Patent Application
20250042028
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. 10-2023-0169608, filed on 2023 Nov. 29, Korean Patent Application No. 10-2023-0169609, filed on 2023 Nov. 29, Korean Patent Application No. 10-2023-0169610, filed on 2023 Nov. 29, Korean Patent Application No. 10-2023-0169611, filed on 2023 Nov. 29, Korean Patent Application No. 10-2023-0169612, filed on 2023 Nov. 29, Korean Patent Application No. 10-2023-0173261, filed on 2023 Dec. 4 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.
[Battery Dismantling System]
Referring to
The computing device 2300 may control the transport device 2310 or the robot apparatus 2330 to transport the battery 10 and to dismantle at least one component of the battery 10. The computing device 2300 may include a main server (processor) for controlling the battery dismantling system and at least one sub-processor.
The battery dismantling area 2320 may include at least one working chamber 2325, which may be a modular space for dismantling operations. Details of the working chamber 2325 are described below in
The computing device 2300 may move the battery 10 using the transport device 2310.
The transport device 2310, which provides a space for loading a battery, can be a mobile robot as shown in
The computing device 2300 may direct the transport device 2310 to enter the battery dismantling area 2320 and locate it within the work area of the robot apparatus 2330 enabling the robot apparatus 2330 to dismantle the battery 10.
For example, the computing device 2300 may locate the mobile robot carrying the battery 10 within the work area of the robot apparatus 2330 upon entering the battery dismantling area 2320. In this case, the computing device 2300 may control the robot apparatus 2330 to dismantle the battery 10 loaded in the mobile robot.
Similarly, the computing device 2300 may control the conveyor belt loading the battery 10 to enter the battery dismantling area 2320. The battery 10 may be transported along the conveyor belt to work area of the robot apparatus 2330, where the robot apparatus 2300 may be controlled to dismantle the battery 10.
Referring to
The processor may obtain sensing data related to the battery from the at least one sensor (s2402). The sensor may include a vision sensor, a camera sensor, an image sensor, a lidar sensor, or a radar sensor, but is not limited thereto. The sensor may be disposed on the apparatus or be disposed on the working chamber (See reference numeral 2325 in
The processor may identify a location of the first fastening member fixing the first part based on the sensing data and set the location as the first target location (s2403). The first fastening member may be a bolt or a welding part. The processor may determine the first target location by identifying coordinates, directions, and distances of the first fastening member.
The processor may identify the locations of multiple fastening members based on the sensing data and select the first fastening member to be dismantled based on a predetermined criterion. The processor may determine the dismantling order for multiple fastening members in advance. In addition, the processor may select a fastening member close to the robot apparatus among the multiple fastening members. The processor may select two or more fastening members among the multiple fastening members which do not interfere with each other for operations by multiple robot apparatus.
The processor may determine a first trajectory for the first end effector to reach the first target location (s2404). The robot apparatus may move the first end effector to the first location along the first trajectory. Specifically, the processor may generate the first trajectory by inputting sensing data into an artificial intelligence model and obtaining a dynamic parameter set for reaching the first location from at least one layer in the artificial intelligence model.
The first end effector may be a means for dismantling the first fastening member, which may be a tool like a cross driver, a letter driver, or a circular driver for dismantling the bolt.
The processor may control the first end effector to perform a task of dismantling the first fastening member from the battery based on the first condition. (s2405). The robot apparatus may perform the task (s2412).
The first condition may be set based on whether the robot apparatus is ready to perform the task of dismantling the fastening member. The processor may identify a relationship (e.g., a distance, a direction) between the first end effector and the first fastening member, and it may determine that it is ready to perform the task if the relationship meets a predetermined criterion.
The first task may include multiple unit actions for dismantling the first fastening member. Specifically, the first task may include an operation of approaching the first end effector to the first fastening member, an operation of contacting the first end effector and the first fastening member, an operation of dismantling the first fastening member by driving the first end effector, and an operation of disconnecting the first fastening member.
For example, the processor may identify the contact between the first end effector and the first fastening member, and it may control the first fastening member to be dismantled by rotating the first end effector (e.g., rotating the screw).
The processor may simultaneously or sequentially perform multiple first tasks using multiple robot apparatus.
A detailed method for controlling the multiple robot apparatus to perform the task will be described below (
The processor may determine whether the first task is completed by verifying if all the fastening members fixing the first component to the battery housing are dismantled based on the sensing data. Once the task of dismantling all the fastening members is completed, the processor may proceed to dismantle the first component.
