Battery cell tab folding detection method and system, integrated robot, and storage medium

Abstract

The application belongs to the technical field of automatic detection of lithium batteries, and particularly relates to a cell tab folding detection method and system, an integrated robot and a storage medium. The cell tab folding detection method comprises the following steps: a control module calculates a pose deviation of a tab center relative to a preset detection path, and corrects the preset detection path to form a corresponding motion interpolation path; the control module drives a six-axis collaborative robot body to clamp a cell to perform a plurality of reciprocating plugging actions, and collects multi-focus images of the inner and outer sides of the tab through each tab folding detection module to generate a tab feature image that is clear in the whole domain; the application integrates a visual detection module into the six-axis collaborative robot body, significantly reduces the size of the equipment, and improves the flexibility of the production line. Through flexible grabbing, real-time path correction and multiple reciprocating plugging detection actions, high-precision and fully-automatic online detection of the folding state of the cell tab is realized.

Description

Technical Field

[0001] This invention belongs to the field of automated testing technology for lithium batteries, specifically relating to a method, system, integrated robot, and storage medium for detecting cell tab folding. Background Technology

[0002] In automated lithium battery production systems, the cell tabs are crucial conductive structures connecting the internal windings of the cell to the external circuitry. Their molding quality, structural integrity, and folding state directly determine the battery's internal resistance consistency, welding yield, and long-term cycle safety performance. As power batteries rapidly upgrade towards higher energy density, higher safety, and higher reliability, the inspection requirements for cell tabs are constantly increasing. It is necessary not only to identify macroscopic defects such as tab breakage and severe folding, but also to reliably detect minute defects such as cutting burrs, microcracks, fine wrinkles, and interlayer misalignment. Traditional inspection methods are no longer adequate for today's stringent quality control standards.

[0003] However, the mechanical structure of the electrode inspection equipment widely used in the industry today severely restricts the flexibility and accuracy of the inspection. Existing inspection solutions are mostly based on fixed-station designs, with the vision system and the execution mechanism being independent of each other. In order to obtain image information of the electrode at different angles, it is necessary to rely on complex external mechanical transmission and flipping mechanisms to forcibly change the posture of the battery cell. This results in a large overall size of equipment and a cumbersome mechanical structure, which not only occupies valuable production line space but also makes the debugging and maintenance of the equipment extremely inconvenient.

[0004] Meanwhile, due to the lack of integrated collaboration between the robot and the detection unit, traditional equipment struggles to achieve adaptive and precise positioning and multi-view scanning when faced with tiny and easily deformable features like tabs, making it prone to detection blind spots.

[0005] Therefore, there is an urgent need to develop a new method, system, integrated robot, and storage medium for detecting the folding of battery cell tabs, in order to solve the technical problems of existing lithium battery tab detection equipment being bulky, inflexible, and inconvenient to change, as well as the traditional machine vision being susceptible to interference from the high reflectivity and complex texture of the tab metal, the easy misdetection and missed detection of small defects, and the inability to accurately quantify the folding state of the tabs.

[0006] It should be noted that the information disclosed in this background section is only for understanding the background technology of the present application concept, and therefore, the above description is not considered to constitute prior art information. Summary of the Invention

[0007] This disclosure provides at least one method, system, integrated robot, and storage medium for detecting the flipping of battery cell tabs.

[0008] In a first aspect, embodiments of this disclosure provide a method for detecting the tab folding of a battery cell, comprising: a control module driving a six-axis collaborative robot to flexibly grasp a battery cell from a material table to transfer the battery cell to a detection starting point; the control module acquiring an image of the tab of the battery cell at the detection starting point through a positioning detection module to calculate the pose deviation of the tab center relative to a preset detection path, and correcting the preset detection path to form a corresponding motion interpolation path; the control module acquiring the identification information of the battery cell through a barcode scanning module, and the control module controlling the six-axis collaborative robot to rigidly grasp the battery cell; and the control module driving the six-axis collaborative robot to clamp the battery cell around an axis. The robot rotates along a linear axis and acquires images of the battery cell's appearance from various sides using an appearance inspection module to obtain corresponding appearance defect analysis results. The control module drives the six-axis collaborative robot to perform several reciprocating insertion and removal actions according to the motion interpolation path. It also acquires multi-focal images of the inner and outer sides of the tabs through each tab folding detection module, and fuses these multi-focal images to generate a clear tab feature image across the entire domain, thereby obtaining the corresponding tab defect analysis results. Finally, the control module controls the six-axis collaborative robot to flexibly grasp the battery cell and, based on the appearance defect analysis results and tab defect analysis results, places the battery cell back into the corresponding placement area on the material platform.

[0009] In one optional implementation, the method by which the control module acquires an image of the battery cell's tabs at the detection starting point through a positioning detection module to calculate the pose deviation of the tab center relative to a preset detection path, and corrects the preset detection path to form a corresponding motion interpolation path includes: the control module acquiring the actual lateral width or actual center position of the tab stack in the tab image and comparing it with a preset standard value to calculate the pose deviation of the battery cell in the horizontal direction; the control module dynamically compensates the pose deviation to the preset detection path of the six-axis collaborative robot body to form a corresponding motion interpolation path.

