Patent Application
20250042661
This U.S. application claims priority to and the benefit of Korean Patent Application No. 10-2023-0100771, filed on 2023 Aug. 2, Korean Patent Application No. 10-2023-0125412, filed on 2023 Sep. 20, Korean Patent Application No. 10-2023-0144367, filed on 2023 Oct. 26, Korean Patent Application No. 10-2023-0169605, filed on 2023 Nov. 29, Korean Patent Application No. 10-2023-0169606, filed on 2023 Nov. 29, Korean Patent Application No. 10-2023-0169607, filed on 2023 Nov. 29, Korean Patent Application No. 10-2023-0169612, filed on 2023 Nov. 29 in the Korean Intellectual Property Office (KIPO), the disclosures of all of which are incorporated by reference herein in their entireties.
The present disclosure pertains to a battery processing system. Specifically, the present disclosure describes a system for transporting, storing, pre-processing, and dismantling batteries.
As global interest in environmental sustainability has increased, the distribution of electric vehicles has expanded significantly. Consequently, the demand for batteries used in electric vehicles has surged. However, given that automotive batteries typically require replacement every 3 to 4 years, the amount of waste batteries has increased substantially.
This has led to a growing need for technology capable of safely dismantling batteries used in electric vehicles. To achieve safe dismantling, it is crucial to accurately diagnose the state/condition of the battery and discharge any remaining current within the battery.
Currently, the dismantling of various battery components is performed manually, which requires a high level of expertise and precision. Relevant prior art includes Korean Patent Application No. 10-2022-0111328 (filed on 2023 Mar. 15).
One objective of the present disclosure is to provide a battery processing system. This disclosure may provide a battery storage method based on battery information. Additionally, it may present a method and a system for automatically processing a battery using a robot apparatus. Furthermore, this disclosure may describe a method for determining a battery processing process based on battery information, as well as a method for training an artificial intelligence (AI) engine to control a robot apparatus, and a method for controlling the robot using the trained AI engine.
The objectives of the present disclosure are not limited to those mentioned above. Additional objectives may become apparent to those skilled in the art with reference to the following detailed description and the accompanying drawings.
According to one embodiment of the present disclosure, a method for transporting and storing a battery is provided. The method comprises the steps of: receiving, by at least one processor in an electronic device, a first control signal indicating that a first battery is stored in a storage, wherein the storage includes a plurality of slots; obtaining battery information corresponding to the first battery and identification information including 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; transporting the first battery to the target slot to accommodate the first battery; and updating the identification information by associating the battery information with an identifier of the target slot.
According to another embodiment of the present disclosure, a robot system for controlling the operation of at least one robot apparatus is provided. The method comprises: obtaining, by at least one processor in the robot system that communicates with at least one processing device, an instruction to control the robot apparatus to grip at least one connector in the processing device using an end effector; obtaining sensing data associated with the battery from at least one sensor; setting a location corresponding to at least one port connected to the battery as a target location based on the sensing data; determining a first trajectory for the end effector to reach a first location corresponding to the target location; and determining a second trajectory for connecting the at least one connector held by the end effector to the at least one port at the first location.
According to another embodiment of the present disclosure, a computing device that controls the processing of a battery using at least one robot apparatus is provided. The method comprises: obtaining battery information by at least one processor in the computing device; identifying whether a communication connection with a battery management system (BMS) connected to the battery is possible based on the battery information; selecting one of at least two predetermined process orders based on the possibility of the communication connection; and controlling the at least one robot apparatus to perform a battery processing process according to the selected process order.
In another embodiment, the present disclosure may provide a robot system that controls the operation of at least one robot apparatus implemented to perform a task based on a control signal. The method comprises: receiving, by at least one processor in the robot system, a control signal indicating that a first component of a battery is to be dismantled; obtaining sensing data associated with the battery from at least one sensor; identifying a location of a first fastening member fixing the first component based on the sensing data and setting the location as a first target location; determining a first trajectory for a first end effector to reach a first location corresponding to the first target location; controlling the first end effector to perform a first task of dismantling the first fastening member from the battery at the first location; setting a second target location based on the sensing data and determining a second trajectory for a second end effector to reach a second location corresponding to the second target location; and controlling the second end effector to perform a second task of disconnecting the first component at the second location.
According to another embodiment of the present disclosure, it provides a robot system for controlling a robot apparatus implemented to perform a task based on a control signal. The method comprises: receiving, by at least one processor in the robot system, a control signal instructing the disconnection of at least one electric wire from a battery; obtaining sensing data associated with the battery from at least one sensor; recognizing a location where a first electric wire is connected to the battery based on the sensing data; setting a first portion of the electric wire spaced apart from the connected location 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 to disconnect the first electric wire.
According to another embodiment of the present disclosure, a robot system is provided for controlling a robot apparatus implemented to perform a task based on a control signal. The method comprises: receiving, by at least one processor in the robot system, a control signal instructing the disconnection of at least one buffer material from a battery; obtaining sensing data associated with the battery from at least one sensor; recognizing at least one adhesive location where at least one buffer material is adhered to a surface of the battery; setting the at least one adhesive location as a first target location, determining a first trajectory for a first end effector to reach a first location corresponding to the first target location; controlling the first end effector to perform a first task for removing adhesive force between the at least one buffer material and the battery; and after performing the first task, controlling the first end effector to perform a second task for disconnecting the at least one buffer material from the battery.
According to another embodiment of the present disclosure, it provides a battery processing system including at least one robot apparatus. The method comprises: identifying, by at least one processor, a profile of a target battery; comparing the identified profile with pre-stored matching information, wherein the matching information includes process information about at least one processing process to be performed according to the battery profile; determining at least one processing process for the target battery based on the comparison; and physically processing the target battery according to the determined processing process.
In another embodiment, the present disclosure provides at least one robot apparatus including at least one sub-processor. The method comprises: transmitting, by at least one processor, a command for performing a first task to at least one sub-processor, performing at least one unit action to accomplish the first task using the at least one robot apparatus, identifying, by the at least one processor, whether the first task is completed, and stopping the operation of the at least one robot apparatus if the first task is not completed even after the unit action exceeds a predetermined criterion.
According to an embodiment of the present disclosure, a robot system includes at least one power device, a first robot apparatus, a second robot apparatus, a first end effector, at least one sensor, an artificial intelligence model, and at least one processor. The method comprises: feeding input data based on sensing data obtained from the at least one sensor to the artificial intelligence model; obtaining 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 determining a first trajectory for moving the first end effector, connected to the first robot apparatus, to a target location based on the at least one control value, wherein the first trajectory is determined by obtaining at least one dynamic parameter for each of a plurality of points along the path to the target location, considering the control of the second robot apparatus.
According to the present disclosure, a battery processing system that provides an optimized storage method based on battery information is provided. Additionally, a method for efficiently processing a battery using a robot apparatus is provided.