The processor may identify the removal of the fastening member, and it may transmit a control signal to replace the end effector. The processor may control the robot apparatus to mount the second end effector for disconnecting the cover. The processor may control the robot apparatus to release the first end effector and mount the second end effector, and it may set the second robot apparatus with the second end effector as the working robot.
Referring to
The processor may determine the number of robot apparatus needed based on the specification (e.g., volume, weight) of the first component. The processor may control the first component to be dismantled using the robot apparatus, or it may control the first component to be dismantled using multiple robot apparatus.
Alternatively, the processor may dismantle the first component using an industrial robot or using an industrial robot equipped with multiple pneumatic grippers.
The second target location for dismantling the first part may be set to a location where is suitable for disconnecting the first part. The battery dismantling system may pre-store a location suitable for dismantling the part based on battery shape and set the second target location accordingly.
The second end effector may be a means for disconnecting the first part from the battery. For example, the second end effector may be a tweezer-type gripper or pneumatic gripper for holding and revealing the part.
The at least one processor may control the second end effector to perform the second task of disconnecting the first part based on the second condition (s2407). In this case, the robot apparatus may perform the second task of disconnecting the first part by the second end effector (s2414).
The second condition may be set based on whether the robot apparatus is ready to perform the task for disconnecting the first part. The processor may identify the relationship (e.g., the distance, the direction) between the second end effector and the first part. If the relationship meets the predetermined criteria, it may determine that the task is ready to perform. The second condition may be set based on the removal of the fastening member.
The second task may include multiple unit actions for disconnecting the first part. The second task may include an operation of approaching the second end effector to the first part, an operation of holding the first part by driving the second end effector, and an operation of disconnecting the first part, but is not limited thereto.
For example, the processor may approach the second end effector to the space between the first part and the battery housing, and it may hold the first part by driving the second end effector. The processor may disconnect the first part by moving the robot apparatus.
In addition, the processor may perform the second task by using multiple robot apparatus. The processor may set multiple target locations and may locate the multiple robot apparatus to each of the multiple target locations. In addition, the multiple robot apparatus may be simultaneously driven to perform the operation of disconnecting the first part.
The processor may control the robot apparatus to perform a task for insulating at the predetermined location of the battery pack. The processor may control the robot apparatus equipped with the third end effector to move to the predetermined location to perform the task for insulating.
For example, the processor may move the third end effector with the insulating material to the predetermined location, and it may perform the insulating operation by applying the insulating material to the target location.
Referring to
The robot system may dismantle the cover by using multiple cooperating robots or an industrial robot equipped with multiple pneumatic grippers.
The processor may obtain sensing data related to the battery from the at least one sensor (s2602).
The processor may identify the location of fastening members fixing the cover based on the sensing data (s2603). The processor may control the dismantling of the fastening member in consideration of the location of the identified fastening member (s2604). The robot apparatus may perform a task of dismantling the fastening member from the battery using the first end effector (s2611).
The processor may identify the removal of the fastening member based on the sensing data (s2605).
Optionally, the processor may dismantle a guide plate positioned between multiple fastening members and the battery. The processor may control at least one robot apparatus to dismantle the guide plate.
The processor may control the disconnection of the cover from the battery housing based on an identified operation (s2606). The robot apparatus may disconnect the cover from the battery housing using the second end effector (s2612).
Referring to
The robot system may determine the dismantling sequence for dismantling multiple modules in the battery pack. The robot system may use multiple robot apparatus to dismantle multiple modules according to a predetermined order. The robot system may determine the dismantling order in advance in consideration of the electrical connection between the multiple modules.
The processor may obtain sensing data related to the battery from the at least one sensor (s2702). The processor may identify the location of the fastening member fixing the module based on the sensing data (s2703).
The processor may control the fastening member to be dismantled in consideration of the location of the identified fastening member (s2704). The robot apparatus may use the first end effector to dismantle the fastening member from the battery (s2711).
The processor may identify the removal of the fastening member based on the sensing data (s2705).
Optionally, the processor may dismantle the guide plate positioned between multiple fastening members and the battery. Specifically, the processor may control the robot apparatus to dismantle the guide plate.
The processor may control the disconnection of the module from the battery housing based on the identified operation (s2706). The robot apparatus may disconnect the module from the battery housing using the second end effector (s2712).