[0010] In one optional implementation, the method of the control module driving the six-axis collaborative robot body to perform a number of reciprocating insertion and removal actions to hold the battery cell according to the motion interpolation path, and acquiring multifocal images of the inner and outer sides of the electrode tab through the electrode tab folding detection module includes: the control module driving the six-axis collaborative robot body to perform at least four reciprocating insertion and removal actions to hold the battery cell according to the motion interpolation path, that is, the control module drives the six-axis collaborative robot body to move the battery cell to the left side of two side-by-side electrode tab folding detection modules, so that the battery cell is pushed in horizontally, so that the outer side of the right electrode tab of the battery cell enters the detection area of ​​the two electrode tab folding detection modules, and maintaining this position for a preset time until the corresponding image is acquired. The robot then exits along the original path; the control module drives the six-axis collaborative robot to move the battery cell horizontally to the right until the prism of the left tab folding detection module is positioned between the two tabs of the battery cell, in order to acquire the inner image of the left tab, and then exits along the original path; the control module drives the six-axis collaborative robot to move the battery cell horizontally to the right until the prism of the right tab folding detection module is positioned between the two tabs of the battery cell, in order to acquire the inner image of the right tab, and then exits along the original path; the control module drives the six-axis collaborative robot to move the battery cell horizontally to the right until the battery cell is completely positioned to the right of the tab folding detection module, in order to acquire the outer image of the left tab, and then exits along the original path.

[0011] In one optional embodiment, the six-axis collaborative robot body is provided with an end effector, which includes: a gripping base, a gripper cylinder, and a floating gripper; the floating gripper is connected to the gripping base through an elastic reset element, and the gripper cylinder is connected to the elastic reset element; the control module controls the gripper cylinder to drive the floating gripper to flexibly or rigidly grasp the battery cell through a solenoid valve.

[0012] In one alternative implementation, the scanning module is configured to read the QR code information on the surface of the battery cell; the positioning detection module is configured to capture an image of the electrode tab; the appearance detection module is configured to capture an image of the battery cell appearance; and the electrode tab folding detection module is equipped with a ring light source, a liquid lens, a camera, and a prism to acquire images of the inside and outside of the electrode tab.

[0013] Secondly, this disclosure also provides a battery cell tab folding detection system, comprising: a control module, a six-axis collaborative robot body, a material platform, a positioning detection module, a barcode scanning module, an appearance inspection module, and tab folding detection modules; wherein the positioning detection module, barcode scanning module, appearance inspection module, and each tab folding detection module are mounted on the six-axis collaborative robot body, and the six-axis collaborative robot body, positioning detection module, barcode scanning module, appearance inspection module, and each tab folding detection module are electrically connected to the control module; the control module is configured to drive the six-axis collaborative robot body to flexibly grasp the battery cell from the material platform to transfer the battery cell to the detection starting point; the control module is configured to acquire the tab image of the battery cell at the detection starting point through the positioning detection module to calculate the pose deviation of the tab center relative to a preset detection path, and correct the preset detection path to form a corresponding motion interpolation path. The control module is configured to acquire the identification information of the battery cell through the barcode scanning module, and control the six-axis collaborative robot to rigidly grasp the battery cell; the control module is configured to drive the six-axis collaborative robot to rotate the battery cell around the axis while holding it, and to collect appearance images of each side of the battery cell through the appearance inspection module to obtain the corresponding appearance defect analysis results; the control module is configured to drive the six-axis collaborative robot to perform several reciprocating insertion and removal actions while holding the battery cell according to the motion interpolation path, and to collect multi-focal images of the inner and outer sides of the tabs through each tab folding detection module, and to fuse the multi-focal images of the inner and outer sides of the tabs to generate a clear tab feature image across the entire domain, thereby obtaining the corresponding tab defect analysis results; and the control module is configured to control the six-axis collaborative robot to flexibly grasp the battery cell, and to place the battery cell back into the corresponding placement area of ​​the material platform according to the appearance defect analysis results and the tab defect analysis results.

[0014] In one alternative implementation, the control module performs the steps of the cell tab flipping detection method described above.

[0015] Thirdly, this disclosure also provides an integrated robot, comprising: a control module, a six-axis collaborative robot body, a positioning and detection module, a barcode scanning module, an appearance inspection module, and a tab folding detection module; wherein the positioning and detection module, the barcode scanning module, the appearance inspection module, and each tab folding detection module are mounted on the six-axis collaborative robot body, and the six-axis collaborative robot body, the positioning and detection module, the barcode scanning module, the appearance inspection module, and each tab folding detection module are electrically connected to the control module; the control module is configured to drive the six-axis collaborative robot body to flexibly grasp the battery cell to move the battery cell to the detection starting point; the control module is configured to acquire the tab image of the battery cell at the detection starting point through the positioning and detection module to calculate the pose deviation of the tab center relative to a preset detection path, and correct the preset detection path to form a corresponding motion interpolation path; the control module... The module is configured to acquire the identification information of the battery cell through a barcode scanning module, and the control module controls the six-axis collaborative robot to rigidly grasp the battery cell; the control module is configured to drive the six-axis collaborative robot to rotate the battery cell around its axis while holding it, and to acquire appearance images of each side of the battery cell through an appearance inspection module to obtain corresponding appearance defect analysis results; the control module is configured to drive the six-axis collaborative robot to perform several reciprocating insertion and removal actions while holding the battery cell according to a motion interpolation path, and to acquire multi-focal images of the inner and outer sides of the electrode through each tab folding detection module, and to fuse the multi-focal images of the inner and outer sides of the electrode to generate a clear tab feature image across the entire domain, thereby obtaining the corresponding tab defect analysis results; and the control module is configured to control the six-axis collaborative robot to flexibly grasp the battery cell, and to place the battery cell at the corresponding position according to the appearance defect analysis results and the tab defect analysis results.