The embodiments and effects of the present disclosure are not limited to those mentioned above. A better understanding of various embodiments and effects of the present disclosure may be gained by those skilled in the art with reference to the following detailed description and the accompanying drawings.
Hereinafter, the 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 to those skilled in the art and are not directly related to the present disclosure will be omitted. This is to clearly describe the subject matter of the present disclosure by omitting redundant descriptions.
The embodiments presented in this specification are intended to clearly describe the spirit of the present invention to those of ordinary skill in the relevant art. The present invention is not limited to the embodiments described herein, and the scope of the present invention should be interpreted to encompass modifications or variations that do not depart from its spirit of the present invention. The example embodiments provided herein serve to explain the principles of the invention and its various applications, thereby enabling those skilled in the art to utilize the invention and understand the embodiments with many modifications and variations.
Although the terminology used in this specification includes as general terms currently widely accepted for describing the functions in the present invention, interpretations of these terms may vary depending on the intentions of practitioners in the relevant field, precedents, or emerging technologies. In a case where a specific term is defined and used with different meanings, the specific meaning will be explicitly provided. Therefore, the terms used herein should be interpreted based on the substantive meaning and the overall context of this specification rather than their mere literal meaning.
The accompanying drawings are intended to easily describe the present invention, and the shapes depicted in the drawings may be exaggerated as necessary to aid understanding of the present invention. Thus, the scope of the present invention is not limited by the depictions in the drawings.
In cases where describing detailed configurations or functions known in relation to the present invention may make the subject matter ambiguous, such description will be omitted as necessary. Additionally, numerical designations (e.g., first, second) used in the description are merely symbols for differentiating one component from another component and do not imply a sequential or hierarchical order unless the context clearly indicates otherwise. Throughout this specification, the same reference numbers refer to the same components.
The suffixes “part,” “module,” and “unit” used for the components in this specification are provided for ease of writing and do not imply distinctive meaning, functions, or roles by themselves. The terms “first” and “second” may be used to describe various components, but these terms are only for differentiation purposes. For example, the first component may be termed the second component, and vice versa.
As used in the embodiments and claims, the singular forms such as “a,” “an,” and “the” are inclusive of their respective plural forms as well, unless the context clearly indicates otherwise. Furthermore, the symbols like “/” and “and/or” refer to and encompass any and all possible combinations of the items they associate.
It should be understood that when an element is described as being “connected” or “coupled” to another element, there may be intervening elements in between or it may be directly connected or coupled to the other element. On the other hand, when an element is described as being “directly connected” or “directly coupled” to another element, it should be understood that there are no intervening elements. Other expressions that describe the relationship between elements (i.e., “between” and “immediately between” or “neighboring to” and “directly neighboring to”) should be interpreted similarly.
In the drawings, each block in the processing flowchart and combinations thereof may be executed by computer program instructions. These instructions may be embedded on a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus. The instructions executed through the processor create means for performing the functions described in the blocks of the flowchart. These instructions may be stored in a computer-usable or computer-readable memory to direct a computer or other programmable data processing apparatus to implement a function in a specific manner, thereby producing an article of manufacture containing instructions for performing the functions described. Furthermore, the instructions may be embedded in a computer or other programmable data processing apparatus and guide the execution of the functions by generating a computer-executed process through a series of operational steps.
Each block may represent a module, segment, or portion of code including one or more executable instructions designed to perform a specified logical function. It should be noted that in some embodiments, the functions mentioned in the blocks may occur in a different order than described. For example, two blocks shown in succession may be performed concurrently, simultaneously or in reverse order, depending on the functions they represent.
The term “unit” used in this specification refers to software or hardware components such as Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuit (ASIC). The “unit” performs specific roles but is not limited to software or hardware. The “unit” may be configured to reside in an addressable storage medium or to reproduce one or more processors. Accordingly, in some embodiments, the “unit” includes components such as software components, object-oriented software components, class components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided in the components and “units” may be combined into fewer components and “units,” or it may be disseminated into additional components and “units.” These components and “units” may be implemented to reproduce one or more CPUs within a device or a secure multimedia card. Additionally, according to various embodiments of the present disclosure, the “units” may include one or more processors.
Hereinafter, the operating principles of the present disclosure will be described in detail with reference to the accompanying drawings. When describing the present disclosure, detailed descriptions of related known functions or configurations will be omitted if their inclusion may obscure the subject matter. The terms described below are defined considering the functions of the present disclosure and may vary depending on the user, operator, or customary practices. Therefore, definitions should be given consistent with the description throughout this specification.
According to one embodiment of the present disclosure, a method for transporting and storing a battery is provided. The method comprises the steps of: receiving, by at least one processor in an electronic device, a first control signal indicating that a first battery is stored in a storage, wherein the storage includes a plurality of slots; obtaining battery information corresponding to the first battery and identification information including 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; transporting the first battery to the target slot to accommodate the first battery; and updating the identification information by associating the battery information with an identifier of the target slot.
The identification information may include state information indicating a state of the battery to be stored in the plurality of slots, and the target slot may be determined by comparing the state information and the battery information. The battery information may include at least one of a battery profile, State of Charge (SOC) information, or State of Health (SOH) information.
Transporting the first battery may involve transporting the first battery to a predetermined location within the storage using a transport device and transporting the first battery from the predetermined location to the target slot using a transport module in the storage.
Determining the target slot may include checking whether the battery is placed in each slot based on their respective identifiers, and then selecting a slot that does not currently house the battery.
Determining the target slot may involve identifying a battery storage condition by determining the state of the battery based on the battery information and selecting a slot that matches the battery storage condition based on the identification information.
The battery information, which includes stability information indicating the stability of the first battery, may be derived from sensing data obtained from at least one sensor monitoring the first battery.
Referring to
Specifically, as shown in
These facilities perform automated battery processing processes based on multiple automated systems. Specifically, these facilities may operate based on processing results (e.g., calculations, decisions, commands) from at least one computing device or electronic device equipped with an automated system.
For example, as depicted in
The computing device 200 may control the battery transport system 21 to move the battery to a designated location, the battery storage system 22 to store or take out the battery in a specified area, the battery pre-processing system 23 to pre-process the battery according to a predetermined method, and the battery dismantling system 24 to dismantle at least one component of the battery.
Additionally, the computing device 200 may cooperate with a recycling or reuse company system 25 to handle post-processing of the dismantled battery. For example, the computing device 200 may transport the dismantled battery to a waste battery recycling company for recycling or the reuse of the battery.
An example of a battery processing process using the battery processing automation system 1 is outlined as follows.
Referring to
The automation systems including the battery processing automation system 1 may be implemented based on the robot system and the computing device for controlling the robot system.
Referring to
The computing device 300 may include a processor 301, a memory 302, a communication circuit 303, and other components that are obvious to those skilled in the art.
The processor 301 may include at least one processor with multiple functions. For instance, software (e.g., a program) may be executed to control at least one component (e.g., hardware or software) of the computing device connected to the processor 301, and it may handle various data processing or calculation.
