying force to at least one connector along a second trajectory. Additionally, at least one processor may determine that the connection operation is complete if at least one connector does not move for more than a predetermined amount of time.

[0200] In addition, when processing by the processing device is completed, at least one processor can control the disconnection of the processing device and the battery. Specifically, at least one processor can control at least one robot device by determining at least one trajectory for performing an operation of gripping at least one connector using an end effector and disconnecting at least one connector connected to at least one port.

[0202] A battery management system (BMS) may be an electronic system connected to the battery to monitor the battery, and the battery management system may be built into the battery or be a separate device connected to the battery.

[0203] The battery processing system can obtain monitoring information about the battery from the battery management system through a communication connection with the battery management system. In other words, the battery can be diagnosed by obtaining monitoring information about the battery stored in the battery management system through a communication connection with the battery management system.

[0204] Accordingly, if a communication connection with a battery management system is possible, the battery processing system can obtain diagnostic information about the battery by performing a battery diagnostic process.

[0205] However, depending on the battery, there may be batteries that do not include a battery management system, or communication connection with the battery management system may be impossible (e.g., if not authorized by the manufacturer, if the port is damaged, etc.).

[0206] In this case, the battery processing system needs to diagnose the battery by individually connecting diagnostic devices to multiple ports to diagnose various battery parameters (e.g., SOC, SOH, etc.). However, the multiple ports for battery diagnosis are located inside the battery and cannot be accessed without removing the battery cover.

[0207] Accordingly, if communication with the battery management system is not possible, the battery processing system needs to perform a battery diagnostic process by disassembling the battery cover and connecting to at least one port.

[0208] The battery processing system can select the currently required battery processing process by determining whether a communication connection with the battery management system is possible, and accordingly, the efficiency of the battery processing automation process can be increased.

[0210] FIG. 15 is a diagram illustrating a method in which a battery processing system determines a process sequence based on information about a battery, according to various embodiments.

[0211] Referring to FIG. 15, at least one processor included in the battery processing system can obtain information about the battery (S1501). At this time, the information about the battery may include battery status information associated with the battery, battery diagnostic information, BMS (Battery Management System) information, battery charging information, or battery profile (e.g., manufacturing information, etc.).

[0212] At least one processor can identify whether communication connection with a battery management system (BMS) connected to the battery is possible based on information about the battery (S1503).

[0213] At least one processor can identify whether communication with the battery management system is possible based on a predetermined method.

[0214] For example, at least one processor can determine whether communication is possible based on whether at least one port for connecting to a battery management system is identified. Specifically, at least one processor can acquire associated sensing data from a battery and identify at least one port for connecting to a battery management system based on the sensing data. In this case, if at least one processor identifies at least one port, it can determine that communication with the battery management system is possible.

[0215] Additionally, at least one processor can identify whether physical connection to at least one port is possible based on sensing data. Specifically, at least one processor can identify the degree of damage to at least one port corresponding to a battery management system based on sensing data, and can identify whether physical connection is possible based on the identified degree of damage. For example, if at least one processor determines that the degree of damage to at least one port is greater than a predetermined standard, it may determine that communication connection with the battery management system is impossible.

[0216] As another example, at least one processor can identify whether communication is possible by identifying whether the battery profile is authorized by the battery management system. The battery processing system may store information in advance about batteries capable of communicating with the battery management system. In this case, at least one processor can determine whether a battery is capable of communicating with the battery management system by comparing the previously stored information with the battery profile.

[0217] At least one processor can select one of at least two predetermined process sequences based on whether a communication connection is possible (S1505). At least one processor can control at least one robot device to perform a battery processing process according to the selected process sequence (S1507).

[0219] FIG. 16 is a diagram illustrating an example of a process sequence based on information about a battery according to various embodiments.

[0220] Referring to FIG. 16, at least one processor included in the battery processing system can determine the order of battery processing processes according to operation S1507.

[0221] If communication connection with a battery management system is possible, at least one processor can process the battery according to the first process sequence. Specifically, at least one processor can be configured to perform a battery diagnostic process (S1601).

[0222] If communication with the battery management system is not possible, at least one processor can process the battery according to the second process sequence. Specifically, at least one processor may be configured to perform a battery diagnostic process after performing a disassembly process of the first component of the battery (S1603). Here, the first component may be the upper cover of the battery, but is not limited thereto.

[0224] FIGS. 17 to 19 illustrate a method for a battery processing system to control a robot device to connect and disconnect a battery diagnostic device and a battery, according to various embodiments.

[0225] Referring to FIGS. 17 to 19, at least one processor included in the battery processing system can acquire battery information (S1701).

[0226] In step S1701, sequentially or independently, at least one robot device may perform the operation of grasping at least one connector included in the diagnostic device using an end effector (S1711).

[0227] At least one processor can determine diagnostic parameters for diagnosing the battery based on battery information (S1702). In this case, the diagnostic device can set a diagnostic mode based on the determined diagnostic parameters (S1721).

[0228] Specifically, at least one processor can determine diagnostic parameters suitable for diagnosing a battery based on battery information. For example, at least one processor can determine a voltage or current, etc. suitable for battery diagnosis and set diagnostic parameters of a diagnostic device based thereon.

[0229] In addition, the processor included in the diagnostic device can set a diagnostic mode based on diagnostic parameters. For example, the diagnostic device can set a diagnostic mode by selecting at least one of diagnostic modes according to the level of diagnosis (basic diagnosis, detailed diagnosis, precise diagnosis, etc.). In addition, for example, the diagnostic device can set a diagnostic mode by selecting at least one of diagnostic modes according to the diagnostic stage (e.g., initial inspection, subsequent inspection, etc.).

[0230] At least one processor can set a location corresponding to at least one port connected to the battery as a first target location based on sensing data (S1703). At this time, at least one port may be placed on at least a part of the battery. At least one port may be placed exposed on the outer surface of the battery. At least one port may be placed on at least a part inside the battery pack. It may be implemented so that at least one port is exposed when the top cover of the battery is removed. Additionally, at least one port may be electrically connected to the battery management system (BMS) of the battery. At least one port may be electrically connected to a configuration capable of diagnosing at least one state of the battery (e.g., charge state, health state, voltage state, current state, etc.).

[0231] At least one processor can determine a first trajectory for the end effector to move to a first position corresponding to a first target position and to connect at least one connector to at least one port (S1704).

[0232] In this case, at least one robot device can perform the operation of moving an end effector based on a first trajectory to connect at least one connector to at least one port (S1712).

[0233] In addition, at least one processor can confirm that the diagnostic device and the battery are properly connected (S1705).

[0234] Specifically, at least one processor can determine the physical connection status between the diagnostic device and the battery. For example, at least one processor can check whether the physical connection status between a connector included in the diagnostic device and a port included in the battery is normal. As a specific example, at least one processor can control at least one robotic device that grasps the connector to apply a predetermined force to the connector in a direction that separates it from the port. In this case, if the connector is not separated from the port, it can be determined that the battery and the diagnostic device are normally connected.

[0235] Additionally, at least one processor can determine whether the diagnostic device and the battery are set to a state suitable for diagnosis. For example, at least one processor can be configured to compare the battery profile and the diagnostic parameters of the diagnostic device, and to proceed with the test if predetermined criteria are satisfied.

[0236] At least one processor can determine a second trajectory for the end effector to move to a predetermined second position (S1706).

[0237] In this case, at least one robot device can perform the operation of the end effector placing at least one connector and moving to a predetermined position based on the second trajectory (S1713).

[0238] In parallel with this, the diagnostic device can perform battery diagnosis based on a determined diagnostic mode (S1722). A processor included in the diagnostic device can inspect various states of the battery, and based on the inspection results, can calculate the battery's SOC, SOH, SOP, or SOB and determine the diagnostic grade.

[0239] Additionally, the diagnostic device can obtain monitoring information by diagnosing the battery (S1723). In this case, the monitoring information may be information obtained by the diagnostic device monitoring the state of the battery. Specifically, a processor included in the diagnostic device can check the safety status of the battery or the presence of an abnormality in the diagnostic device. Specifically, the diagnostic device can monitor the safety status of the battery and the state of the diagnostic device while the diagnosis / inspection is being performed, and can transmit the monitoring information to a battery processing system. For example, the monitoring information may include, but is not limited to, the charge status of the battery, the voltage status of the battery, or the safety status of the battery.

[0240] At least one processor can identify whether an event has occurred based on monitoring information (S1707). At this time, the event may include at least one event that may occur during battery diagnosis. For example, the event may include an event according to a diagnosis step, an event according to a diagnosis function, an event according to a test mode, or an emergency stop event.

[0241] Additionally, at least one processor can control the system based on the generated event (S1708). Specifically, at least one processor can control the system by determining feedback according to the type of generated event.

[0242] For example, if battery instability is detected during the initial inspection, at least one processor can transmit an alarm and control at least one robotic device to perform the action of disconnecting the diagnostic device from the battery.