[0016] In one alternative implementation, the control module performs the steps of the cell tab flipping detection method described above.

[0017] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a control module, implements the steps of the above-described cell tab flipping detection method.

[0018] The beneficial effects of this invention are that by integrating the vision inspection module into the body of a six-axis collaborative robot, the high degree of freedom of the robot replaces the complex mechanical flipping and translation mechanisms in traditional equipment, significantly reducing the size of the equipment and improving the flexibility of the production line. Through flexible gripping, real-time path correction, and multiple reciprocating insertion and removal detection actions, it achieves high-precision, fully automatic online detection of the cell tab flipping state, which can eliminate cumbersome mechanical structure adjustments and facilitates easy model changeover, meeting the needs of large-scale intelligent manufacturing of lithium batteries.

[0019] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 A schematic diagram illustrating the principle of a cell tab flipping detection method provided in this embodiment of the disclosure; Figure 2 A flowchart illustrating a cell tab flipping detection method provided in this embodiment of the disclosure; Figure 3 A flowchart illustrating a process for generating a fully clear polar ear feature image, as provided in this embodiment of the disclosure; Figure 4 A structural diagram of a battery cell tab flipping detection system provided in an embodiment of this disclosure; Figure 5 This is a structural diagram of a tab folding detection module provided in an embodiment of the present disclosure.

[0023] In the picture: 1. Six-axis collaborative robot body; 2. Material table; 3. Positioning and appearance inspection integrated module; 4. Barcode scanning module; 5. Electrode folding detection module; 51. Prism; 6. Battery cell; 61. Electrode. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The terminology used herein is for the purpose of describing specific exemplary configurations only and is not intended to be limiting. As used herein, the singular articles “a,” “an,” and “the” may also be intended to include plural forms unless otherwise clearly stated herein. The terms “comprising,” “including,” and “having” are inclusive and thus specify the presence of features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein should not be construed as requiring them to be performed in the specific order discussed or shown, unless specifically identified as such. Additional or alternative steps may be employed.

[0026] As used herein, the phrases “in one embodiment,” “according to one embodiment,” “in some embodiments,” etc., generally refer to the fact that a particular feature, structure, or characteristic following the phrase can be included in at least one embodiment of this disclosure. Therefore, a particular feature, structure, or characteristic can be included in more than one embodiment of this disclosure, such that these phrases do not necessarily refer to the same embodiment. As used herein, the terms “example,” “exemplary,” etc., are used to “serve as an example, instance, or illustration.” Any implementation, aspect, or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or superior to other implementations, aspects, or designs. Rather, the use of the terms “example,” “exemplary,” etc., is intended to present concepts in a specific manner.

[0027] Research has revealed that in automated lithium battery production systems, the cell tabs are crucial conductive structures connecting the internal windings of the cell to the external circuitry. Their molding quality, structural integrity, and folding state directly determine the battery's internal resistance consistency, welding yield, and long-term cycle safety performance. As power batteries rapidly upgrade towards higher energy density, higher safety, and higher reliability, the requirements for cell tab inspection are constantly increasing. This necessitates not only identifying macroscopic defects such as tab breakage and severe folding, but also consistently detecting minute defects such as cutting burrs, microcracks, fine wrinkles, and interlayer misalignment. Traditional inspection methods are no longer adequate for today's stringent quality control standards. However, the mechanical structure of currently widely used tab inspection equipment severely restricts the flexibility and accuracy of inspection. Existing inspection solutions are mostly based on fixed-station designs, with the vision system and execution mechanism operating independently. To obtain image information from different angles of the cell tabs, complex external mechanical transmission and flipping mechanisms are required to forcibly change the cell's posture, resulting in bulky equipment with cumbersome mechanical structures. This not only occupies valuable production line space but also makes equipment debugging and maintenance extremely inconvenient. More importantly, this rigid mechanical structure results in extremely poor equipment flexibility. When production models change, it often requires shutdown and readjustment or even replacement of mechanical fixtures and transmission paths, leading to long changeover cycles and making it difficult to meet the high flexibility and rapid switching requirements of modern intelligent lithium battery manufacturing. Furthermore, due to the lack of integrated collaboration between robots and inspection units, traditional equipment struggles to achieve adaptive, precise positioning and multi-view scanning when dealing with tiny and easily deformable features like electrode tabs, easily creating blind spots in inspection.

[0028] Based on the above research, this disclosure provides a method, system, integrated robot, and storage medium for detecting the folding of battery cell tabs. By integrating a vision inspection module into the body of a six-axis collaborative robot, the robot's high degree of freedom replaces the complex mechanical flipping and translation mechanisms in traditional equipment, significantly reducing equipment size and improving production line flexibility. Through flexible gripping, real-time path correction, and multiple reciprocating insertion and removal detection actions, high-precision, fully automatic online detection of the folding state of battery cell tabs is achieved. This eliminates the need for cumbersome mechanical structure adjustments and facilitates convenient model changeover, meeting the needs of large-scale intelligent manufacturing of lithium batteries.

[0029] The shortcomings of the above solutions are the result of the inventor's practical experience and careful research. Therefore, the discovery process of the above problems and the solutions proposed in this disclosure below should be considered as the inventor's contribution to this disclosure.