In addition, the processor may control the inversion of the module (s2707). The robot apparatus or a separate mechanism may invert the module (s2713). That is, the processor may perform an operation of inverting the module using a robot apparatus or a separate mechanism.
The processor may perform an operation of dismantling the cooling plate attached to the lower part of the module. The operations of dismantling the cooling plate by the processor using the robot apparatus may be similar to the operations described in
Referring to
The processor may obtain sensing data related to the battery from the at least one sensor (s2802). The processor may identify the location of the at least one fastening member fixing the cell based on the sensing data (s2803).
The processor may control the dismantling of at least one fastening member in consideration of the location of the identified fastening member (s2804). The robot apparatus may perform a task of dismantling the fastening member using the first end effector (s2811).
The processor may identify the removal of the fastening member based on the sensing data (s2805). The processor may dismantle a guide plate positioned between the plurality of fastening members and the battery. The processor may control at least one robot apparatus to dismantle the guide plate.
The processor may control the disconnection of at least one cell from the module housing based on the identified operation (s2806). The robot apparatus may perform a task of disconnecting the cell from the module housing using the second end effector (s2812).
Referring to
The processor may control the transport device to transport at least one dismantled component to the storage (s2902). The storage may include multiple slots for accommodating and storing various battery components. The storage may be configured as the same device as the battery storage described in
The storage may be implemented to store components based on the type of battery components. For example, the storage may have multiple areas partitioned for different types of battery part, with specific areas allocated for specific components.
The processor may determine a target slot within the storage to store the component based on the type of the component. Specifically, the processor may determine the target slot by selecting at least one slot corresponding to the type of the component.
The target slot may be assigned identification information that matches the type of the battery component. The processor may determine the target slot by determining the storage area on the storage corresponding to the type of the component.
The processor may determine the target slot using at least one sensor (e.g., a vision sensor). The processor may determine the target slot by identifying at least one slot corresponding to the type of the component to be stored based on the sensing data.
Additionally, the processor may determine the target slot based on the communication with the battery storage system. The processor may transmit the type of the component to be stored to the battery storage system, which then determine the target slot corresponding to the component. The processor may transmit the location information of the determined target slot to the transport device.
The processor may control the transport device and the storage to store at least one component in the determined target slot (s2904). The method for storing a component in a target slot may be applied to a method of storing a battery.
Referring to
The processor may identify the location of the first fastening member fixing the first part based on the sensing data and set it as the first target location (s3003). The processor may move the at least one robot apparatus to the first target location and dismantle the first fastening member using the robot apparatus (s3004). The processor may identify the location of the second fastening member fixing the first part based on the sensing data and set it as the second target location (s3005). The processor may adjust the location of the transport device or the robot apparatus so that the second target location is within the work area of the robot apparatus (s3006).
For example, the processor may move the second target location into the work area by adjusting the location of the mobile robot, which may involve rotating the mobile robot loaded with the battery. The processor may move the second target location into the work area by adjusting the location of the conveyor belt, which may involve rotating at least a portion (e.g., a rotating conveyor) of the conveyor belt loaded with the battery.
In addition, the processor may move the second target location into the work area by adjusting the location of the at least one robot apparatus. The processor may move the robot apparatus to move the work area itself, and accordingly, the second target location may be located within the work area.
The processor may move the robot apparatus to the second target location and perform a task of dismantling the second fastening member using the robot apparatus (s3007).
Referring to
Referring to
By moving the mobile robot loaded with the battery 10, the transport device 3150 may be controlled to locate the third fastening member 3101c and the fourth fastening member 3101d within the work area of the first robot apparatus 3100a and the second robot apparatus 3100b. Specifically, the processor may adjust the location of the battery by rotating the mobile robot.
By driving the conveyor belt loaded with the battery 10, the transport device 3150 may be controlled to locate the third fastening member 3101c and the fourth fastening member 3101d a within the work area of the first robot apparatus 3100a and the second robot apparatus 3100b. Specifically, the processor may adjust the location of the battery by rotating the conveyor.
By moving the locations of the first robot apparatus 3100a and the second robot apparatus 3100b, the transport device 3150 may be controlled to locate the third fastening member 3101c and the fourth fastening member 3101d within the work area of the first robot apparatus 3100a and the second robot apparatus 3100b.
Referring to
Referring to
The processor may recognize a location where the first wire and the battery are connected based on the sensing data (s3203). The processor may set the first portion of the electric wire at a predetermined distance from the connection location as the first target location (s3204).