[0243] Additionally, for example, if a fire hazard is detected, at least one processor can transmit an alarm and control at least one robot device to perform emergency measures. Specifically, at least one processor can control at least one robot device to perform the action of dropping a battery into a water tank.

[0244] After diagnosing the battery according to step S1723, the diagnostic device can complete the execution of the diagnostic operation (S1724). In this case, the diagnostic device can calculate multiple indicators related to the state of the battery (e.g., SOC, SOH, etc.) and calculate a diagnostic grade based on the multiple indicators.

[0245] Additionally, at least one processor can determine a third trajectory for the end effector to move to a third position corresponding to at least a part of at least one connector to grasp at least one connector (S1709). Additionally, at least one robot device can perform the operation of moving the end effector to the third position and grasping at least one connector based on the third trajectory (S1714).

[0246] Additionally, at least one processor can determine a fourth trajectory for separating at least one connector grasped by an end effector from the battery (S1710). In this case, at least one robot device can perform the operation of separating at least one connector from the battery based on the fourth trajectory (S1715).

[0248] According to one embodiment, at least one processor can control the storage of a battery on which a diagnostic process has been performed in a battery storage system. In this case, at least one processor can classify and store the batteries according to the battery diagnostic grade.

[0249] FIG. 20 is a drawing illustrating a method for a battery processing system to store a battery according to a battery diagnostic grade, according to various embodiments.

[0250] Referring to FIG. 20, at least one processor included in the battery processing system can determine the battery diagnostic grade and complete the battery diagnostic (S2001).

[0251] Additionally, at least one processor can transfer a battery to a battery storage using a transfer device (S2003). At this time, the battery storage may include a plurality of slots for receiving and storing batteries.

[0252] Additionally, the battery storage can be implemented to store batteries according to battery diagnostic grades. For example, the battery storage may include multiple zones partitioned by battery diagnostic grade, and a specific zone may be configured to store batteries assigned to a specific diagnostic grade.

[0253] At least one processor can determine a target slot for storing the battery according to a determined diagnostic grade (S2005). Specifically, at least one processor can determine the target slot by selecting at least one slot corresponding to the diagnostic grade of the battery. At this time, the target slot may have identification information assigned to it corresponding to the diagnostic grade of the battery. Additionally, at least one processor can determine the target slot by determining a storage area on the battery storage corresponding to the diagnostic grade of the battery.

[0254] At least one processor can determine a target slot using at least one sensor (e.g., a vision sensor). Specifically, at least one processor can determine a target slot by identifying at least one slot corresponding to the diagnostic grade of the battery to be stored based on sensing data.

[0255] Additionally, at least one processor can determine a target slot based on a communication connection with a battery storage system. Specifically, at least one processor can transmit the diagnostic grade of the battery to be stored to the battery storage system, the battery storage system can determine a target slot corresponding to the grade, and transmit location information of the determined target slot to a transfer device.

[0256] At least one processor controls the transfer device and battery storage to store the battery in a determined target slot (S2007). The specific method for storing the battery in the target slot has been described above and will be omitted.

[0258] FIG. 21 is a diagram illustrating a method for a battery processing system to control a robot device to connect a battery discharge device and a battery according to various embodiments.

[0259] Referring to FIG. 21, at least one processor can set a location corresponding to at least one port connected to a battery as a first target location based on sensing data (S2101).

[0260] In step S2101, sequentially or independently, at least one robot device may perform the operation of grasping at least one connector included in the discharger using an end effector (S2111).

[0261] Additionally, at least one processor can determine a first trajectory for the end effector to move to a first position corresponding to a first target position to connect at least one connector to at least one port (S2102). At this time, at least one port may be placed on at least a part of the battery. At least one port may be placed exposed on the outer surface of the battery. At least one port may be placed on at least a part inside the battery pack. It may be implemented so that at least one port is exposed when the top cover of the battery is removed. Additionally, at least one port may be electrically connected to at least one cell of the battery.

[0262] In this case, at least one robot device can perform the operation of moving an end effector based on a first trajectory to connect at least one connector to at least one port (S2112).

[0263] In addition, at least one processor can confirm that the discharger and the battery are properly connected (S2103).

[0264] The discharger can perform battery discharge (S2121).

[0265] Additionally, at least one processor can control monitoring operations during discharge and connector disconnection operations after discharge (S2104). For example, at least one processor can monitor the battery discharge rate, discharge amount, or remaining discharge time, and when discharge is complete, at least one robot device can disconnect at least one connector from the battery.

[0267] FIG. 22 is a diagram illustrating a method for a battery processing system to control a robot device to connect a battery charging device and a battery according to various embodiments.

[0268] Referring to FIG. 22, at least one processor can set a location corresponding to at least one port connected to a battery as a first target location based on sensing data (S2201).

[0269] In step S2201, sequentially or independently, at least one robot device may perform the operation of grasping at least one connector included in the charger using an end effector (S2211).

[0270] Additionally, at least one processor can determine a first trajectory for the end effector to move to a first position corresponding to a first target position and to connect at least one connector to at least one port (S2202). At this time, at least one port may be placed on at least a part of the battery. At least one port may be placed exposed on the outer surface of the battery. At least one port may be placed on at least a part inside the battery pack. It may be implemented so that at least one port is exposed when the top cover of the battery is removed. Additionally, at least one port may be electrically connected to at least one cell of the battery. Additionally, at least one port may be placed on the outer surface of an electric moving body equipped with the battery.

[0271] In this case, at least one robot device can perform the operation of moving an end effector based on a first trajectory to connect at least one connector to at least one port (S2212).

[0272] In addition, at least one processor can confirm that the charger and the battery are properly connected (S2203).

[0273] The charger can charge the battery (S2221).

[0274] Additionally, at least one processor can control monitoring operations during charging and connector disconnection operations after charging (S2204). For example, at least one processor can monitor the battery charging speed, charging amount, charging cost, or remaining charging time, and when charging is complete, at least one connector can be disconnected from the battery using at least one robotic device.

[0276] According to one embodiment, at least one processor included in a battery processing system can perform a processing process for removing coolant contained in a battery. Specifically, at least one processor can identify at least one passage forming a coolant flow path of the battery based on sensing data. At least one processor can connect a means for removing coolant to the identified at least one passage using at least one robotic device. Additionally, at least one processor can drive a means for removing coolant to remove coolant contained in the battery.

[0277] For example, at least one processor can identify two holes defining a coolant passage based on sensing data associated with a battery. Additionally, at least one processor can connect a pump to the two holes using at least one robotic device. Furthermore, at least one processor can control the pump to drive and remove the coolant contained in the coolant passage.

[0279] According to one embodiment, at least one processor can control the battery dismantling process to be performed on a battery on which a battery pretreatment process has been performed. Specifically, at least one processor can transfer a battery on which a diagnostic process has been performed to a location corresponding to a battery dismantling system.

[0280] Below, a battery dismantling system that automatically dismantles a battery pack using at least one robotic device is described in detail.

[0282] [Battery Disassembly System]

[0283] FIG. 23 is a diagram illustrating the configuration of a battery dismantling system according to various embodiments.

[0284] Referring to FIG. 23, the battery dismantling system may include a computing device (2300), at least one transport device (2310) for loading and moving a battery (10), a battery dismantling area (2320) where a workspace for dismantling the battery is provided, and at least one robot device (2330), etc.

[0285] The computing device (2300) can control the transfer device (2310) or the robot device (2330) to transfer the battery (10) and perform the operation of dismantling at least one component of the battery (10). The computing device (2300) may include, for example, a main server (processor) and at least one sub-processor for controlling the battery dismantling system, but is not limited thereto.

[0286] The battery dismantling area (2320) may include at least one work chamber (2325). In this case, the at least one work chamber (2325) may be a modular space for performing battery dismantling operations. Details regarding the work chamber (2325) are described below (Figs. 48 and 49).

[0287] The computing device (2300) can move the battery (10) using the transfer device (2310).

[0288] At this time, the transfer device (2310) may be a device capable of operation and having a space for loading batteries. For example, the transfer device (2310) may include a mobile robot such as that shown in FIG. 23, and may also include a conveyor (not shown) for transferring batteries.

[0289] Additionally, the computing device (2300) can bring the transfer device (2310) into the battery dismantling area (2320) and position the transfer device (2310) within the work area of ​​the robot device (2330) so that the robot device (2330) can perform the battery (10) dismantling operation.

[0290] For example, the computing device (2300) can position the mobile robot carrying the battery (10) within the work area of ​​the robot device (2330) by bringing it into the battery dismantling area (2320). In this case, the computing device (2300) can control the robot device (2330) to dismantle the battery (10) loaded on the mobile robot.

[0291] Additionally, for example, the computing device (2300) can control a conveyor belt carrying the battery (10) to bring the battery into the battery dismantling area (2320). In this case, the battery (10) can be positioned within the working area of ​​the robot device (2330) by being transported along the conveyor belt, and the robot device (2300) can be controlled to dismantle the transported battery (10).

[0293] FIGS. 24 and 25 are drawings illustrating the operation method of a robot system for disassembling a battery pack according to various embodiments.