[0030] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0031] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0032] like Figures 1 to 5 As shown, at least one embodiment provides a method for detecting the flipping of battery cell tabs, which includes: a control module driving a six-axis collaborative robot body 1 to flexibly grasp a battery cell 6 from a material table 2 to transfer the battery cell 6 to a detection starting point; the control module acquiring an image of the tab 61 of the battery cell 6 at the detection starting point through a positioning detection module to calculate the pose deviation of the center of the tab 61 relative to a preset detection path, and correcting the preset detection path to form a corresponding motion interpolation path; the control module acquiring the identification information of the battery cell 6 through a barcode scanning module 4, and the control module controlling the six-axis collaborative robot body 1 to rigidly grasp the battery cell 6; the control module driving the six-axis collaborative robot body 1 to rotate the battery cell 6 around an axis. The robot rotates and collects images of the appearance of each side of the battery cell 6 through the appearance inspection module to obtain the corresponding appearance defect analysis results; the control module drives the six-axis collaborative robot body 1 to clamp the battery cell 6 and perform several reciprocating insertion and removal actions according to the motion interpolation path, and collects multi-focal images of the inner and outer sides of the tabs 61 through the tab flipping detection module 5, and fuses the multi-focal images of the inner and outer sides of the tabs 61 to generate a clear feature image of the tabs 61 in the whole domain, thereby obtaining the corresponding tab 61 defect analysis results; and the control module controls the six-axis collaborative robot body 1 to flexibly grasp the battery cell 6, and puts the battery cell 6 back into the corresponding placement area of ​​the material table 2 according to the appearance defect analysis results and the tab 61 defect analysis results.

[0033] Specifically, the positioning detection module and the appearance detection module are integrated into the same positioning and appearance detection integrated module 3.

[0034] In at least one embodiment, by integrating the vision inspection module into the body 1 of a six-axis collaborative robot, the robot's high degree of freedom replaces the complex mechanical flipping and translation mechanisms in traditional equipment, significantly reducing the size of the equipment and improving the flexibility of the production line. Through flexible gripping, real-time path correction, and multiple reciprocating insertion and removal detection actions, high-precision, fully automatic online detection of the folded state of the 6 tabs 61 of the battery cell is achieved. This eliminates the need for cumbersome mechanical structure adjustments and facilitates easy model changeover, meeting the needs of large-scale intelligent manufacturing of lithium batteries.

[0035] Specifically, material station 2 is divided into an inspection area, an NG placement area (for unqualified battery cells 6) and an OK placement area (for qualified battery cells 6).

[0036] Specifically, the positioning detection module, the barcode scanning module 4, the appearance inspection module, and the tab folding detection module 5 are integrated on the six-axis collaborative robot body 1. During the movement of the battery cell 6 while being held by the six-axis collaborative robot body 1, the positioning of the tab 61, the barcode scanning of the battery cell 6, the appearance image acquisition, and the multi-focal image acquisition of the tab 61 are completed sequentially.

[0037] Specifically, the control module is integrated with the positioning and detection module, the barcode scanning module 4, the appearance inspection module, the tab folding detection module 5, and the six-axis collaborative robot body 1. It receives and processes image data to realize defect identification, tab 61 analysis, and sorting control of the six-axis collaborative robot body 1. At the same time, it feeds back control commands to the six-axis collaborative robot body 1 to form a closed-loop control.

[0038] In at least one embodiment, the method by which the control module acquires an image of the tabs 61 of the battery cell 6 at the detection starting point through the positioning detection module, calculates the pose deviation of the center of the tabs 61 relative to the preset detection path, and corrects the preset detection path to form a corresponding motion interpolation path includes: the control module obtains the actual lateral width or actual center position of the tab stack in the tab 61 image, and compares it with a preset standard value to calculate the pose deviation of the battery cell 6 in the horizontal direction; the control module dynamically compensates the pose deviation to the preset detection path of the six-axis collaborative robot body 1 to form a corresponding motion interpolation path.

[0039] Specifically, the six-axis collaborative robot body 1 moves the grasped battery cell 6 into the field of view of the positioning and detection module. The positioning and detection module acquires the stacked image of the tabs 61 and transmits it to the control module. The control module performs binarization processing and edge detection on the image, measures the actual lateral width or actual center position of the stacked tabs 61, compares the measurement result with the center coordinates of the preset standard detection path, and calculates the pose deviation of the battery cell 6 in the horizontal direction. This pose deviation is fed back to the control module in real time. Based on this, the control module dynamically updates the motion interpolation path in the subsequent tab 61 folding detection process, so that when the battery cell 6 is pushed in later, the gap between the two tabs 61 can be aligned with the insertion position of the prism 51, avoiding contact between the prism 51 and the tabs 61.

[0040] It should be noted that the image processing and deviation calculation method given in this embodiment is only an example. Those skilled in the art can use other edge detection, width measurement or deviation calculation methods instead, as long as they can obtain the pose deviation amount from the stacked image of the tabs 61 and correct the motion path of the six-axis collaborative robot body 1 in real time.