According to one embodiment, during data processing or calculation, the processor 301 may store the command or data received from the other components in the memory 302 (e.g., volatile memory), process the command or data stored in the volatile memory, and store the results in the non-volatile memory. The processor 301 may include a main processor (e.g., central processing device or application processor) or an auxiliary processor (e.g., neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor), which may operate independently or together. For example, the auxiliary processor may be set to use lower power than the main processor or to be specific to a specified function. The auxiliary processor may operate independently or together with the main processor. For example, the auxiliary processor may operate when the main processor is in an inactive state (e.g., sleep mode) or concurrently with the main processor when the main processor is in an active state (e.g., application execution). The auxiliary processor may control a function or state of various components (e.g., the communication circuit 303) when the main processor is inactive. Additionally, the auxiliary processor (e.g., the image signal processor or the communication processor) may be implemented as part of another component (e.g., the communication circuit 303) that is functionally related to. According to an embodiment, the auxiliary processor (e.g., the neural network processing device) may include a hardware structure specialized in processing the artificial intelligence model.
The artificial intelligence model may be generated through machine learning. Such learning may be implemented in the computing device that the artificial intelligence model is performed or may be performed through a separate server. The learning algorithm may include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, or inverses reinforcement learning. The artificial intelligence model may include multiple artificial neural network layers. The artificial neural network may be one of deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), deep belief network (DBN), bidirectional recurrent deep neural network (BRDNN), deep Q-networks, transformer, or a combination of two or more of the above, but is not limited to the above examples. The artificial intelligence model may additionally or alternatively include a software structure in addition to the hardware structure. Meanwhile, the operation of the computing device described below may be understood as the operation of the processor 301.
The memory 302 may store various data output by at least one component (e.g., processor 301) of the computing device. The data may include software and input or output data for instructions. The memory 302 may include a volatile memory or a non-volatile memory and store the operating system, middleware, or application, and/or the artificial intelligence models.
The communication circuit 303 may support the establishment of a direct (e.g., wired) or wireless communication channel between the computing device and an external electronic device (e.g., the measuring device or the user device), and communication performance through the established communication channel. The communication circuit 303 may include one or more communication processors (e.g., communication chips) that operate independently from the processor 301 (e.g., the program processor) and support wired or wireless communication.
The communication circuit 303 may include a wired or wireless communication module for cellular, short-range wireless, GNSS, local area network (LAN), or power line communication module. These communication modules may communicate with computing device (e.g., mobile device, wearable device, or server device) over various networks (e.g., short-range communication network such as Bluetooth, Wi-Fi (wireless fidelity) direct, IrDA (infrared data association) or long-range communication network such as legacy cellular network, 5G network, 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 one component (e.g., a single chip), or may be implemented as multiple separate components (e.g., multiple chips). The wireless communication module may identify or authenticate the computing device within the communication network by using subscriber information (e.g., international mobile subscriber identifier (IMSI)) stored in the subscriber identification module. The wireless communication module may support 5G networks and next-generation communication technologies (e.g., new radio access technology (NR)). The NR access technology may support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), terminal power minimization and multiple terminal access (mMTC (massive machine type communications)), or high-reliability and low-latency communications (URLLC). The wireless communication module may support a high frequency band (e.g., mmWave band) to achieve a high data rate. The wireless communication module may support various technologies for securing performance in the high frequency band, for example, beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large-scale antenna.
The wireless communication module may support a Peak data rate (e.g., 20 Gbps or more) for eMBB implementation, a loss coverage (e.g., 164 dB or less) for mMTC implementation, or a U-plane latency (e.g., 0.5 ms or less, or round trip 1 ms or less, respectively, for downlink (DL) and uplink (UL)) for URLLC implementation.
The robot system 320 may include multiple robot apparatus 310a, 310b, 310c, 310d, and others, such as cooperating robots, industrial robots, mobile robots with autonomous driving functions, automatic transport robots, or autonomous driving forklift.
For instance, the first robot apparatus 310a may include at least one robot arm 311, at least one driving device 312 for providing power to the robot arm, at least one sensor 313, at least one processor 314, and memory 315. The processor 314 may be a sub-processor for communicating with the main processor and controlling the components of the robot.
The robot may be a device with one or more actuators and parts. The actuator may convert electrical energy into kinetic energy based on the 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, an air pressure cylinder, and an air pressure motor.
The robot system 320 may further include at least one end effector for performing a task. The at least one end effector may be connected to the end of the robot apparatus (or a robot arm in the robot apparatus) to perform a task.
For example, the end effector may include, but is not limited to, a gripper used to pick or move an object (e.g., a mechanical gripper that picks an object using fixed fingers and a vacuum (or pneumatic) gripper that picks an object using vacuum), a paint spray gun that allows a welding torch robot designed to perform a welding operation to automatically perform paint or coating operations, a camera and sensor used for quality inspection, precision measurement, or visual monitoring of a working environment, a tool holder that allows a robot to perform various assembly operations by mounting a drill, screw driver, or other tools, an electrical and electronic component manipulator used to handle or assemble precise electronic components, or a scraper used to remove a material attached to a surface.
The battery processing automation system may further include at least one sensor 330, worker terminal 340, or at least one processing device 350 that communicates with the computing device 300.
The sensor 330 may include a camera, a radar sensor, an ultrasonic sensor, a depth measurement sensor (e.g., Lidar, TOF camera), a motion detection sensor, a temperature detection sensor, or a thermal image camera for obtaining visual information about the workspace.
The worker terminal 340 may be an electronic device (e.g., mobile device such as a smartphone, a tablet PC, or a display device connected to an automated system) capable of communicating with the computing device 300. The computing device 300 may control the automation system based on the command received from the worker terminal 340.
A processing device 350 may physically or chemically process a battery. The processing device 350 may include a diagnostic device for diagnosing performance of a battery, a charger for charging a battery, or a discharger for discharging a battery, but is not limited thereto.
The battery processing automation system may further include at least one lighting for controlling brightness inside the working cell, a foreign substance suction device for suctioning foreign substances like dust in the working space, and an air circulation device for controlling temperature and humidity in the working space.
The processor 301 may train the robot system 320 using various artificial intelligence learning method.
The processor 301 may utilize imitation learning to train the robot system 320. The imitation learning is a method of learning by observing and mimicking the behaviors of humans or other robots. This method enables the robot to acquire techniques necessary for performing complex tasks through the imitation learning.
The processor 301 may utilize reinforcement learning to train the robot system 320. In reinforcement learning, the robot learns through trial and error by selecting behaviors in a given environment and receives feedback based on the outcomes. This method allows the robot to find an optimal sequences of actions to achieve a specific goal through the reinforcement learning.