[0294] Referring to FIG. 24, at least one processor included in the robot system may receive a control signal instructing the disassembly of a first component of the battery (S2410). At this time, the control signal may be input to the robot system by user input, or may be input from another system (e.g., battery storage system, battery preprocessing system, etc.). Additionally, the first component may include, but is not limited to, an upper cover, a lower cover, a module, or a cell of the battery pack, and may be at least one component coupled to the housing of the battery pack through a fastening member.

[0295] At least one processor can acquire sensing data associated with a battery from at least one sensor (S2402). At this time, the at least one sensor may include, but is not limited to, a vision sensor, a camera sensor, an image sensor, a lidar sensor, or a radar sensor. The at least one sensor may be placed in at least a part of at least one robot device or in at least a part of a work chamber (see reference numeral 2325 in FIG. 23).

[0296] At least one processor can identify the location of a first fastening member for fixing a first part based on sensing data and set it as a first target location (S2403). At this time, the first fastening member may include, but is not limited to, a bolt or welded part for fixing the part. Specifically, at least one processor can determine the first target location by identifying the location coordinates, direction, and distance of the first fastening member.

[0297] At this time, at least one processor can identify the locations of multiple fastening members based on sensing data. At least one processor can select a first fastening member among the multiple fastening members to perform disassembly operations based on predetermined criteria. Specifically, at least one processor can predetermine the disassembly order for the multiple fastening members. Additionally, at least one processor can select a fastening member among the multiple fastening members that is close to at least one robot device. Additionally, at least one processor can select at least two fastening members among the multiple fastening members such that the multiple robot devices do not interfere with each other.

[0298] At least one processor can determine a first trajectory for a first end effector to reach a first position corresponding to a first target position (S2404). At this time, at least one robot device can perform an operation to move the first end effector to the first position based on the first trajectory (S2411). Specifically, at least one processor can generate the first trajectory by inputting sensing data into an artificial intelligence model and obtaining a set of dynamic parameters for reaching the first position from at least one layer included in the artificial intelligence model.

[0299] The first end effector may be a means for disassembling the first fastening member. For example, the first end effector may be an end effector in the form of a screwdriver (e.g., Phillips screwdriver, flathead screwdriver, rotary screwdriver, etc.) for disassembling a bolt, but is not limited thereto.

[0300] At least one processor can control the first end effector to perform a first task of disassembling the first fastening member from the battery based on the first condition (S2405). At this time, at least one robot device can perform the first task of disassembling the first fastening member from the battery by the first end effector (S2412).

[0301] The first condition may be set based on whether at least one robot device is ready to perform a task to disassemble a fastening member. At least one processor may check the relationship (e.g., distance, direction, etc.) between the first end effector and the first fastening member, and if the relationship meets a predetermined standard, it may determine that the task is ready to be performed.

[0302] The first task may include a plurality of unit actions for disassembling the first fastening member. Specifically, the first task may include, but is not limited to, an action of bringing the first end effector to the first fastening member, an action of contacting the first end effector and the first fastening member, an action of driving the first end effector to disassemble the first fastening member, and an action of separating the first fastening member.

[0303] For example, at least one processor can check contact between the first end effector and the first fastening member, and control the first end effector to rotate (e.g., loosen a screw) to disassemble the first fastening member.

[0304] At least one processor can perform multiple first tasks simultaneously or sequentially using multiple robotic devices.

[0305] A specific method for performing a task by controlling multiple robot devices will be described below (Figs. 44 to 47).

[0306] At least one processor can determine whether the first task has been completed. Specifically, at least one processor can determine, based on sensing data, whether all fastening members connecting the first component to the battery housing have been disassembled.

[0307] At least one processor can perform an operation for disassembling the first component when the disassembling of all fastening members is completed.

[0308] At least one processor can identify the removal of a fastening member and transmit a control signal for replacing the end effector. At this time, at least one processor can control at least one robot device to mount a second end effector for separating the cover. In this case, at least one processor can control at least one robot device to release the first end effector and mount the second end effector, or, instead of the first robot device equipped with the first end effector, set the second robot device equipped with the second end effector as the work robot.

[0309] Referring to FIG. 25, at least one processor can set a second target position based on sensing data and determine a second trajectory for the second end effector to reach a second position corresponding to the second target position (S2406). At this time, at least one robot device can perform an operation of moving the second end effector to the second position based on the second trajectory (S2413). Specifically, at least one processor can generate the second trajectory by inputting sensing data into an artificial intelligence model and obtaining a set of dynamic parameters for reaching the second position from at least one layer included in the artificial intelligence model.

[0310] At least one processor can determine the number of robot devices to use based on the specifications (e.g., volume, weight, etc.) of the first part. Specifically, at least one processor can control the disassembly of the first part using one robot device, or control the disassembly of the first part using multiple robot devices.

[0311] Alternatively, at least one processor may disassemble the first part using an industrial robot. Specifically, at least one processor may disassemble the first part using an industrial robot equipped with a plurality of pneumatic grippers, but is not limited thereto.

[0312] A second target location for disassembling a first component can be set as a location suitable for performing the operation of separating the first component. The battery disassembly system can store a location suitable for disassembling a component according to the shape of the battery in advance, and can set a second target location based on the pre-stored location.

[0313] The second end effector may be a means for separating the first part from the battery. For example, the second end effector may be a clamp-type gripper or a pneumatic gripper for grasping and exposing the part, but is not limited thereto.

[0314] At least one processor can control the second end effector to perform a second task of separating the first part based on the second condition (S2407). At this time, at least one robot device can perform the second task of separating the first part by the second end effector (S2414).

[0315] The second condition may be set based on whether at least one robot device is ready to perform a task to separate the first part. At least one processor may check the relationship (e.g., distance, direction, etc.) between the second end effector and the first part, and if the relationship meets a predetermined criterion, it may determine that the task is ready to be performed.

[0316] In addition, the second condition may be established based on whether the removal of at least one fastening member has been completed.

[0317] The second task may include a plurality of unit actions for separating the first component. Specifically, the second task may include, but is not limited to, an action of approaching the first component with a second end effector, an action of driving the second end effector to grasp the first component, and an action of separating the first component.

[0318] For example, at least one processor can bring the second end effector into the space between the first component and the battery housing and drive the second end effector to perform the action of grasping the first component. Additionally, at least one processor can perform the action of separating the first component by moving the robot device.

[0319] Additionally, at least one processor may perform a second task using multiple robot devices. In this case, at least one processor may set multiple target locations and position multiple robot devices at each of the multiple target locations. Additionally, the operation of separating the first component may be performed by simultaneously operating multiple robot devices.

[0320] Additionally, at least one processor can control at least one robotic device to perform at least one task for insulation at a predetermined location of the battery pack. Specifically, at least one processor can control at least one robotic device equipped with a third end effector to move to a predetermined location to perform at least one task for insulation.

[0321] For example, at least one processor can move a third end effector provided with an insulating material to a predetermined position and perform an insulating operation by applying the insulating material to a target position.

[0323] FIG. 26 is a drawing illustrating the operation method of a robot system for disassembling the upper cover of a battery pack according to various embodiments.

[0324] Referring to FIG. 26, at least one processor included in the robot system can receive a control signal instructing to remove the battery cover (S2601).

[0325] The robot system can be implemented to dismantle the cover using multiple collaborative robots or to dismantle the cover using an industrial robot equipped with multiple pneumatic grippers.

[0326] At least one processor can obtain sensing data associated with the battery from at least one sensor (S2602).

[0327] At least one processor can identify the location of at least one fastening member for fixing the cover based on sensing data (S2603).

[0328] At least one processor can control the disassembly of at least one fastening member by considering the location of at least one identified fastening member (S2604). At this time, at least one robot device can perform at least one task of disassembling at least one fastening member from the battery using a first end effector (S2611).

[0329] At least one processor can identify the removal of at least one fastening member based on sensing data (S2605).

[0330] Additionally, optionally, at least one processor can perform an operation to dismantle a guide plate disposed between a plurality of fastening members and a battery. Specifically, at least one processor can control at least one robot device to dismantle the guide plate.

[0331] At least one processor can control the cover to be separated from the housing based on an identification operation (S2606). At this time, at least one robot device can perform at least one task of separating the cover from the battery housing using a second end effector (S2612).

[0333] FIG. 27 is a drawing illustrating the operation method of a robot system for disassembling a battery pack module according to various embodiments.

[0334] Referring to FIG. 27, at least one processor included in the robot system can receive a control signal instructing to dismantle at least one module of the battery (S2701).

[0335] A robot system can determine a disassembly order for disassembling multiple modules included in a battery pack. Specifically, the robot system can disassemble multiple modules according to a predetermined order using multiple robot devices. The robot system can determine the disassembly order in advance by considering the electrical connections between the multiple modules.

[0336] At least one processor can acquire sensing data associated with the battery from at least one sensor (S2702). At least one processor can identify the location of at least one fastening member for fixing at least one module based on the sensing data (S2703).