[0041] Specifically, the positioning and detection module acquires stacked images of the tabs 61 of the battery cell 6 and transmits them to the control module. The control module performs preprocessing on the stacked tab 61 images: converting them to grayscale images, performing binarization using an adaptive threshold, and extracting the edge contours of the stacked tab 61 region using the Canny edge detection operator. Based on the edge detection results, the actual lateral width of the stacked tabs 61 is measured. and the geometric center of the stacked area The width Defined as the actual length of the horizontal pixel distance between the left and right edges of the stacked tab 61, after calibration conversion, with the center position... These are the actual spatial coordinates of the midpoints of the left and right edges. The control module reads the preset standard width and standard center position for the current cell model 6 from the memory. Standard width The standard center position is determined based on the stacking design width of the tabs 61 of the qualified battery cell 6. This corresponds to the ideal centerline position where prism 51 needs to be inserted in the electrode tab 61 folding inspection station. Calculate the width deviation. and center offset These two deviations reflect different states of the current 6-tab 61 stack of the battery cells: center offset describes the overall deviation of the stack from the ideal centerline, and width offset describes the width of the gap between the two tabs 61. Based on the width offset and center offset, a pose deviation in the horizontal direction is generated. and One example of the correspondence is: center offset. Decide Used to compensate for the overall translation of the six-axis collaborative robot body 1 in the horizontal direction; width deviation Determined by combining the preset prism 51 width threshold This is used to adjust the lateral offset during insertion, ensuring that the prism 51 can smoothly enter the gap between the two tabs 61. The correction value is sent to the control module in real time. The control module dynamically updates the motion interpolation path in the subsequent electrode tab 61 flipping detection process based on the received correction value. This ensures that when the cell 6 performs four reciprocating insertion and removal actions, the gap between the two electrodes 61 can be precisely aligned with the insertion position of the prism 51, avoiding physical contact between the prism 51 and the electrodes 61, while ensuring that the images of the inner and outer sides of the electrodes 61 can be completely and clearly acquired.

[0042] In at least one embodiment, the method of the control module driving the six-axis collaborative robot body 1 to perform a number of reciprocating insertion and removal actions to clamp the battery cell 6 according to the motion interpolation path, and acquiring multifocal images of the inner and outer sides of the electrode tab 61 through the electrode tab folding detection module 5 includes: the control module driving the six-axis collaborative robot body 1 to perform at least four reciprocating insertion and removal actions to clamp the battery cell 6 according to the motion interpolation path, that is, the control module drives the six-axis collaborative robot body 1 to move the battery cell 6 to the left side of the two side-by-side electrode tab folding detection modules 5, so that the battery cell 6 is pushed in horizontally, so that the outer side of the right electrode tab 61 of the battery cell 6 enters the detection area of ​​the two electrode tab folding detection modules 5, and maintains this position for a preset time until the corresponding image is acquired and then exits along the original path. The control module drives the six-axis collaborative robot body 1 to move the battery cell 6 horizontally to the right until the prism 51 of the left tab folding detection module 5 is located between the two tabs 61 of the battery cell 6, so as to collect the inner image of the left tab 61 and then exit along the original path; the control module drives the six-axis collaborative robot body 1 to move the battery cell 6 horizontally to the right until the prism 51 of the right tab folding detection module 5 is located between the two tabs 61 of the battery cell 6, so as to collect the inner image of the right tab 61 and then exit along the original path; the control module drives the six-axis collaborative robot body 1 to move the battery cell 6 horizontally to the right until the battery cell 6 is completely located to the right of the two tab folding detection modules 5, so as to collect the outer image of the left tab 61 and then exit along the original path.

[0043] Specifically, the six-axis collaborative robot body 1 precisely transfers the battery cell 6 to the detection area of ​​the tab folding detection module 5. Two tab folding detection modules 5 are arranged side-by-side, each equipped with a ring light source, a liquid lens, a camera, and a prism 51. The robot drives the battery cell 6 to perform four reciprocating insertion and removal actions, in the following sequence: First insertion / removal: The six-axis collaborative robot body 1 moves the battery cell 6 horizontally to the left of the two tab folding detection modules 5, meaning the battery cell 6 is completely positioned to the left of the left tab folding detection module 5. Then, it pushes it in horizontally, allowing the outer side of the right tab 61 to enter the field of view of the prism 51 of the right tab folding detection module 5. After maintaining this position for a preset time, the liquid lens automatically focuses, acquiring a multi-focal image of the outer side of the right tab 61, and then exits.

[0044] Second insertion / removal: The six-axis collaborative robot body 1 moves the battery cell 6 horizontally to the right a certain distance, and then pushes it in, so that the prism 51 of the left electrode flipping detection module 5 is located in the middle of the two electrodes 61, and collects multi-focal images of the inside of the left electrode 61, and then exits.

[0045] Third insertion / removal: The six-axis collaborative robot body 1 continues to move horizontally to the right, and then pushes in, so that the prism 51 of the right pole ear flip detection module 5 is located in the middle of the two pole ears 61, and acquires a multi-focal image of the inside of the right pole ear 61, and then exits.

[0046] Fourth insertion / removal: The six-axis collaborative robot body 1 continues to move horizontally to the right, pushing in until the battery cell 6 is completely located on the right side of the two tab flipping detection modules 5, acquiring a multi-focal image of the outer side of the left tab 61, and then exiting.

[0047] Multifocal images of the inner and outer sides of the tab 61 are acquired during each insertion and holding phase. Multifocal images are fused to generate a clear feature image of the tab 61 across the entire domain. The clear feature image of the tab 61 is used for tab 61 segmentation and tab 61 line layer count. A deep learning model is then used for defect identification and qualification judgment.