The processor may determine a first trajectory for the first end effector to reach the first target location (s3205). The robot apparatus may move the first end effector to the first location based on the first trajectory (s3211).
Referring to
The processor may determine a first trajectory for reaching the first target location and move the first robot apparatus 3300 equipped with the first end effector 3310.
Referring again to
Referring to
The first task may include multiple unit actions for disconnecting the first wire.
Specifically, the first task may involve an operation of approaching the first end effector 3310 to the first portion 3301, an operation of holding the first portion 3301 by driving the first end effector 3310, an operation of disconnecting the first wire from the battery by driving the first robot apparatus 3300, and an operation of placing the first portion 3301 by driving the first end effector 3310, but is not limited thereto.
For example, the processor may confirm that the first portion 3301 is positioned between the first end effectors (e.g., a finger gripper), grip the first portion 3301 by driving the first end effector 3310, and disconnect the first wire by applying a force to the first robot apparatus 3300 in a direction that is disconnected from the battery.
The processor may determine the type of the wire to be dismantled and identify at least one means for dismantling based on the type of the wire. The processor may classify the wire into multiple predetermined types based on the sensing data.
The processor may determine the type of end effect to be used for dismantling based on the type of wire. The processor may determine the end effector to be used based on the type of the wire and control the at least one robot apparatus to mount the determined end effector.
In addition, a task for dismantling the wire may be determined based on the mounted end effector.
For example, if the wire is of the first type which can be cut, the processor may control the robot apparatus to equip the end effector with the cutting member and control the cutting member to cut the first type of electric wire.
If the wire is of the second type which can be cut, the processor may control the robot apparatus to quip the end effector with the gripper and pull out the second type of wire using the gripper. The robot system may dismantle multiple wires using multiple robot apparatus.
In this case, the robot system may designate the sequence of dismantling in advance by multiple robot apparatus. The robot system may determine the sequence of dismantling multiple wires in consideration of the electrical connection relationship and stability, and it may control multiple robot apparatus to dismantle the wires according to the determined sequence.
Referring to
The processor may identify at least one adhesive location where at least one buffer material is attached the battery surface based on the sensing data. The processor may identify the butter material and its adhesive location based on a predetermined method. For example, the processor may identify at least one adhesive location by identifying the adhesive material based on the sensing data. In addition, the processor may recognize the at least one adhesive location by identifying at least one indicator indicating the adhesive location based on the sensing data.
The processor may recognize at least one adhesive location based on the pre-stored battery information, which includes details about the adhesive location of the buffer material. The processor may recognize at least one adhesive location based on the location information.
The processor may set the recognized at least one adhesive location as the first target location (s3404). The processor may determine a first trajectory for the first end effector to reach the first target location (s3405). The robot apparatus may move the first end effector to the first location along the first trajectory (s3411).
Referring to
The processor may determine a first trajectory for reaching the first target location and move the first robot apparatus 3500 equipped with the first end effector 3510. The first end effector 3510 may be a scraper for scraping a specific material.
In this case, the vertical location of the first end effector 3510 that reaches the first location may correspond to the space between the buffer 3501 and the battery.
Referring again to
Referring to
The first task may include multiple unit actions to remove adhesion. Specifically, the first task may include an operation of approaching the first end effector 3510 to at least one adhesive location, an operation of inserting the first end effector 3510 into the space between the buffer 3501 and the battery (which may include reciprocating operations for removing adhesive force), and an operation of pulling out the first end effector 3510 from the space between the buffer 3501 and the battery.
In addition, the processor may control the first robot apparatus 3500 to insert the first end effector 3510 into the space (between the buffer 3501 and the battery) around at least one adhesive location. The processor may remove the adhesive force between the buffer 3501 and the battery by controlling the first robot apparatus 3500 to move the first end effector 3510 within a space between the buffer 3501 and the battery.
The processor may verify the completion of the first task based on a predetermined criterion. Specifically, the processor may confirm the removal of the adhesive force between the buffer material and the battery surface. For example, the processor may identify whether the adhesive force between the buffer material and the battery surface has been removed based on the frictional force applying to the first end effector 3510. The processor may determine that the adhesive force between the buffer material and the battery surface is removed when the frictional force applying to the first end effector 3510 falls below a predetermined threshold.
Referring again to
Referring to
The second task may include multiple unit actions for disconnecting the buffer material. Specifically, the second task may involves inserting the first end effector 3510 into the space between the buffer material 3501 and the battery, and then lifting the first end effector 3510 to disconnect the buffer material 3501. The processor may control to perform the second task using a second end effector different from the first end effector.