The processor 301 may utilize inverse reinforcement learning to train the robot system 320. The inverse reinforcement learning is a learning method of inferring reward functions from observed behavior. Instead of directly training the robot what specific behaviors should be taken to achieve a goal, this method trains the robot to understand and infer the goal based on observed behaviors. Through inverse reinforcement learning, the processor 301 may train the robot to observe human behavior, comprehend its purpose, and then act in a similar manner to achieve comparable goals in a similar situation.
Hereinafter, a detailed description of automation system based on a robot system for providing battery processing automation will follow.
Referring to
The transport device 410 may include a first transport device 410a (e.g., autonomous forklift) for loading, unloading, or taking out the battery in the storage 420 and a second transport device 410b (e.g., mobile robot or conveyor) for transporting the battery to the working space. The transport device 410, equipped with an autonomous driving function, may transport the battery without human intervention (e.g., without the manual manipulation of a person).
The storage 420 may include multiple slots for storing the battery. The storage 420 may further include at least one sensing device for monitoring the battery stored in the slots, such as temperature sensor for detecting potential fire risks.
The storage 420 may further include a transport module for moving batteries in the storage. The transport module may relocate batteries between the slots in the storage and may include a moving mechanism, such as a rail-based system, but is not limited thereto.
The battery 10 may be loaded, transported, or stored on a pallet. The transport device 410 may move the battery loaded on the pallet, and the storage 420 may accommodate the battery loaded on the pallet into at least one slot.
The processor in the computing device 400 may perform an operation of transporting the battery to the storage based on the control signal indicating the need to store the battery. Additionally, the processor may perform an operation of taking out the battery from the storage based on a control signal indicating the need to remove the battery.
The memory in the computing device 400 may store identification information including multiple identifiers corresponding to each of the plurality of slots of the storage. Specifically, the computing device 400 may store an identifier reflecting whether a battery is accommodated and/or information about the battery in each slot.
The processor may set the condition of the battery to be stored in multiple slots within the storage 420. The processor may control the transport device 410 and the storage 420 to take out or store the battery according to the set condition. In this case, the identification information stored in the memory may include the condition information indicating the condition of the battery to be stored in the target slot.
For example, the processor may set the battery storage condition based on whether multiple processing processes to be performed on the battery have been completed. Specifically, the processor may determine the appropriate slot by identifying the battery according to whether the diagnosis/discharge process is performed or whether the dismantling process is performed.
Additionally, the processor may set the battery storage condition based on the processing priority of the battery to be placed in the storage slots. Specifically, the processor may prioritize the processing based on the battery's stability or the extent of the damage to the battery. That is, the battery storage system ensures that a battery with low stability and significant damage is quickly took out and processed.
The battery storage system may be implemented to store the battery separately based on each manufacturer. The battery storage system may categorize the battery by type (for recycling or reuse) and store the battery. The battery storage system can determine a slot to store the battery based on the user input.
Further, the battery storage system may categorize the battery by diagnosis grade and store the battery.
The storage 420 may include multiple areas (zones) partitioned according to a predetermined battery storage condition. The area may include at least one slot.
For example, the first area 421 of the storage may be set to accommodate a battery that meets the first condition (e.g., performing the diagnosis and discharging process). The processor may be set to place the battery satisfying the first condition into the nearest available slot within the first area 421, but it is not limited thereto.
Referring to
The processor may transport the first battery and store it in a storage (s520). The operation of storing the battery may further include the following detailed operations.
Specifically, the processor may obtain battery information corresponding to the first battery (s521). The battery information may include the state or performance of the battery. The battery information may include, but is not limited to, battery profile (manufacturer, type, specification, etc.), battery diagnosis result, battery charging state (e.g., State of Charge (SOC)), battery stability or risk, or battery performance (e.g., State of Health (SOH)).
The processor may obtain battery information based on sensing data obtained from a sensor. Specifically, the processor may analyze the sensing data to determine the state of the battery.
Additionally, the processor may obtain battery information pre-stored in the server. Specifically, the server may receive and store battery information in advance from the outside (e.g., battery supplier's server).
In addition, the processor may determine a target slot out of a plurality of slots based on the identification information or the battery information. (s523). A detailed method for determining the target slot by at least one processor will be described in detail with reference to of
According to one embodiment, the processor may determine the target slot based on whether a battery is already accommodated in the plurality of slots in the storage.
Referring to
The processor may verify whether each slot is occupied by a battery based on the plurality of identifiers (s603). Specifically, the processor may check the battery accommodation information recorded in each identifier. The processor may select one or more unoccupied slots from the plurality of slots (s605).
The processor may determine the selected unoccupied slot as the target slot (s607). In this case, the processor may determine a slot with a high priority among multiple slots as the target slot. Specifically, the processor may determine a slot with the shortest transport distance among multiple slots as the target slot. The processor may determine a slot corresponding to the battery storage condition among multiple slots as the target slot. Alternatively, the processor may arbitrarily select one slot and determine it as the target slot.
In another embodiment, the processor may determine the target slot based on the battery storage condition of the slot.
Referring to
In addition, the processor may compare the battery's state and condition information from the battery information (s703). Specifically, the processor may compare the matching degree of the state (or performance) of the battery identified from the battery information and the condition information stored for each slot.
The processor may determine the slot corresponding to the matching condition information as the target slot (s705). Specifically, the processor may designate the condition related to the battery state based on the battery information, and it may determine the slot to which the condition information corresponding to the designated condition is allocated as the target slot.
According to an embodiment, the processor may determine a target slot to store the battery based on the safety state of the battery.
Referring to
In addition, the processor may assign a priority to the battery, indicating the urgency of processing process (e.g., diagnosis, discharge, or dismantling) after storage, which may also correspond to the order of which battery should be taken out first.
Specifically, the processor may assign a priority based on at least one of a battery profile or battery stability. The processor may assign a priority according to the battery information identified from the battery profile. In addition, the processor may determine the urgency of battery processing based on battery stability and assign priority accordingly.
The processor may designate a battery with low stability as a major monitoring target. In this case, the processor may store this battery in a slot equipped with a monitoring function (e.g., fire detection and emergency taking-out function).
The processor may determine at least one slot corresponding to the assigned priority as the target slot. Specifically, the processor may select at least one slot matching the priority allocated to the battery.
According to one embodiment, it allows for efficient automation of storing large quantities of batteries by improving the processor's calculation efficiency through a battery storage solution based on battery information and slot identification information.
Referring again to
The processor may update the identification information by associating the battery information with the identifier of the target slot. Specifically, the processor may update the identification information by recording information indicating that the battery is stored in the target slot within the corresponding identifier. Additionally, the processor may update the identification information by recording the battery information in the identifier. For example, the processor may record acceptance information of the battery, battery profile, battery state information, or battery performance information in the identifier of the target slot.
Referring to
In addition, the processor may take out the first battery from the storage (s920). This taking-out operation may further include the following detailed operations.
The processor may identify the first slot in which the first battery is stored (s921). The processor does this by referencing identification information that includes multiple identifiers recording battery storage details for the slots.