[0048] It should be noted that the multifocal image fusion method given in this embodiment is only an example. Those skilled in the art can use other fusion algorithms based on sharpness evaluation to replace it, as long as they can achieve the fusion of multifocal images and generate a clear image across the entire field.

[0049] Specifically, please refer to Figure 3 The system receives multi-focal distance image sequences from the inner and outer sides of the electrode 61 from the electrode folding detection module 5, and converts all images into grayscale image format. Each frame of the multi-focal distance image is denoised using a 3×3 Gaussian filter, and contrast is enhanced through adaptive histogram equalization to highlight edge and texture features. After preprocessing, a 7×7 sliding window with a step size of 2 is used to calculate the two-factor composite sharpness pixel by pixel.

[0050] Calculate gradient magnitude Characterizes the sharpness of edges: (1); in: (2); (3); In the formula, For the first Frame pixels grayscale value; and They represent the first Frame pixels The horizontal and vertical grayscale gradients.

[0051] Calculate local information entropy Characterizes the richness of texture information: (4); In the formula, This represents the total number of gray levels. For Within the 7×7 neighborhood centered on the value of grayscale The probability of occurrence.

[0052] Weighted fusion yields overall clarity: (5); In the formula, For the first Frame pixels A comprehensive and clear evaluation value; and These are weighting coefficients. , .

[0053] Pixel-by-pixel fusion weights are calculated based on overall sharpness: (6); In the formula, For the first Frame pixels Normalized fusion weights; It is a very small constant used to avoid the denominator being 0.

[0054] Pixel-wise soft-weighted fusion of multiple frames of images according to weights: (7); In the formula, The grayscale value of the final fused full-domain clear 61 feature map.

[0055] A 3×3 median filter is used to smooth the initial fused image, suppressing noise and preserving defect details.

[0056] Finally, the fully fused, clear 61-feature map of the entire electrode is output and transmitted to the defect extraction and classification submodule.

[0057] It should be noted that the method given in this embodiment is only an example. Those skilled in the art can use other skeleton extraction or branch optimization algorithms instead, as long as they can achieve accurate statistics of the number of 61-line layers of the tab.

[0058] To address the inaccurate counting issues caused by dense 61-line electrode density, burr interference, adhesion, and breakage, high-precision counting is achieved through improved skeleton extraction, hierarchical constraints, and intersection point optimization. The specific implementation steps are as follows: Input: Globally sharp 61 feature map obtained by multi-focus image fusion The image has undergone noise reduction and contrast enhancement preprocessing, and the edges and contours of the 61-line electrode are clear.

[0059] 61-characteristic diagram of the polar ear Adaptive dual-threshold segmentation and edge enhancement are performed, and combined with the geometric prior of the tab 61 (position, orientation, and spacing constraints), the tab 61 line region is separated from the background and the tab 61 body region. Small noise points are removed by morphological opening operations to generate a binarized segmentation map of the tab 61 line. It fully preserves the pixel outline of each layer's tab 61 lines, avoiding over-segmentation and under-segmentation.

[0060] (8); In the formula, and To achieve adaptive dual thresholds, dynamic calculation is performed based on the grayscale distribution of the 61-element region of the electrode. Represents the area of ​​electrode 61. Represents the background area.

[0061] To address the redundant skeleton branches caused by dense 61-line polarity and spur interference, an improved skeleton extraction algorithm based on hierarchical trajectory fitting and abnormal branch suppression is adopted. This algorithm is applied to the binarized segmentation image. Initial skeleton extraction with distance field constraints is performed to obtain the initial skeleton map. : (9); In the formula, For pixels Euclidean distance field to the nearest background boundary; In pixels The 3×3 neighborhood range centered on the center.

[0062] Based on the structural features of the 61-line multi-layer arrangement and continuous main trunk, connected component analysis and line fitting are performed on the initial skeleton to generate the main trajectory set of each layer; a branch topology similarity criterion is constructed, and the branch length is calculated. Deviation from the main trajectory direction Remove items with a length less than the threshold and a directional deviation greater than the threshold. The abnormal branches, such as burrs and adhesions, were removed; finally, the main skeleton that conforms to hierarchical continuity was retained, and after single-pixel fine connection repair, a 61-line main skeleton diagram of the tab without redundancy and false bifurcation was obtained. .

[0063] Optimized diagram of the main skeleton of the 61-line electrode. Perform intersection point extraction and constraint optimization to extract the skeleton intersection point set. Calculate the included angle between adjacent skeleton branches at the intersection point. : (10); In the formula, and Let be the direction vectors of the two branches at the intersection point.

[0064] angle With preset angle threshold By comparing and combining branch connectivity characteristics, a comprehensive judgment is made. Intersections where the included angle is less than a threshold and the branch is an isolated short branch are identified as abnormal intersections formed by burr residue, and the corresponding redundant branches are removed. For skeleton regions that conform to the main extension trend but have breaks, breakpoint completion is performed to complete the secondary optimization of the skeleton topology. The number of effective skeleton branches after optimization is counted to obtain the actual number of layers of the 61-line electrode. : (11); In the formula, This represents the total number of skeleton branches; This is the validity determination function; valid branches take the value 1, and invalid branches take the value 0.

[0065] In at least one embodiment, the six-axis collaborative robot body 1 is provided with an end effector, which includes: a gripping base, a gripper cylinder, and a floating gripper; the floating gripper is connected to the gripping base through an elastic reset element, and the gripper cylinder is connected to the elastic reset element; the control module controls the gripper cylinder to drive the floating gripper to flexibly or rigidly grasp the battery cell 6 through a solenoid valve.