Referring to
The second task may include multiple unit actions for disconnecting the buffer material. The second task may include an operation of driving the second end effector 3520 to grip the buffer material 3501, an operation of moving the buffer material 3501 from the battery by applying a force on the second robot apparatus 3550, and an operation of moving the second robot apparatus 3550 to disconnect the buffer material 3501.
The processor may control the first robot apparatus 3500 to release the first end effector 3510 and mount the second end effector, and it may perform a second task for disconnecting the buffer material using the first robot apparatus 3500 equipped with the second end effector.
As described in
For example, the robot system may dismantle the fastening member using a robot apparatus through the artificial intelligence model that learns control values to perform all operations for executing tasks.
Referring to
In addition, the processor may obtain at least one control value for controlling the operation of the robot apparatus from at least one layer of the trained model (s3602). The processor may control the robot apparatus to perform multiple unit actions based on at least one control value (s3603). The processor may perform tasks by performing multiple unit actions using at least one robot apparatus (s3604).
For the robot system to dismantle the battery, it is necessary to accurately determine the pose of the battery prior to the dismantling operation according to
The robot system may estimate the pose of the battery by processing the sensing data obtained from at least one sensor. In addition, the processor may calibrate the pose to be suitable for dismantling the battery based on the estimated pose.
Referring to
The processor may subsequently adjust the location of the at least one robot apparatus or the transport device such that the locational relationship satisfies a predetermined criterion (s3704).
The processor may estimate and calibrate the pose of the battery by identifying the reference location on the battery. The processor may adjust the location of the battery so that the reference location on the battery aligns with a predetermined locational relationship (e.g., distance, direction) relative to the robot apparatus, or it may adjust the location of the robot apparatus.
For example, the processor may calibrate the pose by adjusting the location of the battery or by controlling the transport device (e.g., a mobile robot, a conveyor). The processor may calibrate the pose by adjusting the location of the at least one robot apparatus.
The detailed configuration of the battery packs varies depending on each manufacturer. There are various types of components, quantity, placement, and the size of the battery packs.
For instance, one manufacturer may design the battery packs on a module basis, while another manufacturer may design the battery packs on a cell basis. The shapes of the modules and the cells are often different for each battery.
That is, the battery processing process needs to be performed differently depending on the battery profile (e.g., the battery manufacturer, kind, type, specification). In addition, it is necessary to train the robot artificial intelligence engine based on various battery types to implement a system for dismantling the battery in various environments.
Referring to
The at least one processor in the battery processing system may obtain sensing data related to the battery from the at least one sensor. In addition, the processor may identify a battery profile by extracting at least one feature in the battery based on the sensing data.
For example, referring to
Referring again to
Specifically, the processor may identify the appropriate processing process for the target battery based on the pre-stored matching information. The matching information may include information about the processing process necessary for each profile of the battery, allowing the processor to identify the corresponding processing process by comparing the battery's profile and the matching information.
In addition, the matching information may further include end effector information used when during a specific process. Specifically, the processor may identify the end effector information used when processing the target battery according to a specific process. In this case, the processor may transmit the end effector information to a relevant processing facility. Additionally, the matching information may include location information of a destination needed to transport the battery for a specific process. Specifically, the processor may identify destination location needed to process the target battery according to the specific process. The processor may transmit the location information to the transport device loaded with the battery.
Table 1 below shows an example of a configuration of matching information.
In addition, the processor may determine at least one processing process to be performed on the target battery according to the comparison result (s3803). The processor may physically process the target battery based on the determined processing process (s3804).
Specifically, the processor may set the determined location for performing the processing process as the destination of the transport apparatus and transport the battery using the transport apparatus. The processor may control the dismantling of a configuration of the target battery by using the robot apparatus.
In addition, the processor may determine whether the battery processing process is performed based on the battery profile. Specifically, the processor may identify whether to perform the processing process on the target battery, and it may determine the processing process to be performed based on the identified process. For example, the processor may identify that the target battery has not been discharged based on the target battery profile and discharge the target battery.
Referring to
In addition, the processor may set at least one location corresponding to the determined processing process as the destination of the transport apparatus (s4002). In this case, the transport device may be controlled to autonomously travel to the designated destination.
Referring to
The processor may preset the end effector to be used by the first robot apparatus 4121 to perform the first process (s4103). The first robot apparatus 4121 may be controlled to mount the designated end effector.