In addition, the processor may transport the first battery from the first slot (s923). This may involve either moving the palette on which the battery is placed or moving the first slot containing the battery.
A priority order of taking out battery may be set, and the processor may control the taking-out order based on the priority order. The priority of taking out the battery may be assigned based on information about the battery, with higher priority given to batteries that require urgent processing, such as diagnosis, discharging, or dismantling.
Referring to
The processor may monitor the plurality of slots using the sensor value obtained by the predetermined method (s1020). In this case, the processor may be configured to receive a sensor value from at least one sensor installed in the storage. Specifically, the processor may obtain the sensor value from at least one sensor designed to monitor the interior of the plurality of slots in the storage. The sensor may be utilized to determine the stability of the battery stored in the slot. For example, the sensor may include a thermal image camera, a temperature sensor, a smoke sensor, or a thermal sensor, but are not limited thereto.
The processor may determine that at least one of the plurality of slots is in a dangerous state based on the monitoring result (s1030). For example, the processor may determine that the battery is in a dangerous state if the battery temperature exceeds a specified threshold or if a fire is detected in the battery.
Furthermore, the at least one processor may take out at least one slot determined as being in a dangerous state (s1040). The slot accommodating the dangerous battery may be equipped with a fire extinguishing device, which could be activated to extinguish a fire in the battery.
Alternatively, the processor may take out the battery from the slot that is determined as being in a dangerous state, transporting the battery to an emergency space equipped with fire suppression facilities using the transport device.
According to one embodiment, the battery storage solution may continuously monitor the state of the battery, thereby implementing an automation system with enhanced safety.
Referring to
The processor may determine at least one empty slot by analyzing a slot image using an artificial intelligence model (s1130), such as a deep learning model for image analysis like Convolution Neural Network (CNN).
In addition, the processor may determine battery information by analyzing a battery image using an artificial intelligence model (s1140), such as a deep learning model for image analysis like Convolution Neural Network (CNN).
The processor may determine one of the empty slots as the target slot based on the battery information (s1150). The method for determining the target slot has been described above.
The processor may transport the battery to a location corresponding to the target slot using the transport device (s1160).
Referring to
The device 1210 may include a charging device for charging a battery, a discharging device for discharging a battery, or a diagnostic device for diagnosing a battery.
The computing device 1200 may preprocess batteries using the device 1210. Specifically, the computing device 1200 may diagnose a battery using a battery diagnostic device.
The computing device 1200 may obtain the state of charge (SOC) information by measuring the amount of charge remaining in the battery through the battery diagnostic device.
In addition, the computing device 1200 may obtain the state of health (SOH) information indicating the battery's lifespan and health through the battery diagnostic device.
The computing device 1200 may determine if the battery operates within a normal parameter by measuring its voltage and current through the battery diagnostic device.
In addition, the computing device 1200 may diagnose battery aging by measuring internal resistance through the battery diagnostic device. The internal resistance of the battery increases as the battery ages.
The computing device 1200 may measure the temperature data of the battery through the battery diagnostic device. The past temperature data can be used to diagnose past performance and lifespan of the battery.
The computing device 1200 may determine the remaining life of the battery by obtaining its charge and discharge cycle through the battery diagnostic device.
Additionally, the computing device 1200 may provide information about the internal chemical and electrical processes of the battery by applying the electrochemical impedance spectroscopy (EIS) through the battery diagnostic device. Through this, the computing device 1200 may identify a performance degradation pattern or defect of the battery.
The computing device 1200 may measure the actual capacity of the battery by fully charging and discharging the battery through a battery charging and discharging device. The computing device 1200 may check whether the battery performance degrades by comparing the measured actual capacity with the rated capacity of the battery.
The computing device 1200 may measure the balance state of the cells within the battery pack using the battery diagnostic device. Specifically, the computing device 1200 may check whether the voltages of individual cells are balanced.
In addition, the computing device 1200 may charge a battery module (e.g., a battery powering an electric-driven moving body) using a battery charging device.
The computing device 1200 may connect or disconnect the device 1210 to a battery 10 using at least one robot 1220. Specifically, the robot 1220 may connect or disconnect the at least one connection to the battery 10 by applying physical force to the connection (e.g., a connector) in the device 1210.
The method of integrating a battery with a processing device using a robot will be described below.
Referring to
Specifically, the processor in the robot system may obtain sensing data related to the battery from the at least one sensor (s1301).
Sequentially or independently from step s1301, the robot apparatus may grip at least one connector in a processing device using the end effector (s1311).
The processor may set a location corresponding to the at least one port connected to the battery as the target location based on the sensing data. Specifically, the processor may extract a feature value from the sensing data and determine the target location by specifying an area with a feature value matching a predetermined condition.
In this case, the processor may use at least one artificial intelligence model (e.g., CNN) to process the sensing data.
The processor may set the target location based on a predetermined condition, such as the ability to communicate through at least one port. The processor may identify multiple ports from the sensing data, and it may determine which of these multiple ports can communicate with the processing device. The location corresponding to the communicated port is set as the target location.
The processor may activate an alarm if it is impossible to establish a communication connection through the determined port based on sensing data.
Optionally, the processor may obtain battery information and determine an operating parameter for the processing device based on the battery information. For example, the processor may set the operating parameter (e.g., voltage or current) for the battery diagnostic device to diagnose the battery based on the battery's state information. Additionally, the processor may set the operating parameter (e.g., charging time, charging speed) for the battery charging device to charge the battery based on the battery's charging state information.
The processor may control the robot apparatus by generating a trajectory for its movement.
Specifically, the processor may generate the trajectory by determining dynamic parameters (e.g., speed, acceleration, force, torque) at various points until at least a portion (e.g., multiple joints in the robot apparatus) of the robot apparatus reaches a specific destination.
The at least one processor may control the robot apparatus by leveraging the artificial intelligence engine (or model) to perform a specific task. The specific method of training the robot apparatus will be omitted because it has been described above.
Referring to
The processor may generate a trajectory for the robot apparatus to operate based on the dynamic parameter.
The processor may input the generated trajectory into the motion controller of the robot apparatus, and the motion controller may drive the robot apparatus based on the trajectory.
In addition, the processor may obtain a dynamic parameter for the end effector to perform a specific operation (e.g., screwing, picking, etc.)
According to one embodiment, by utilizing the artificial intelligence engine to control the trajectory of the robot apparatus or the end effector's operation, the processor may achieve a task execution with a minimum computational cost.
Referring again to
Specifically, the processor may determine the first trajectory based on the physical relationship between the robot apparatus (or the end effector in the robot apparatus) and the target location based on the sensing data.
The processor may determine at least one of the location coordinates of the target location, the direction from the robot apparatus (or the end effector in the robot apparatus) and the distance from the robot apparatus (or the end effector in the robot apparatus) based on the sensing data. The processor may determine the first trajectory based on at least one of the location coordinates of the target location, the direction from the robot apparatus (or the end effector in the robot apparatus), and the distance from the robot apparatus (or the end effector in the robot apparatus).