[0337] At least one processor can control the disassembly of at least one fastening member by considering the location of at least one identified fastening member (S2704). At this time, at least one robot device can perform at least one task of disassembling at least one fastening member from the battery using a first end effector (S2711).

[0338] At least one processor can identify the removal of at least one fastening member based on sensing data (S2705).

[0339] Additionally, optionally, at least one processor can perform an operation to dismantle a guide plate disposed between a plurality of fastening members and a battery. Specifically, at least one processor can control at least one robot device to dismantle the guide plate.

[0340] At least one processor can control at least one module to be separated from the housing based on an identification operation (S2706). At this time, at least one robot device can perform at least one task of separating at least one module from the battery housing using a second end effector (S2712).

[0341] Additionally, at least one processor can control at least one module to be flipped (S2707). At this time, at least one robotic device or a separate mechanism can perform at least one task for flipping at least one module (S2713). That is, at least one processor can perform the operation of flipping at least one module using at least one robotic device or a separate mechanism.

[0342] At least one processor can perform the operation of dismantling a cooling plate attached to the lower part of at least one module. The operation of dismantling the cooling plate by at least one processor using at least one robotic device may be applied as described in FIGS. 24 and 25.

[0344] FIG. 28 is a diagram illustrating the operation method of a robot system for disassembling a battery pack cell according to various embodiments.

[0345] Referring to FIG. 28, at least one processor included in the robot system can receive a control signal instructing to dismantle at least one cell of the battery (S2801).

[0346] A robot system can determine a disassembly order for disassembling multiple cells included in a battery pack. Specifically, the robot system can disassemble multiple cells according to a predetermined order using multiple robot devices. The robot system can determine the disassembly order in advance by considering the electrical connections between multiple cells.

[0347] At least one processor can acquire sensing data associated with the battery from at least one sensor (S2802). At least one processor can identify the location of at least one fastening member for fixing at least one cell based on the sensing data (S2803).

[0348] At least one processor can control the disassembly of at least one fastening member by considering the location of at least one identified fastening member (S2804). At this time, at least one robot device can perform at least one task of disassembling at least one fastening member from the battery using a first end effector (S2811).

[0349] At least one processor can identify the removal of at least one fastening member based on sensing data (S2805).

[0350] Additionally, optionally, at least one processor can perform an operation to dismantle a guide plate disposed between a plurality of fastening members and a battery. Specifically, at least one processor can control at least one robot device to dismantle the guide plate.

[0351] At least one processor can control at least one cell to be separated from the module housing based on an identification operation (S2806). At this time, at least one robot device can perform at least one task of separating at least one cell from the module housing using a second end effector (S2812).

[0353] FIG. 29 is a drawing illustrating a method for a battery processing system to store parts disassembled from a battery pack according to various embodiments.

[0354] Referring to FIG. 29, at least one processor included in the battery processing system can load at least one component (e.g., module, cell, fastening member, battery housing, etc.) disassembled from the battery onto a transfer device (S2901). At this time, the transfer device may include a mobile robot or a conveyor belt, but is not limited thereto. Additionally, the transfer device may be a different device (e.g., a separate mobile robot) from the transfer device where the battery disassembly operation is performed, but may also be the same device (e.g., a connected conveyor belt).

[0355] At least one processor can control a transfer device to transfer at least one part to storage (S2902). Storage may include a plurality of slots for receiving and storing parts of the battery. Storage may be configured as a device identical to the battery storage of FIG. 4, but may also be configured as an independent device.

[0356] Storage can be implemented to store components according to the type of battery component. For example, storage may include multiple zones partitioned by the type of battery component, and a specific zone may be configured to store a specific component.

[0357] At least one processor can determine a target slot on storage to store at least one component according to the type of component (S2903). Specifically, at least one processor can determine the target slot by selecting at least one slot corresponding to the type of component. At this time, the target slot may be assigned identification information corresponding to the type of battery component. Additionally, at least one processor can determine the target slot by determining a storage area on storage corresponding to the type of component.

[0358] At least one processor can determine a target slot using at least one sensor (e.g., a vision sensor). Specifically, at least one processor can determine a target slot by identifying at least one slot corresponding to the type of part to be stored based on sensing data.

[0359] In addition, at least one processor can determine a target slot based on a communication connection with a battery storage system. Specifically, at least one processor can transmit the type of part to be stored to the battery storage system, the battery storage system can determine a target slot corresponding to the part, and transmit location information of the determined target slot to a transfer device.

[0360] At least one processor controls the transfer device and storage to store at least one part in a determined target slot (S2904). The specific method for storing the part in the target slot can be the same as the method for storing the battery.

[0362] FIG. 30 is a flowchart illustrating a method for a battery processing system to disassemble a plurality of fastening members according to various embodiments.

[0363] FIG. 31 is a drawing illustrating a method for a battery processing system to dismantle a plurality of fastening members according to various embodiments.

[0364] Referring to FIG. 30, at least one processor included in the battery processing system may receive a control signal instructing to dismantle a first component of the battery (S3001). At least one processor may obtain sensing data associated with the battery from at least one sensor (S3002).

[0365] At least one processor can identify the position of a first fastening member for fixing a first part based on sensing data and set it as a first target position (S3003).

[0366] At least one processor can move at least one robot device to a first position corresponding to a first target position and perform a task of disassembling a first fastening member using at least one robot device (S3004).

[0367] At least one processor can identify the position of a second fastening member for fixing a first part based on sensing data and set it as a second target position (S3005).

[0368] At least one processor can adjust the position of the transfer device or at least one robot device so that the second target position is located within the working area of ​​at least one robot device (S3006).

[0369] For example, at least one processor can move a second target position into a work area by adjusting the position of a mobile robot. Specifically, at least one processor can move a second target position into a work area by rotating a mobile robot loaded with a battery.

[0370] Additionally, for example, at least one processor can move a second target position into a work area by adjusting the position of a conveyor belt. Specifically, at least one processor can move a second target position into a work area by rotating at least a portion of a conveyor belt loaded with batteries (e.g., a rotary conveyor).

[0371] Additionally, for example, at least one processor can move a second target position into a work area by adjusting the position of at least one robot device. Specifically, at least one processor can move the work area itself by moving at least one robot device, and accordingly, position the second target position into the work area.

[0372] At least one processor can move at least one robot device to a second position corresponding to a second target position and perform a task of dismantling a second fastening member using at least one robot device (S3007).

[0373] For example, referring to FIG. 31 (a), at least one processor can control the first fastening member (3101a) and the second fastening member (3101b) of the battery (10) to be disassembled using the first robot device (3100a) and the second robot device (3100b).

[0374] Additionally, referring to (a), (b) and (c) of FIG. 31, when the first fastening member (3101a) and the second fastening member (3101b) are disassembled, the transfer device (3150) can be controlled so that the third fastening member (3101c) and the fourth fastening member (3101d) are positioned within the working area of ​​the first robot device (3100a) and the second robot device (3100b).

[0375] For example, the transfer device (3150) can be controlled so that the third fastening member (3101c) and the fourth fastening member (3101d) are positioned within the working area of ​​the first robot device (3100a) and the second robot device (3100b) by moving the position of the mobile robot loaded with the battery (10). Specifically, at least one processor can adjust the position of the battery by rotating the mobile robot.

[0376] Additionally, for example, the transfer device (3150) can be controlled so that the third fastening member (3101c) and the fourth fastening member (3101d) are positioned within the working area of ​​the first robot device (3100a) and the second robot device (3100b) by driving a conveyor belt loaded with a battery (10). Specifically, at least one processor can adjust the position of the battery by driving a rotary conveyor.

[0377] Additionally, for example, the transfer device (3150) can be controlled so that the third fastening member (3101c) and the fourth fastening member (3101d) are positioned within the working area of ​​the first robot device (3100a) and the second robot device (3100b) by moving the positions of the first robot device (3100a) and the second robot device (3100b).

[0378] Additionally, referring to (c) and (d) of FIG. 31, when all fastening members are disassembled, at least one processor can disassemble the upper cover (3110) of the battery using the first robot device (3100a) and the second robot device (3100b).

[0380] FIG. 32 is a flowchart illustrating a method for a robot system to disconnect a wire connected to a battery according to various embodiments.

[0381] FIG. 33 is a drawing illustrating a method for a robot system to disconnect a wire connected to a battery according to various embodiments.

[0382] Referring to FIG. 32, at least one processor included in the robot system can receive a control signal instructing to disconnect at least one wire (S3201). At least one processor can obtain sensing data associated with a battery from at least one sensor (S3202).

[0383] At least one processor can recognize the location where the first wire and the battery are connected based on sensing data (S3203).

[0384] At least one processor can set a first portion of a wire spaced apart from a connected location by a predetermined distance as a first target location (S3204).

[0385] At least one processor can determine a first trajectory for the first end effector to reach a first position corresponding to a first target position (S3205). At this time, at least one robot device can perform an operation of moving the first end effector to the first position based on the first trajectory (S3211).