In addition, the processor may perform the first process at the first location using the first robot apparatus 4121. For example, the processor may control the diagnostic device to automatically connect to the battery using the first robot apparatus 4121 to perform the battery diagnosis process.
The processor may perform the second process on the battery based on the profile of the target battery. For example, the second process may be a battery dismantling process. The processor may set the second location for performing the second process as the destination of the transport device 4110. The transport device 4110 may move to the second location (s4105).
In addition, the processor may preset the end effector to be used by the second robot apparatus 4122 to perform the second process (s4106). The second robot apparatus 4122 may be controlled to mount the designated end effector.
In addition, the processor may perform the second process at the second location by using the second robot apparatus 4122. For example, the processor may use the second robot apparatus 4122 to unload the battery during the battery dismantling process.
The battery may have different levels for dismantling depending on the manufacturer or the type of battery. A certain battery may be configured on a module basis, so that it may be necessary to dismantle modules. Another type of battery may be configured on a cell basis, so that it may be necessary to dismantle cells. In addition, the shapes of the cells and modules may vary depending on each battery. Accordingly, the type of end effector used to dismantle a module or cell and dynamic parameters applied to the end effector may be trained differently.
The battery profile or its current state of the battery varies. In the case of a waste battery requiring dismantling, there may be damage to the appearance or the stability of the battery. The processing system for processing such a waste battery needs to accurately recognize the safety state of the battery and perform an appropriate processing process. The battery processing system may recognize the state of the battery and the status of the processing process, and it may provide feedback based on such recognition.
Referring to
The processor may identify whether at least a portion of the target battery is damaged based on the sensing data. Specifically, the processor may recognize the appearance (e.g., shape) of the battery based on the sensing data, and it may identify if the battery is damaged by comparing the recognized appearance with the original shape of the target battery. For example, the processor may identify if the battery is damaged by checking for discrepancies between the original shape of the target battery and the recognized shape based on the sensing data. The processor may identify the damage location of the battery by checking at least a portion different from the original shape of the battery.
In addition, the processor may determine whether to perform the task according to the extent of damage (s4203). Specifically, if the battery is damaged beyond a certain level, the processor may determine that the task for the battery processing process cannot be performed. Conversely, if the battery is damaged below a specific level, the processor may determine that the task can be performed. The processor may perform the task for the battery processing process by using the at least one robot apparatus.
In addition, the processor may output an alarm if the task cannot be performable (s4204). That is, the processor may provide the user with feedback on the situation where the task cannot be performed, thereby providing an automated process with guaranteed safety.
The processor may determine the feasibility of the task performance based on sensing data. Specifically, the processor may calculate the similarity between the sensing data and the trained cases to determine if the task can be performed. For example, the processor may determine that task performance is not feasible if the sensing data significantly deviates from the trained case (i.e., it is an outlier). In this case, the processor may also train the artificial intelligence engine with the failure case to improve future task performance.
Referring to
The robot apparatus may perform at least one unit action for accomplish the first task (s4311). The processor may determine that a problem occurs in the performance of the first task if the task performance is not completed even after performing the unit actions beyond a predetermined criterion (s4303).
Specifically, the processor may determine that a problem occurs in performing the first task if the first task is not completed despite the unit actions for the first task being repeated more than a predetermined number of times.
For example, the processor may perform a first unit action of dismantling the bolt by rotating the end effector to dismantle the bolt. If the bolt remains undismantled after several attempts beyond the predetermined criterion, the processor may determine that there is a problem in performing the task.
The processor may stop the operation of the robot apparatus (s4312) and output the alarm (s4304).
The processor may further train the artificial intelligence engine on the failure case to improve future task performance. Specifically, the processor may additionally train using the robot apparatus with respect to the failure case using techniques such as Imitation Learning or Inverse Reinforcement Learning.
For example, the processor may additionally train the dismantling operation of the case where the guide plate is damaged and the bolt is difficult to dismantle, or the bolt itself is damaged and difficult to dismantle.
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.
[Example of Working Chamber Implementation]
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 |
| 10-2023-0169608 | Nov 2023 | KR | national |
| 10-2023-0169609 | Nov 2023 | KR | national |
| 10-2023-0169610 | Nov 2023 | KR | national |
| 10-2023-0169611 | Nov 2023 | KR | national |
| 10-2023-0169612 | Nov 2023 | KR | national |
| 10-2023-0173261 | Dec 2023 | KR | national |