[0066] Specifically, the six-axis collaborative robot body 1 drives the end effector to move down to the inspection area of ​​the material table 2. When the floating gripper contacts the battery cell 6, it uses the compression stroke of the elastic reset element to absorb the height error in the Z-axis direction, and completes the flexible gripping of the battery cell 6 to avoid rigid collision caused by positioning fixtures or dimensional deviations of the battery cell 6. After gripping, the pressure sensor monitors the clamping force in real time, but it still remains in a floating state at this time.

[0067] Specifically, the control module controls the solenoid valve of the end effector to apply pressure to the gripper cylinder, which locks the elastic reset element and restricts the relative movement between the floating gripper and the gripping base, thereby switching the floating gripper from a flexible floating state to a rigid state.

[0068] Specifically, after the inspection is completed, the control module depressurizes the end effector, releases the locking state of the elastic reset element, and restores the flexibility of the floating gripper. Based on the comprehensive judgment results (appearance defects, tab 61 defects, tab 61 wire layer count, quantitative scoring, etc.), the six-axis collaborative robot body 1 transfers the battery cell 6 to the qualified or unqualified battery cell 6 placement area on the material platform 2. During the return process, the flexible state prevents secondary damage to the battery cell 6. After sorting, the six-axis collaborative robot body 1 returns to its initial position, awaiting the next inspection instruction for the battery cell 6, completing the inspection closed loop.

[0069] In at least one embodiment, the scanning module 4 is configured to read the QR code information on the surface of the battery cell 6; the positioning detection module is configured to capture an image of the tab 61; the appearance detection module is configured to capture an image of the appearance of the battery cell 6; and the tab folding detection module 5 is provided with a ring light source, a liquid lens, a camera and a prism 51 to acquire images of the inside and outside of the tab 61.

[0070] Specifically, the QR code area on the surface of the battery cell 6 is moved into the reading range of the scanning module 4. The scanning module 4 collects the QR code image and decodes it. The decoded unique identifier of the battery cell 6 is associated with all subsequent test data to form a traceable test file.

[0071] Specifically, the six-axis collaborative robot body 1 holds the battery cell 6 and rotates around its own axis in a rigid state. During the rotation of the battery cell 6, the appearance detection module is triggered and high-definition images of the large surface, bottom surface, two sides and top surface of the battery cell 6 are collected in sequence. The control module stitches and analyzes the collected multi-faceted images to identify whether there are appearance defects such as scratches, dents, dirt, and damage, and records the location and type of defects.

[0072] Based on the same technical concept, please refer to Figures 4 to 5At least one embodiment also provides a battery cell tab folding detection system, comprising: a control module, a six-axis collaborative robot body 1, a material table 2, a positioning detection module, a barcode scanning module 4, an appearance inspection module, and tab folding detection modules 5; wherein the positioning detection module, barcode scanning module 4, appearance inspection module, and each tab folding detection module 5 are mounted on the six-axis collaborative robot body 1, and the six-axis collaborative robot body 1, the positioning detection module, the barcode scanning module 4, the appearance inspection module, and each tab folding detection module 5 are electrically connected to the control module; the control module is configured to drive the six-axis collaborative robot body 1 to flexibly grasp the battery cell 6 from the material table 2 to transfer the battery cell 6 to the detection starting point; the control module is configured to acquire images of the tabs 61 of the battery cell 6 at the detection starting point through the positioning detection module to calculate the pose deviation of the center of the tabs 61 relative to a preset detection path, and correct the preset detection path to form a corresponding motion interpolation path; The control module is configured to acquire the identification information of the battery cell 6 through the barcode scanning module 4, and control the six-axis collaborative robot body 1 to rigidly grasp the battery cell 6; the control module is configured to drive the six-axis collaborative robot body 1 to rotate the battery cell 6 around the axis, and acquire appearance images of each side of the battery cell 6 through the appearance inspection module to obtain the corresponding appearance defect analysis results; the control module is configured to drive the six-axis collaborative robot body 1 to perform several reciprocating insertion and removal actions according to the motion interpolation path, and acquire multi-focal images of the inner and outer sides of the tab 61 through the tab folding detection module 5, and fuse the multi-focal images of the inner and outer sides of the tab 61 to generate a clear feature image of the tab 61 in the whole domain, thereby obtaining the corresponding tab 61 defect analysis results; and the control module is configured to control the six-axis collaborative robot body 1 to flexibly grasp the battery cell 6, and put the battery cell 6 back into the corresponding placement area of ​​the material table 2 according to the appearance defect analysis results and the tab 61 defect analysis results.

[0073] In at least one embodiment, the control module performs the steps of the cell tab flipping detection method described above.