[0386] For example, referring to FIG. 33 (a), at least one processor can recognize the location where the battery and the first wire are connected based on sensing data and set a first part (3301) spaced a predetermined distance from that location as the first target location.

[0387] At this time, at least one processor can determine a first trajectory to reach a first position corresponding to a first target position and move the first robot device (3300) equipped with the first end effector (3310).

[0388] Referring again to FIG. 32, at least one processor can control the first end effector to perform a first task of separating the first wire (S3206). At this time, at least one robot device can perform the first task of separating the first wire using the first end effector (S3212).

[0389] For example, referring to FIG. 33(b), at least one processor can control the first robot device (3300) to perform the task of grasping the first part (3301) using the first end effector (3310) and pulling the first part to separate it from the battery.

[0390] The first task may include a plurality of unit actions for disconnecting the first wire. Specifically, the first task may include, but is not limited to, an action of bringing the first end effector (3310) closer to the first part (3301), an action of driving the first end effector (3310) to grasp the first part (3301), an action of driving the first robot device (3300) to disconnect the first wire from the battery, and an action of driving the first end effector (3310) to release the first part (3301).

[0391] For example, at least one processor can determine that the first part (3301) is positioned between the first end effectors (e.g., gripper-type grippers), drive the first end effectors (3310) to grasp the first part (3301), and control the first robot device (3300) to apply force in a direction of separation from the battery to separate the first wire.

[0392] At least one processor can determine the type of wire to be dismantled and determine at least one means for dismantling based on the determined type of wire.

[0393] Specifically, at least one processor can classify wires into a predetermined number of types based on sensing data.

[0394] Additionally, at least one processor can determine the type of end effector to be used for dismantling based on the determined type of wire. Specifically, at least one processor can determine the end effector to be used based on the type of wire, and can control at least one robot device to mount the determined end effector. Additionally, a task for dismantling the wire can be determined based on the mounted end effector.

[0395] For example, if the wire is of the first type that can be cut, the robot device can be controlled to be equipped with an end effector having a cutting member, and can be controlled to perform a task of cutting the first type of wire using the cutting member.

[0396] In addition, for example, if the wire is of the second type that cannot be cut, the robot device can be controlled to be equipped with an end effector having a gripper, and can be controlled to perform a task of grasping and pulling the second type of wire using the gripper.

[0397] The robot system can perform the operation of dismantling multiple wires using multiple robot devices.

[0398] At this time, the robot system can pre-specify the order in which multiple robot devices perform dismantling operations. Specifically, the robot system can determine the order in which multiple wires are dismantled by considering electrical connection relationships and stability, and can control multiple robot devices to dismantle the wires according to the determined order.

[0400] FIG. 34 is a flowchart illustrating a method for a robot system to dismantle a buffer connected to a battery according to various embodiments.

[0401] FIG. 35 is a drawing illustrating a method for a robot system to dismantle a buffer connected to a battery according to various embodiments.

[0402] Referring to FIG. 34, at least one processor included in the robot system can receive a control signal instructing to separate at least one buffer material (S3401). At least one processor can obtain sensing data associated with a battery from at least one sensor (S3402).

[0403] At least one processor can recognize at least one bonding location where at least one buffer material and the surface of the battery are bonded (S3403). Specifically, at least one processor can recognize at least one buffer material based on sensing data and recognize at least one bonding location where at least one buffer material and the surface of the battery are bonded based on a predetermined method.

[0404] For example, at least one processor can recognize at least one adhesive location by identifying an adhesive material based on sensing data.

[0405] In addition, for example, at least one processor can recognize at least one adhesive location by identifying at least one mark indicating an adhesive location based on sensing data.

[0406] Additionally, for example, at least one processor can recognize at least one adhesive location based on pre-stored battery information. Specifically, the battery information of the battery to be disassembled may include information regarding the adhesive location of the cushioning material, and at least one processor can recognize at least one adhesive location based on the information regarding said location.

[0407] At least one processor can set at least one recognized adhesive location as a first target location (S3404).

[0408] At least one processor can determine a first trajectory for the first end effector to reach a first position corresponding to a first target position (S3405). At this time, at least one robot device can perform an operation of moving the first end effector to the first position based on the first trajectory (S3411).

[0409] For example, referring to FIG. 35, at least one processor can identify a buffer material (3501) based on sensing data and recognize at least one bonding location where the buffer material (3501) is bonded to the battery surface. Additionally, at least one processor can set the recognized at least one bonding location as a first target location. In this case, if there are multiple bonding locations, at least one processor can set multiple first target locations.

[0410] At least one processor can determine a first trajectory to reach a first position corresponding to a first target position and move a first robot device (3500) equipped with a first end effector (3510). At this time, the first end effector (3510) may be a scraper for scraping off a specific material, but is not limited thereto.

[0411] At this time, the vertical position of the first end effector (3510) that has reached the first position may correspond to the space between the buffer material (3501) and the battery.

[0412] Referring again to FIG. 34, at least one processor can control a first end effector to perform a first task to remove the adhesive force between at least one buffer material and a battery (S3406). At this time, at least one robot device can use the first end effector to perform a first task to remove the adhesive force between at least one buffer material and a battery (S3412).

[0413] For example, referring to FIG. 35, at least one processor can control the first robot device (3500) so that the first end effector (3510) is inserted into a space (between the cushioning material (3501) and the battery) corresponding to at least one adhesive position. Specifically, at least one processor can control the first robot device (3500) to remove the adhesive force between the cushioning material (3501) and the battery using a scraper.

[0414] The first task may include a plurality of unit actions for removing adhesive force. Specifically, the first task may include, but is not limited to, an action of bringing the first end effector (3510) to at least one adhesive location, an action of inserting the first end effector (3510) into the space between the cushioning material (3501) and the battery (may include a reciprocating action for removing adhesive force), and an action of removing the first end effector (3510) from the space between the cushioning material (3501) and the battery.

[0415] Additionally, but not limited to this, at least one processor may control the first robot device (3500) so that the first end effector (3510) is inserted into the surrounding space (between the cushioning material (3501) and the battery) at at least one adhesive location. At this time, at least one processor may remove the adhesive force between the cushioning material (3501) and the battery by controlling the first robot device (3500) so that the first end effector (3510) moves in the space between the cushioning material (3501) and the battery.

[0416] At least one processor can determine whether the first task has been completed based on a predetermined criterion. Specifically, at least one processor can determine whether the adhesive force between the buffer material and the battery surface has been removed. For example, at least one processor can determine whether the adhesive force between the buffer material and the battery surface has been removed based on the frictional force acting on the first end effector (3510). At least one processor may determine that the adhesive force between the buffer material and the battery surface has been removed if the frictional force acting on the first end effector (3510) falls below a predetermined threshold value, but is not limited thereto.

[0417] Referring again to FIG. 34, after performing the first task, at least one processor can be controlled to perform a second task to separate at least one buffer material from the battery (S3407). At this time, at least one robot device can perform the second task to separate at least one buffer material from the battery (S3413).

[0418] At least one processor can control the execution of the second task using the first end effector.

[0419] For example, referring to FIG. 35, at least one processor can control a first robot device (3500) equipped with a first end effector (3510) to separate the buffer material (3501) from the battery. Specifically, at least one processor can separate the buffer material (3501) from the battery by controlling the first end effector (3510) inserted in the space between the buffer material (3501) and the battery to lift.

[0420] The second task may include a plurality of unit actions for separating the buffer material. Specifically, the second task may include, but is not limited to, an action of inserting the first end effector (3510) into the space between the buffer material (3501) and the battery, and an action of lifting the first end effector (3510) to separate the buffer material (3501).

[0421] Alternatively, at least one processor may control the execution of a second task using a second end effector different from the first end effector.

[0422] For example, referring to FIG. 35, at least one processor can perform a second task using a second robot device (3550) equipped with a second end effector (3520). At this time, the second end effector (3520) may be a gripper, but is not limited thereto. Specifically, at least one processor can separate the buffer material by using the second end effector (3520) to grasp the buffer material (3501) and perform the operation of detaching it from the battery.

[0423] At this time, the second task may include a plurality of unit actions for separating the buffer material. Specifically, the second task may include, but is not limited to, an action of driving the second end effector (3520) to grasp the buffer material (3501), an action of applying force to the second robot device (3550) to detach the buffer material (3501) from the battery, and an action of moving the second robot device (3550) to separate the buffer material (3501).

[0424] Alternatively, at least one processor can control the first robot device (3500) to release the first end effector (3510) and attach the second end effector, and perform a second task to separate the buffer material using the first robot device (3500) with the second end effector attached.

[0426] A series of actions in which at least one processor included in a robot system (such as described in FIGS. 24 to 35) generates a trajectory for controlling at least one robot device and drives the robot device according to the generated trajectory to perform at least one task may be actions learned based on an artificial intelligence engine.

[0427] For example, by using an artificial intelligence model that has learned control values ​​for performing all movements to execute a task, a robot system can dismantle a fastening member using the robot device.

[0428] FIG. 36 is a flowchart illustrating a method for a robot system to perform a task using a robot device according to various embodiments.