The first location may be placed at a predetermined distance from the target location. The processor may be trained to locate the end effector at the first location placed at a predetermined distance from the target location. For example, the processor may measure the distance between the end effector and the target location using a sensor measuring a distance, and it may determine the first location based on the measured distance.
In addition, the processor may determine a second trajectory for connecting the at least one connector gripped by the end effector to the at least one port. (s1304). The robot apparatus may connect at least one connector to at least one port based on the second trajectory. (s1313).
The processor may train an artificial intelligence model such that a portion (e.g., a protrusion for connection) of the connector is inserted into a portion (e.g., a receiving unit for connection) of the at least one port. The processor may control the robot apparatus to apply force for connecting the connector to the port using the trained artificial intelligence model.
The processor may determine that the connection operation is completed when the connector no longer moves. The processor may determine that the connection between the connector and the port is completed when the connector is no longer inserted into the port even though the force is applied to the connector according to the second trajectory, or after a predetermined duration without movement.
Upon completion of the processing task by the processing device, the processor may control the release of the connection between the processing device and the battery. Specifically, the processor may control the robot apparatus by executing a trajectory to grip the connector using the end effector and disconnect the connector from the port.
The battery management system (BMS) may be an electronic system connected to the battery to monitor its state. This battery management system may be built (or integrated) within the battery or connected to the battery externally.
The battery processing system may obtain monitoring information on the battery from the BMS through the communication with the BMS. That is, the BMS diagnoses the battery by receiving monitoring information on the battery through the communication with the BMS.
Accordingly, when the communication with the BMS is possible, the battery processing system may obtain diagnosis information on the battery through the battery diagnosis process.
However, depending on the battery, certain batteries may lack a built-in BMS, or the communication with the BMS may be infeasible (e.g., due to port damage or not applied by the manufacturer).
In this case, the battery processing system needs to diagnose the battery by individually connecting the diagnostic device to multiple ports in the battery to assess various parameters (e.g., SOC, SOH, etc.). However, since the ports may be placed inside the battery, accessing to such ports may require removing or dismantling the battery cover.
When the communication with the BMS is not feasible, the battery processing system needs to perform the battery diagnosis process after dismantling the battery cover and connecting to the available port.
By determining the feasibility of communication with the BMS, the battery processing system may select the appropriate battery processing process, thereby enhancing the efficiency of the battery processing automation.
Referring to
The processor may ascertain the feasibility of establishing communication with the battery management system (BMS). This ascertainment may be based on a predetermined method.
For example, the processor may assess whether communication is feasible by identifying at least one port for connecting to the BMS. Specifically, sensing data from the battery may be utilized to identify or locate the port for connecting to the BMS. Identifying at least one operational port may indicate the possibility for communication with the BMS.
The processor may determine whether physical connection to the port is viable based on the sensing data. This involves assessing the extent of damage to the port corresponding to the BMS. The processor may determine the feasibility of physical connection based on the assessed damage level. The processor may determine that communication with the BMS is infeasible if the damage level of the port equals or exceeds a predetermined threshold.
The processor may determine the availability of the battery profile for communication by identifying authorization with the BMS. The battery processing system may store information on a battery capable of communicating with the BMS in advance. In this case, the processor may determine whether the battery can communicate with the BMS by comparing the pre-stored information with the battery profile.
The processor may select one of predetermined multiple process sequences based on the feasibility of the communication (s1505). The processor may control the robot apparatus to perform a battery processing process in accordance with the selected sequence (s1507).
Referring to
If communication with the battery management system (BMS) is feasible, the processor may initiate the battery diagnosis process according to the first process sequence (s1601).
If communication with the BMS is not feasible, the processor may undertake the battery diagnosis process according to the second process sequence after dismantling the first configuration of the battery (e.g., upper cover of the battery) (s1603).
Referring to
Sequentially or independently from step s1701, the robot apparatus may grip at least one connector in the diagnostic device using the end effector (s1711).
The processor may determine a diagnostic parameter for battery diagnosis based on battery information (s1702). The diagnostic device may set the diagnosis mode based on the determined diagnostic parameter (s1721).
The processor may determine a diagnostic parameter suitable for diagnosing the battery based on the battery information. For example, the processor may determine voltage or current suitable for battery diagnosis, and it may set a diagnostic parameter of the diagnostic device based on the determined voltage or current.
In addition, the processor in the diagnostic device may set the diagnosis mode based on the diagnostic parameter. For example, the diagnostic device may set the diagnosis mode by selecting at least one of the diagnosis modes based on the level of diagnosis (basic diagnosis, detailed diagnosis, precise diagnosis, etc.). The diagnostic device may set the diagnosis mode by selecting at least one of the diagnosis modes based on the diagnosis stage (e.g., initial check, later check).
The processor may set a location corresponding to at least one port connected to the battery as the first target location based on the sensing data (s1703). The port may be located within the battery pack or on the external surface of the battery. If the upper cover of the battery is dismantled, at least one port may be revealed. The port may be electrically connected to the battery management system (BMS) of the battery. The port may be electrically connected to a configuration capable of diagnosing at least one state (e.g., charging state, state of health, voltage state, current state, etc.) of the battery.
The processor may determine a first trajectory for connecting the connector to the port by moving the end effector to a first location corresponding to the first target location (S1704). The robot apparatus may move the end effector based on the first trajectory to connect the connector to the port (s1712).
In addition, the processor may confirm the proper connection between the diagnostic device and the battery (s1705).
The processor may determine the physical connection state between the diagnostic device and the battery. For example, the processor may check whether the physical connection state between the connector in the diagnostic device and the port in the battery is normal. The processor may control the robot apparatus grasping the connector to apply force in the direction of disconnecting the connector from the port. If the connector is not disconnected from the port, the battery and the diagnostic device are properly connected.
In addition, the processor may confirm whether the diagnostic device and the battery are set to be suitable for diagnosis. For example, the processor may diagnose the battery when a predetermined criterion is satisfied by comparing the battery profile and the diagnostic parameter of the diagnostic device.
The processor may determine a second trajectory for the end effector to move to a predetermined second location (S1706).
In this case, the robot apparatus may allow the end effector to place at least one connector and move to a predetermined location based on the second trajectory (s1713).
In parallel, the diagnostic device may perform battery diagnosis based on the determined diagnosis mode (s1722). The processor in the diagnostic device may examine various states of the battery, and it may calculate SOC, SOH, SOP, or SOB of the battery based on the diagnosis result and determine the diagnosis grade.
In addition, the diagnostic device may obtain monitoring information by diagnosing the battery (s1723). The monitoring information may be obtained by the diagnostic device monitoring the state of the battery. Specifically, the processor in the diagnostic device may confirm the safety state of the battery or the presence or abnormality of the diagnostic device. The diagnostic device may monitor the safety state of the battery and the state of the diagnostic device while the diagnosis (or test) is being performed. The diagnostic device may transmit the monitoring information to the battery processing system. For example, the monitoring information may include a charging state of the battery, a voltage state of the battery, or a safety state of the battery.
The processor may assess whether any event occurs based on the monitoring information (s1707), which may include an event occurring during battery diagnosis. The event may include an event according to a diagnosis stage, an event according to a diagnosis function, an event for each test mode, or an emergency stop event, etc.
In addition, the processor may control the system based on the generated event (s1708). Specifically, the processor may control the system by determining feedback according to the type of the event.
For example, if battery instability is detected during an initial inspection, an alarm may be triggered, and the robot apparatus may disconnect the diagnostic device from the battery.
If the risk of fire is sensed, the processor may activate an alarm and control the robot apparatus to perform emergency measures. Specifically, the processor may control the robot apparatus to take a battery into a fire-extinguishing tank (e.g., water tank).
The diagnostic device may complete the diagnostic operation after diagnosing the battery according to step s1723 (s1724). In this case, the diagnosis device may calculate multiple indicators (e.g., SOC, SOH) related to the state of the battery, and it may calculate a diagnosis grade based on the multiple indicators.
In addition, the processor may determine a third trajectory for the end effector to grip at least one connector by moving to a third location corresponding to a portion of the connector (s1709). The robot apparatus may move the end effector to the third location based on the third trajectory and grip the at least one connector (s1714).
The processor may determine a fourth trajectory for disconnecting the at least one connector gripped by the end effector from the battery (s1710). In this case, the robot apparatus may disconnect the connector from the battery based on the fourth trajectory (s1715).
According to one embodiment, the processor may control the diagnosis process to store the battery in the battery storage system, classifying and storing batteries according to their diagnosis grade.
Referring to
In addition, the processor may transport the battery to the battery storage using the transport device (s2003). The battery storage may include multiple slots for accommodating and storing the battery.
The battery storage may be implemented to store the battery according to the battery diagnosis grade. For example, the battery storage may include multiple areas partitioned for each battery diagnosis grade, and the specific area may be set to store the battery matching the specific diagnosis grade.
The processor may determine a target slot to store the battery according to the determined diagnosis grade (s2005). Specifically, the processor may select at least one slot corresponding to the battery diagnosis grade as the target slot. In this case, identification information corresponding to the battery diagnosis grade may be assigned to the target slot. The processor may determine the target slot by determining the storage area on the battery storage corresponding to the battery diagnosis grade.
The processor may determine the target slot using at least one sensor (e.g., a vision sensor). Specifically, the processor may determine the target slot by identifying at least one slot corresponding to the diagnosis grade of the battery to be stored based on the sensing data.
In addition, the processor may determine the target slot based on the communication with the battery storage system. Specifically, the processor may transmit the battery diagnosis grade to the battery storage system, and the battery storage system may determine the target slot corresponding to the diagnosis grade and transmit the location information of the determined target slot to the transport device.
The processor may store the battery in the determined target slot by controlling the transport device and the battery storage (s2007). The specific method of storing the battery in the target slot will be omitted because it has been previously described.
Referring to
Sequentially or independently from step s2101, at least one robot apparatus may grip at least one connector in the discharger using the end effector (s2111).
In addition, the processor may determine a first trajectory for connecting the connector to the port by moving the end effector to the first target location (S2102). The port may be disposed on a portion of the battery. The port may be located on the outer surface of the battery, inside the battery pack, or revealed by dismantling the upper cover of the battery. The port may be electrically connected to at least one cell of the battery.
The robot apparatus may move the end effector along the first trajectory to connect the connector to the port (s2112).
The processor may confirm the proper connection between the discharger and the battery (s2103). The discharger may perform an operation of discharging the battery (s2121).
Furthermore, the processor may control the monitoring of the discharge process and the disconnection of the connector once the discharge is complete (s2104). For example, the processor may monitor parameters such as discharge speed, discharge amount, or remaining discharge time. Upon completion of the discharge, the robot apparatus may disconnect the connector from the battery.
Referring to
Sequentially or independently from the step of s2201, the robot apparatus may grip at least one connector of the charger using its end effector (s2211).
In addition, the processor may determine a first trajectory for connecting the connector to the port by moving the end effector to the first target location (s2202). The port may be on the outer surface of the battery or inside the battery pack. The port may be revealed if the upper cover of the battery is dismantled. The port may be electrically connected to at least one cell of the battery. The port may be on the outer surface of the electric driving movable body (e.g., electric vehicle) on which the battery is mounted.
In this case, the robot apparatus may move the end effector along the first trajectory to connect the connector to the port (s2212). The processor may confirm that the charger and the battery are properly connected (s2203). The charger may charge the battery (s2221).
The processor may control the monitoring operation during charging and the disconnecting operation after charging (s2204). For example, the processor may monitor charging speed, charging amount, charging cost, or remaining charging time. Once the charging is completed, the robot apparatus may disconnect the connector from the battery.
According to one embodiment, the processor in the battery processing system may remove coolant from the battery. Specifically, the processor may identify at least one path forming the coolant flow of the battery based on the sensing data and use the robot apparatus to connect a means for removing coolant to the identified path. The processor may activate the means for removing coolant to extract the coolant from the battery.
For example, the processor may identify one or more holes defining the coolant paths based on sensing data for the battery. The processor may connect a pump to the one or more holes using the robot apparatus. The processor may control the pump to remove the coolant in the coolant path.
According to an embodiment, the processor may manage the dismantling of the battery in which the pre-processing process is performed. Specifically, the processor may transport the diagnosed battery to a location corresponding to the battery dismantling system.
The battery dismantling system that automatically dismantles a battery pack using at least one robot apparatus will be described in detail below.
The battery processing system according to an embodiment performs a battery processing operation using multiple robot apparatus. The battery processing system trains and uses the artificial intelligence engine to control multiple robot apparatus simultaneously, ensuring that their operations do not interfere with each other.
To this end, the battery processing system may train an artificial intelligence engine so that multiple robot apparatus performs tasks in consideration of movements of other robot apparatus.
Referring to
The processor may control multiple robot apparatus based on the determined trajectory. For example, the processor may control the first robot apparatus by transmitting the first trajectory to the first robot apparatus. In addition, the processor may control the second robot apparatus by transmitting the second trajectory to the second robot apparatus.
Referring to
The processor may obtain at least one control value for a power device of the first robot apparatus based on the output data (s4502). Specifically, the processor may drive the power device by transmitting the control value to the power device (e.g., a motor) in the driving unit (e.g., a joint) for driving the first robot apparatus.
The processor may determine the first trajectory for moving the first end effector connected to the first robot apparatus to the target location based on the control value in consideration of the control of the second robot apparatus (s4503). The processor may determine the control state of the second robot apparatus by sensing the movement of the second robot apparatus or receiving the trajectory of the second robot apparatus.