[0429] Referring to FIG. 36, at least one processor included in the robot system can learn an artificial intelligence model to perform a task for disassembling a first fastening member (S3601). At this time, the artificial intelligence model can be learned based on the purpose of the action, while mimicking human movements. The method of learning the robot artificial intelligence model has been described above and will be omitted.

[0430] Additionally, at least one processor can obtain at least one control value for controlling the operation of a robot device from at least one layer of a learned model (S3602). Additionally, at least one processor can control the robot device to perform a plurality of unit actions based on at least one control value (S3603). Additionally, at least one processor can perform a task by performing a plurality of unit actions using at least one robot device (S3604).

[0432] In addition, for the robot system to dismantle the battery, it is necessary to accurately determine the pose of the battery before performing the dismantling operation according to FIGS. 24 to 35.

[0433] The robot system can estimate the orientation of the battery by processing sensing data acquired using at least one sensor. Additionally, at least one processor can calibrate the battery to an orientation suitable for disassembly based on the estimated orientation.

[0434] FIG. 37 is a diagram illustrating a method for a robot system to estimate and correct the attitude of a battery based on sensing data according to various embodiments.

[0435] Referring to FIG. 37, at least one processor included in the robot system can acquire sensing data from at least one sensor (S3701).

[0436] Additionally, at least one processor can identify a reference position on the battery based on sensing data (S3702). Additionally, at least one processor can estimate the pose of the battery based on the positional relationship between the identified reference position and at least one robot device (S3703). Additionally, at least one processor can adjust the position of at least one robot device or transfer device so that the positional relationship satisfies a predetermined standard (S3704).

[0437] Specifically, at least one processor can estimate the orientation of the battery by identifying a reference position on the battery and perform calibration. At least one processor can adjust the position of the battery or adjust the position of at least one robot device so that the reference position on the battery and at least one robot device have a predetermined positional relationship (e.g., distance, direction, etc.).

[0438] For example, at least one processor can correct the posture by adjusting the position of the battery by controlling a transfer device (e.g., mobile robot, conveyor). Additionally, for example, at least one processor can correct the posture by adjusting the position of at least one robot device.

[0440] [Diversity of Battery Packs]

[0441] The detailed configuration of battery packs varies by manufacturer. The types, number, placement, and sizes of detailed components differ from pack to pack.

[0442] For example, battery packs from certain manufacturers are composed of modules, whereas those from others are composed of cells. Furthermore, the shapes of modules and cells often vary depending on the battery.

[0443] In other words, the battery processing process needs to be carried out differently depending on the battery profile (e.g., battery manufacturer, model, type, specifications, etc.).

[0444] In addition, it is necessary to implement a robust system for battery dismantling in various environments by training a robot AI engine based on various batteries.

[0446] [Fair Decision Algorithm]

[0447] FIG. 38 is a flowchart illustrating a method for a battery processing system to determine a processing process based on the profile of a battery, according to various embodiments.

[0448] Referring to FIG. 38, at least one processor included in the battery processing system can identify the profile of the target battery (S3801). At this time, the profile of the target battery may be stored in advance in the target battery information. Specifically, at least one processor can identify the profile by checking the battery information of the battery to be processed and checking the battery profile included in the battery information.

[0449] In addition, as another example, at least one processor can identify the profile of the target battery using at least one sensor.

[0450] FIG. 39 is a diagram illustrating a method for a battery processing system to identify a battery profile according to various embodiments.

[0451] At least one processor included in the battery processing system can acquire sensing data associated with the battery from at least one sensor. Additionally, the at least one processor can identify a battery profile by extracting at least one feature of the battery based on the sensing data.

[0452] For example, referring to FIG. 39 (a), at least one processor can identify a battery profile by sensing an identification mark (3901) of a battery (10) using at least one sensor (3910). At this time, the identification mark (3901) may be a barcode, QR code, etc., in which information about the battery is stored, but is not limited thereto.

[0453] Additionally, for example, referring to FIG. 39(b), at least one processor can identify a battery profile by sensing the shape of the battery (10) using at least one sensor (3910). Specifically, at least one processor can extract features associated with the shape of the battery (10) based on the sensing data. Additionally, at least one processor can identify a battery profile by determining information about the battery (e.g., manufacturer, model, etc.) based on the features associated with the shape.

[0454] Referring again to FIG. 38, at least one processor can compare the identified profile with pre-stored matching information—the matching information includes process information for at least one processing step to be performed according to the battery profile—(S3802).

[0455] Specifically, at least one processor can identify at least one processing process corresponding to a target battery based on pre-stored matching information. The matching information may include information on the processing process required for each battery profile (e.g., model), and at least one processor can identify at least one corresponding processing process by comparing the target battery profile with the matching information.

[0456] Additionally, the matching information may further include end effector information used when performing a specific process. Specifically, at least one processor can identify information regarding an end effector used when processing a target battery according to a specific process. In this case, at least one processor can transmit the end effector information to a specific process facility.

[0457] Additionally, the matching information may further include location information of a destination where the battery must be transported to perform a specific process. Specifically, at least one processor may identify location information regarding a destination where the target battery must be positioned to be processed according to a specific process. In this case, at least one processor may transmit said location information to a transport device loaded with the battery.

[0458] Table 1 below shows an example of the configuration of matching information.

[0459]

[0460]

[0461] In addition, at least one processor can determine at least one processing step to be performed on the target battery based on the comparison result (S3803).

[0462] In addition, at least one processor can physically process the target battery based on at least one determined processing process (S3804).

[0463] Specifically, at least one processor can set a location for performing at least one determined processing step as the destination of the transfer device and can transfer the battery using the transfer device. Additionally, at least one processor can control at least one robotic device to dismantle at least one component of the target battery.

[0465] Additionally, at least one processor can determine whether to perform a processing process on the battery based on the battery profile. Specifically, at least one processor can identify whether to perform a processing process to be performed on the target battery and, based on this, determine the processing process to be performed. For example, at least one processor can identify that the target battery is not discharged based on the target battery profile and determine that a battery discharge process is to be performed on the target battery.

[0467] FIG. 40 is a flowchart illustrating operations performed by a battery processing system after determining a processing process according to various embodiments.

[0468] FIG. 41 is a diagram illustrating the operation performed by a battery processing system after determining a processing process according to various embodiments.

[0469] Referring to FIG. 40, after at least one processor determines at least one processing process according to operation S3803, it can transmit end effector information to at least one sub-processor (processor of the robot device) corresponding to the determined at least one processing process (S4001). At this time, the at least one sub-processor can control the robot device to equip an end effector corresponding to the received information.

[0470] Additionally, at least one processor can set at least one location corresponding to at least one determined processing step as the destination of the transfer device (S4002). At this time, the transfer device can be controlled to drive autonomously to the set destination.

[0471] For example, referring to FIG. 41, at least one processor can transmit a destination location according to the profile of the target battery to a transfer device (4110) (S4101).

[0472] For example, at least one processor may determine to perform a first process on a battery based on the profile of the target battery. For example, the first process may be a battery pretreatment process (e.g., diagnosis, discharge, coolant removal, etc.).

[0473] In this case, at least one processor can set a first location for performing a first process as the destination of the transfer device (4110). At this time, the transfer device (4110) can move to the first location (S4102).

[0474] Additionally, at least one processor can pre-set an end effector to be used by the first robot device (4121) to perform the first process (S4103). At this time, the first robot device (4121) can be controlled to be equipped with the set end effector.

[0475] Additionally, at least one processor can perform a first process at a first location using a first robot device (4121) (S4104). For example, to perform a battery diagnostic process, at least one processor can control the diagnostic device to automatically connect to the battery using the first robot device (4121), but is not limited thereto.

[0476] As another example, at least one processor may decide to perform a second process on the battery based on the profile of the target battery. For example, the second process may be a battery dismantling process.

[0477] In this case, at least one processor can set a second location for performing a second process as the destination of the transfer device (4110). At this time, the transfer device (4110) can move to the second location (S4105).

[0478] Additionally, at least one processor can pre-set an end effector to be used by the second robot device (4122) to perform the second process (S4106). At this time, the second robot device (4122) can be controlled to be equipped with the set end effector.

[0479] Additionally, at least one processor may perform a second process at a second location using a second robot device (4122) (S4107). For example, at least one processor may perform at least one task for the battery lower body using the second robot device (4122) to perform a battery dismantling process, but is not limited thereto.

[0480] The level of disassembly required for batteries may vary depending on the manufacturer or model. Specifically, certain types of batteries are composed of modules, requiring the disassembly of the modules, while others are composed of cells, requiring the disassembly of the cells. Furthermore, the shapes of the cells and modules can differ for each battery type. Consequently, the types of end effectors used to disassemble modules or cells, as well as the dynamic parameters applied to them, may be learned differently.

[0482] Batteries vary widely not only in their battery profiles but also in their current condition. In particular, waste batteries requiring dismantling may have external damage or compromised stability.

[0483] A processing system for handling such waste batteries needs to accurately recognize the safety status of the batteries and perform the battery processing process.

[0484] A battery processing system according to one embodiment can recognize the state of the battery and the status of the processing process, and provide feedback based thereon.

[0485] FIG. 42 is a diagram illustrating a method for a battery processing system to determine whether a task can be performed based on sensing data for a battery, according to various embodiments.

[0486] Referring to FIG. 42, at least one processor included in the battery processing system can obtain sensing data associated with the battery from at least one sensor (S4201).

[0487] Additionally, at least one processor can identify whether at least a portion of the target battery is damaged based on sensing data (S4202). Specifically, at least one processor can recognize the appearance (e.g., shape) of the battery based on sensing data and identify whether the battery is damaged based on the recognized appearance. For example, at least one processor can identify whether the battery is damaged by comparing the original shape of the target battery with the shape recognized based on sensing data. At least one processor can identify the location of the battery damage by identifying at least a portion that differs from the original shape of the battery based on sensing data.

[0488] Additionally, at least one processor can determine whether a task can be performed based on the degree of damage (S4203). Specifically, if the battery is damaged beyond a certain level, at least one processor may determine that a task for the battery processing process cannot be performed. Additionally, if the battery is damaged below a certain level, at least one processor may determine that the task can be performed and may perform the task for the battery processing process using at least one robot device.

[0489] In addition, at least one processor can output an alarm if task execution is impossible (S4204). That is, by providing feedback to the user regarding the situation where task execution is impossible, at least one processor can provide an automated process with guaranteed stability.

[0490] At least one processor may determine that task execution is impossible based on sensing data. Specifically, at least one processor may determine whether task execution is possible by calculating similarity with learned cases based on sensing data. For example, at least one processor may determine that task execution is impossible if it is determined that the sensing data is an outlier case that is significantly different from existing learned cases. In addition, in this case, at least one processor may additionally train the artificial intelligence engine with task execution failure cases.

[0492] FIG. 43 is a diagram illustrating a method for a battery processing system to provide feedback during task execution according to various embodiments.

[0493] Referring to FIG. 43, at least one processor included in the battery processing system may transmit a command to perform a first task to at least one sub-processor (S4301). At this time, the at least one processor may be a processor included in at least one robot system for performing the first task. Additionally, the first task may include at least one unit action for performing a specific task. For example, a task for disassembling a fastening member of a battery may include, but is not limited to, a first unit action of bringing an end effector to the fastening member, a second unit action of the end effector contacting the fastening member, a third unit action of disassembling the fastening member using the end effector, and a fourth unit action of separating the fastening member.

[0494] At this time, at least one robot device can perform at least one unit action to perform the first task (S4311).

[0495] At this time, the completion of the task execution may not be identified even though unit actions exceeding a predetermined standard have been performed (S4303). In this case, at least one processor may determine that a problem has occurred in the execution of the first task. Specifically, at least one processor may determine that a problem has occurred in the execution of the first task if the first task is not completed even though at least one unit action for executing the first task has been performed repeatedly more than a predetermined number of times.

[0496] For example, at least one processor may perform a first unit action of dismantling a bolt by rotating an end effector to perform a task of dismantling a bolt. In this case, if the bolt is not dismantled even though the first unit action has been performed beyond a predetermined threshold, at least one processor may determine that a problem has occurred in the task execution.

[0497] At this time, at least one processor can stop the operation of at least one robot device (S4312) and output an alarm (S4304).

[0498] Additionally, at least one processor can additionally learn task execution failure cases in the artificial intelligence engine (S4305). Specifically, for failure cases, at least one processor can additionally learn the action of performing the first task using a robot device (e.g., imitation learning, backreinforcement learning, etc.).

[0499] For example, at least one processor may additionally learn disassembly operations to the artificial intelligence engine for cases where disassembly is difficult due to damage to the guide plate, cases where disassembly is difficult due to damage to a part of the bolt, etc., but is not limited thereto.

[0501] [Multi-robot control algorithm]

[0502] A battery processing system according to one embodiment performs battery processing tasks using a plurality of robotic devices. In addition, the battery processing system trains and utilizes an artificial intelligence engine to control the plurality of robotic devices simultaneously. At this time, the artificial intelligence engine is trained to control the plurality of robotic devices without mutual interference.

[0503] To this end, the battery processing system can train an artificial intelligence engine so that multiple robotic devices perform tasks by taking into account the movements of other robotic devices.

[0505] FIG. 44 is a diagram illustrating a method in which a battery processing system, according to various embodiments, controls a plurality of robot devices using an artificial intelligence engine.

[0506] Referring to FIG. 44, the battery processing system (4400) may include a processor and an artificial intelligence engine. The battery processing system (4400) may receive a task execution command and, in response to the received operation, may determine trajectories for controlling a plurality of robots using the processor and the artificial intelligence engine. At this time, since the specific method for the artificial intelligence engine to generate the robot trajectories has been described above, it will be omitted.

[0507] In addition, the processor can control multiple robot devices based on a determined trajectory. For example, the processor can control the first robot device by transmitting a first trajectory to the first robot device. In addition, the processor can control the second robot device by transmitting a second trajectory to the second robot device.

[0509] FIG. 45 is a flowchart illustrating an embodiment in which a battery processing system, according to various embodiments, controls a plurality of robot devices using an artificial intelligence engine.

[0510] Referring to FIG. 45, at least one processor can acquire input data based on sensing data acquired from at least one sensor and input it into an artificial intelligence model (S4501). In this case, at least one processor can acquire output data from at least one layer included in the artificial intelligence model.

[0511] At least one processor can obtain at least one control value for at least one power device of the first robot device based on output data (S4502).

[0512] Specifically, at least one processor can drive a power device (e.g., motor, etc.) included in a drive unit (e.g., joint, etc.) for driving a first robot device by transmitting at least one control value to the power device.

[0513] At least one processor can determine a first trajectory for moving a first end effector connected to a first robot device to a target position based on at least one control value, taking into account the control of the second robot device (S4503). At this time, at least one processor can determine the control state of the second robot device by sensing the movement of the second robot device or by receiving the trajectory of the second robot device.

[0514] At least one processor can determine the trajectory of the first robot device by considering the position of the second robot device. At this time, at least one processor can determine the position of the second robot device based on sensing data associated with the battery. For example, at least one processor can determine the trajectory of the first robot device by considering the position of the second robot device such that the position of the second robot device does not correspond to the path of the first robot device. At least one processor can determine the trajectory of the first robot device such that the trajectory of the first robot device does not overlap with the position of the second robot device.

[0515] Additionally, at least one processor can determine the trajectory of the first robot device by considering the trajectory of the second robot device. At this time, at least one processor can determine the trajectory of the second robot device by receiving the trajectory of the second robot device. For example, at least one processor can predict the position of the second robot device over time by considering the trajectory of the second robot device, and can determine the trajectory of the first robot device such that the predicted position of the second robot device does not correspond to the path of the first robot device. At least one processor can determine the trajectory of the first robot device such that the trajectory of the first robot device does not overlap with the trajectory of the second robot device.

[0516] Additionally, at least one processor can determine the trajectory of the first robot device by considering the path of the second robot device. At this time, at least one processor can determine the path of the second robot device by predicting the path of the second robot device based on sensing data. Specifically, at least one processor can determine the path of the second robot device by predicting the path that the second robot device will move forward based on the path that the second robot device has traveled based on sensing data. For example, at least one processor can predict the position of the second robot device over time by considering the path of the second robot device, and can determine the trajectory of the first robot device such that the predicted position of the second robot device does not correspond to the path of the first robot device. At least one processor can determine the trajectory of the first robot device such that the trajectory of the first robot device does not overlap with the predicted path of the second robot device.

[0518] FIG. 46 is a flowchart illustrating an embodiment in which a battery processing system, according to various embodiments, controls a plurality of robot devices using an artificial intelligence engine.

[0519] Referring to FIG. 46, at least one processor can acquire sensing data from at least one sensor (S4602). At least one processor can acquire a first set of dynamic parameters for controlling the first robot device (S4602). At this time, the first set of dynamic parameters may be a set of dynamic parameters (e.g., force, torque, acceleration, velocity, etc.) at a plurality of points forming the trajectory of the first robot device.

[0520] Additionally, at least one processor can obtain a second set of dynamic parameters for controlling a second robot device based on sensing data and a first set of dynamic parameters (S4603).

[0521] At least one processor can generate a trajectory of a second robot device by considering the control of the first robot device when the first robot device is identified based on sensing data. This is because interference from the first robot device may occur in the trajectory of the second robot device when the first robot device is identified based on sensing data. That is, at least one processor can identify the first robot device based on sensing data, verify a first dynamic parameter set corresponding to the trajectory of the first robot device, and obtain a second dynamic parameter set corresponding to the trajectory of the second robot device based on the first dynamic parameter set.

[0522] At least one processor can generate a trajectory of a second robot device without considering the control of the first robot device when the first robot device is not identified based on the sensing data. This is because when the first robot device is not identified based on the sensing data, there is no interference of the first robot device with the trajectory of the second robot device.