The processor may determine the trajectory of the first robot apparatus in consideration of the location of the second robot apparatus. The processor may ascertain the location of the second robot apparatus based on the sensing data related to the battery. The processor may consider the location of the second robot apparatus and determine the trajectory of the first robot apparatus, which does not correspond to the location of the second robot apparatus. The processor may determine the trajectory of the first robot apparatus, which the trajectory of the first robot apparatus does not overlap with the location of the second robot apparatus.
In addition, the processor may determine the trajectory of the first robot apparatus in consideration of the trajectory of the second robot apparatus. The processor may ascertain the trajectory of the second robot apparatus by receiving the trajectory of the second robot apparatus. For example, the processor may predict the location of the second robot apparatus according to time in consideration of the trajectory of the second robot apparatus, and it may determine the trajectory of the first robot apparatus, which does not correspond to the predicted location of the second robot apparatus. The processor may determine the trajectory of the first robot apparatus which does not overlap with the trajectory of the second robot apparatus.
In addition, the processor may determine the trajectory of the first robot apparatus in consideration of the path of the second robot apparatus. The processor may ascertain the path of the second robot apparatus by predicting the path of the second robot apparatus based on the sensing data. Specifically, the processor may determine the path of the second robot apparatus by predicting the path which the second robot apparatus will move in the future based on the past path of the second robot apparatus from the sensing data. For example, the processor may predict the location of the second robot apparatus over time in consideration of the path of the second robot apparatus, and it may determine the trajectory of the first robot apparatus which does not correspond to the predicted location of the second robot apparatus. The processor may determine the trajectory of the first robot apparatus which does not overlap with the predicted path of the second robot apparatus.
Referring to
In addition, the processor may obtain a second dynamic parameter set for controlling the second robot apparatus based on the sensing data and the first dynamic parameter set (s4603).
When the first robot apparatus is identified based on the sensing data, the processor may generate the trajectory for the second robot apparatus while considering the control of the first robot apparatus to avoid interference. This is because the interference of the first robot apparatus may occur in the trajectory of the second robot apparatus if the first robot apparatus is identified based on the sensing data. That is, the processor may identify the first robot apparatus based on the sensing data, identify the first dynamic parameter set corresponding to the trajectory of the first robot apparatus, and obtain the second dynamic parameter set corresponding to the trajectory of the second robot apparatus based on the first dynamic parameter set.
If the first robot apparatus is not identified based on the sensing data, the processor may generate the trajectory of the second robot apparatus without considering the first robot apparatus's control. This is because the interference of the first robot apparatus does not occur in the trajectory of the second robot apparatus if the first robot apparatus is not identified based on the sensing data.
Referring to
The processor may operate to control multiple robot apparatus based on the sensing data. For example, the first processor may determine a first target location for the first robot apparatus based on the sensing data and transmit this information to the first robot apparatus.
The second processor may generate a first trajectory for moving the first robot apparatus to the first target location. The first trajectory may include a set of dynamic parameters at multiple points.
The second processor may determine the first dynamic parameter set in consideration of the control state of another robot apparatus.
The third processor may determine the second dynamic parameter set for the second robot apparatus's trajectory and transmit the second dynamic parameter set to the second processor.
The second processor may determine the first dynamic parameter set based on the first target location and the received second dynamic parameter set.
The battery processing system, according to an embodiment, may control a plurality of robots without interference using the artificial intelligence model, thereby improving the efficiency of robot control.
The battery processing system may transport the battery into the working chamber (see reference numeral 2325 in
Referring to
The battery processing system may include multiple working chambers. For example, the battery processing system may have a first working chamber 4810 and a second working chamber 4820 that are connected to each other. The first working chamber 4810 and the second working chamber 4820 may be spatially connected to each other. For example, the first working chamber 4810 and the second working chamber 4820 may be spatially connected through a path formed between them.
To facilitate battery transport between these multiple working chambers, the battery processing system may use a mobile robot or a conveyor that spans the various chambers. For example, the mobile robot loaded with the battery may move between the first working chamber 4810 and the second working chamber 4820 via connecting the path.
The multiple working chambers may be implemented to perform different operations. The battery may undergo a first process (e.g., dismantling a battery cover) in the first working chamber 4810. Then, the battery may be transported to the second working chamber 4820 (using the transport device) where it undergoes a second process (e.g., dismantling a battery module).
In addition, the multiple working chambers may be implemented to perform the same operations.
Referring to
The working chamber may be equipped with at least one working space 4920 for performing battery processing. The working space 4920 may be located within the working area of at least one robot apparatus 4910. The battery processing system may control the battery transport apparatus so that the battery entering the working chamber is located within the working space 4920.
The robot apparatus 4910 may be controlled to perform dismantling at least one component of the battery located in the working space 4920. The robot apparatus 4910 may include at least one cooperative robot or an industrial robot, although it is not limited thereto.
For example, the robot apparatus 4910 may be controlled to dismantle the upper cover of the battery. After the cover is removed, the battery can be transported to another chamber for a subsequent process using the transport device. In addition, the robot apparatus 4910 may be controlled to dismantle at least one module of the battery from which the cover is dismantled. In addition, the robot apparatus 4910 may be controlled to dismantle at least one cell of the battery from which the module is dismantled. In this case, the robot apparatus 4910 may be controlled to replace the module or cell with an end effector suitable for dismantling. The dismantled components can then be loaded onto the transport device, which moves these components outside the working chamber.
The method for dismantling battery components (e.g., covers, modules, cells) using the robot apparatus will be omitted because they are described above.
The method according to the embodiment may be implemented in the form of program instructions which can be executable through various computer means and recorded in a computer-readable medium. The computer readable medium may include program instructions, data files, data structures, etc., alone or in combination thereof. The program instructions recorded in the medium may be specially designed and configured for the embodiment or may be known and usable 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 media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and perform program instructions such as ROMs, RAMs, and flash memories. Program instructions can include machine language codes such as those made by a compiler, as well as high-level language codes that can be executed by a computer using an interpreter. The hardware device described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.
Although the present disclosure is described with specific embodiments and drawings, various modifications and variations may be made by those skilled in the art. For example, appropriate results may be achieved even if the described techniques are performed in a different order or manner, and/or components such as the described system, structure, apparatus, and circuit are combined, combined in a different way from the described method, or substituted in a different form or replaced or substituted by other components or equivalents. Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the claims described below.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2023-0100771 | Aug 2023 | KR | national |
| 10-2023-0125412 | Sep 2023 | KR | national |
| 10-2023-0144367 | Oct 2023 | KR | national |
| 10-2023-0169605 | Nov 2023 | KR | national |
| 10-2023-0169606 | Nov 2023 | KR | national |
| 10-2023-0169607 | Nov 2023 | KR | national |
| 10-2023-0169612 | Nov 2023 | KR | national |