[0524] FIG. 47 is a diagram illustrating an embodiment in which a battery processing system, according to various embodiments, controls a plurality of robot devices using an artificial intelligence engine.

[0525] Referring to FIG. 47, at least one sensor included in the battery processing system can acquire sensing data and transmit it to at least one processor (e.g., a first processor, a second processor, a third processor).

[0526] At this time, at least one processor can operate to control multiple robot devices based on sensing data.

[0527] For example, the first processor can determine a first target position to move the first robot device based on sensing data and transmit it to the first robot device.

[0528] Additionally, the second processor can generate a first trajectory for moving the first robot device to a first target position. At this time, the first trajectory may include a first dynamic parameter, which is a set of dynamic parameters at a plurality of points included in the trajectory.

[0529] At this time, the second processor can determine the first dynamic parameter set by considering the control state of the other robot device.

[0530] For example, the third processor can determine a second set of dynamic parameters that constitute the trajectory of the second robot device and transmit it to the second processor.

[0531] The second processor can determine the first dynamic parameter set based on the first target location and the received second dynamic parameter set.

[0532] A battery processing system according to one embodiment can control a plurality of robots without interference by using an artificial intelligence model, and thereby improve the efficiency of robot control.

[0534] [Example of implementation of a working chamber]

[0535] The battery processing system can transfer the battery into a work chamber (see reference numeral 2325 in FIG. 23) and perform a battery processing process (e.g., dismantling process) using at least one robotic device. In this case, the work chamber may be a modular space for performing battery processing (e.g., dismantling) operations.

[0537] FIG. 48 is a drawing illustrating an example of a work chamber implemented in a battery processing system according to various embodiments.

[0538] Referring to FIG. 48, the battery processing system may include at least one work chamber (4810, 4820). The work chamber may have a space inside for performing battery processing operations. The work chamber may have a rectangular shape, but is not limited thereto. The work chamber may further include a control module (4830) for controlling a process performed inside and a display module (4840) for monitoring a process performed inside. The work chamber may further include at least one passage (4850) for spatially connecting the outside and the inside.

[0539] A battery processing system may include a plurality of working chambers. For example, the battery processing system may include a first working chamber (4810) and a second working chamber (4820) connected to each other, but is not limited thereto. In this case, the first working chamber (4810) and the second working chamber (4820) may be spatially connected. For example, the first working chamber (4810) and the second working chamber (4820) may be spatially connected through a passage formed between the first working chamber (4810) and the second working chamber (4820).

[0540] At this time, a transfer device for transferring batteries between multiple work chambers may include a mobile robot moving between the work chambers or a conveyor formed across the multiple work chambers. For example, a mobile robot loaded with batteries can move between the first work chamber (4810) and the second work chamber (4820) by moving through the passage.

[0541] Multiple work chambers may be implemented to perform different operations. In this case, a battery in which a first process (e.g., disassembly of a battery cover) has been performed in a first work chamber (4810) may be transferred to a second work chamber (4820) (using a transfer device), and a second process (e.g., disassembly of a battery module) may be performed in the second work chamber (4820).

[0542] In addition, not limited to this, multiple work chambers may be implemented to perform the same operation.

[0544] FIG. 49 is a drawing illustrating the internal configuration of a working chamber implemented in a battery processing system according to various embodiments.

[0545] Referring to FIG. 49, the work chamber may include at least one robot device (4910). In this case, at least one robot device (4910) may be attached to the ceiling of the work chamber. Alternatively, at least one robot device (4910) may be attached to a fixedly mounted part in the work chamber.

[0546] Additionally, the work chamber may be provided with at least one workspace (4920) for performing battery processing operations. At this time, the workspace (4920) may be located within the work area of ​​at least one robot device (4910). The battery processing system may control a battery transfer device so that a battery that has entered the work chamber is located in the workspace (4920).

[0547] At least one robot device (4910) can be controlled to perform a process for dismantling at least one part of a battery located on a workspace (4920). At this time, the at least one robot device (4910) may include at least one collaborative robot or industrial robot, but is not limited thereto.

[0548] For example, at least one robot device (4910) may be controlled to perform a process of dismantling the top cover of a battery. The battery with the cover dismantled may be moved to another chamber via a transfer device to perform the next process. Additionally, but not limited to this, at least one robot device (4910) may be controlled to perform a process of dismantling at least one module of the battery with the cover dismantled. Additionally, at least one robot device (4910) may be controlled to perform a process of dismantling at least one cell of the battery with the module dismantled. In this case, at least one robot device (4910) may be controlled to replace the module or cell with an end effector suitable for dismantling. Additionally, the dismantled parts may be loaded onto a transfer device, and the transfer device may transport the dismantled parts by moving outside the work chamber.

[0549] Detailed information regarding the method of disassembling battery components (e.g., covers, modules, cells, etc.) using at least one of the aforementioned robotic devices has been previously provided and will therefore be omitted.

[0551] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

[0552] Although the embodiments have been described above with reference to limited examples and drawings, those skilled in the art can make various modifications and variations from the description above. For example, suitable results can be achieved even if the described techniques are performed in a different order than described, and / or the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0553] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

Claims

Claim 1 A method of operation of a computing device for controlling a process for processing a battery using at least one robotic device, comprising: a step of acquiring information about a battery by at least one processor included in the computing device; a step of identifying whether a communication connection with a battery management system (BMS) connected to the battery is possible based on the information about the battery; a step of selecting one of at least two predetermined process sequences based on whether a communication connection is possible; and a step of controlling the at least one robotic device to perform a battery processing process according to the selected process sequence; wherein the step of selecting the process sequence is characterized by selecting a first process sequence including a diagnostic process of acquiring battery status information through at least one port electrically connected to the BMS when a communication connection with the BMS is possible, and selecting a second process sequence in which, when a communication connection with the BMS is impossible, at least one internal port inside the battery is identified, and the robotic device is controlled to physically connect a diagnostic connector to the identified internal port, and then perform a battery diagnostic process. Claim 2 delete Claim 3 delete Claim 4 delete Claim 5 delete Claim 6 A method of operation according to claim 1, further comprising: a step of diagnosing the battery using a diagnostic device corresponding to the diagnostic connector; and a step of calculating the diagnostic grade of the battery according to the diagnostic result. Claim 7 A method of operation according to claim 6, further comprising the step of determining a target slot on a storage to store the battery according to the diagnostic grade by the at least one processor. Claim 8 A method of operation according to claim 1, wherein the step of identifying whether communication is possible comprises: a step of acquiring sensing data associated with the battery by the at least one processor; and a step of identifying at least one port connected to the battery management system based on the sensing data. Claim 9 A method of operation according to claim 1, wherein the step of identifying whether communication is possible comprises: acquiring sensing data associated with the battery by the at least one processor; identifying the degree of damage of at least one port corresponding to the battery management system based on the sensing data; and confirming whether physical connection to the at least one port is possible based on the identified degree of damage. Claim 10 A method of operation according to claim 1, wherein the step of identifying whether communication is possible includes the step of identifying whether the battery management system is authorized based on information about the battery by the at least one processor. Claim 11 A computing device for controlling a process for processing a battery using at least one robotic device, comprising: a memory; and at least one processor electronically connected to the memory; wherein the at least one processor is configured to acquire information about the battery, identify whether a communication connection with a battery management system (BMS) connected to the battery is possible based on the information about the battery, select one of at least two predetermined process sequences based on whether a communication connection is possible, and control the at least one robotic device to perform a battery processing process according to the selected process sequence; wherein the at least one processor selects a first process sequence including a diagnostic process of acquiring battery status information through at least one port electrically connected to the BMS when a communication connection with the BMS is possible, and selects a second process sequence in which, when a communication connection with the BMS is impossible, at least one internal port inside the battery is identified, and the robotic device is controlled to physically connect a diagnostic connector to the identified internal port, and then performs a battery diagnostic process. Claim 12 delete Claim 13 delete Claim 14 delete Claim 15 delete Claim 16 In claim 11, the at least one processor is a computing device further configured to diagnose the battery using a diagnostic device corresponding to the diagnostic connector and to calculate the diagnostic grade of the battery according to the diagnostic result. Claim 17 In paragraph 16, the above at least one processor is a computing device further configured to determine a target slot on a storage to store the battery according to the diagnostic grade. Claim 18 In claim 11, the computing device configured such that at least one processor acquires sensing data associated with the battery and identifies whether communication is possible by identifying at least one port connected to the battery management system based on the sensing data. Claim 19 A computing device configured such that, in claim 11, the at least one processor acquires sensing data associated with the battery by the at least one processor, identifies the degree of damage to at least one port corresponding to the battery management system based on the sensing data, and identifies the possibility of communication by confirming whether physical connection to the at least one port is possible based on the identified degree of damage. Claim 20 In claim 11, the computing device configured such that at least one processor identifies whether communication is possible by identifying whether the battery management system is authorized based on information regarding the battery. Claim 21 A computer program in which the method of claim 1 is implemented and stored on a computer-readable storage medium.

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