[0074] Based on the same technical concept, at least one embodiment also provides an integrated robot, comprising: a control module, a six-axis collaborative robot body 1, a positioning and detection module, a barcode scanning module 4, an appearance inspection module, and a tab folding detection module 5; wherein the positioning and detection module, the barcode scanning module 4, the appearance inspection module, and each tab folding detection module 5 are mounted on the six-axis collaborative robot body 1, and the six-axis collaborative robot body 1, the positioning and detection module, the barcode scanning module 4, the appearance inspection module, and each tab folding detection module 5 are electrically connected to the control module; the control module is configured to drive the six-axis collaborative robot body 1 to flexibly grasp the battery cell 6, so as to move the battery cell 6 to the detection starting point; the control module is configured to acquire images of the tabs 61 of the battery cell 6 through the positioning and detection module at the detection starting point, so as to calculate the pose deviation of the center of the tabs 61 relative to the preset detection path, and correct the preset detection path to form a corresponding motion interpolation path; the control module The module is configured to acquire the identification information of the battery cell 6 through the barcode scanning module 4, and the control module controls the six-axis collaborative robot body 1 to rigidly grasp the battery cell 6; the control module is configured to drive the six-axis collaborative robot body 1 to rotate the battery cell 6 around the axis, and to acquire appearance images of each side of the battery cell 6 through the appearance detection module to obtain the corresponding appearance defect analysis results; the control module is configured to drive the six-axis collaborative robot body 1 to perform several reciprocating insertion and removal actions according to the motion interpolation path, and to acquire multi-focal images of the inner and outer sides of the tab 61 through each tab folding detection module 5, and to fuse the multi-focal images of the inner and outer sides of the tab 61 to generate a clear feature image of the tab 61 in the whole domain, thereby obtaining the corresponding tab 61 defect analysis results; and the control module is configured to control the six-axis collaborative robot body 1 to flexibly grasp the battery cell 6, and to place the battery cell 6 at the corresponding position according to the appearance defect analysis results and the tab 61 defect analysis results.

[0075] In at least one embodiment, the control module performs the steps of the cell tab flipping detection method described above.

[0076] Based on the same technical concept, at least one embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a control module, implements the steps of the above-described cell tab flipping detection method.

[0077] In summary, this invention integrates a vision inspection module into the body of a six-axis collaborative robot, utilizing the robot's high degrees of freedom to replace the complex mechanical flipping and translation mechanisms in traditional equipment. This significantly reduces equipment size and improves production line flexibility. Through flexible gripping, real-time path correction, and multiple reciprocating insertion and removal detection actions, it achieves high-precision, fully automated online detection of the cell tab folding state. This eliminates the need for cumbersome mechanical structure adjustments and facilitates convenient model changeover, meeting the needs of large-scale intelligent manufacturing of lithium batteries.

[0078] The disclosures and other solutions, examples, embodiments, modules, and functional operations described in this document can be implemented in digital electronic circuits, or computer software, firmware, or hardware, including the structures disclosed in this document and their structural equivalents, or combinations thereof. The disclosures and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible and non-volatile computer-readable medium for execution by a data processing apparatus or for controlling the operation of the data processing apparatus. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a storage device, a material composition that influences machine-readable propagated signals, or one or more of these. The terms "data processing unit" or "data processing apparatus" include all means, devices, and machines for processing data, including, for example, programmable processors, computers, or multiprocessors or computer groups. In addition to hardware, the apparatus may also include code that creates an execution environment for a computer program, such as code constituting processor firmware, a protocol stack, a database management system, an operating system, or combinations thereof. The propagated signals are artificially generated signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information for transmission to a suitable receiver device.

[0079] Computer programs (also known as programs, software, software applications, scripts, or code) can be written in any programming language (including compiled or interpreted languages) and can be deployed in any form, including as standalone programs or as modules, components, subroutines, or other units suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to that program, or in multiple coordinating files (e.g., a file storing one or more modules, subroutines, or portions of code). Computer programs can be deployed and executed on one or more computers located at a single site or distributed across multiple sites interconnected by a communication network.

[0080] The processing and logic flows described in this document can be executed by one or more programmable processors that execute one or more computer programs to perform functions by manipulating input data and generating outputs. The processing and logic flows can also be executed by special-purpose logic circuitry, and the devices can be implemented as special-purpose logic circuitry, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits).

[0081] For example, processors suitable for executing computer programs include general-purpose and special-purpose microprocessors, as well as any one or more of any type of digital computer. Typically, the processor receives instructions and data from read-only memory or random access memory, or both. The basic components of a computer are a processor that executes instructions and one or more storage devices that store the instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or operatively coupled to receive data from or transfer data to mass storage devices, or both. However, a computer does not necessarily have such devices. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and optical disc read-only memory (CD ROM) and digital versatile optical disc read-only memory (DVD-ROM). The processor and memory may be supplemented by dedicated logic circuitry or incorporated into dedicated logic circuitry.

[0082] While this patent document contains numerous details, it should not be construed as limiting the scope of any invention or claim, but rather as a description of features of specific embodiments of a particular invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various functions described in the context of a single embodiment may also be implemented individually in multiple embodiments, or in any suitable sub-combination. Furthermore, although the foregoing features may be described as functioning in certain combinations, or even initially claimed to be so, in certain circumstances, one or more features from a combination of claims may be removed from the combination, and a combination of claims may refer to a sub-combination or a variation of a sub-combination.

[0083] Similarly, although the operations are described in a specific order in the accompanying drawings, this should not be construed as requiring the specific order or sequence shown to perform such operations, or all the described operations, in order to obtain the desired result. Furthermore, the separation of various system components in the embodiments of this patent document should not be construed as requiring such separation in all embodiments.

[0084] Only some implementations and examples are described; other implementations, enhancements, and variations can be made based on the content described and illustrated in this patent document.

[0085] While several embodiments are provided in this disclosure, it should be understood that the disclosed systems and methods may be embodied in many other specific forms without departing from the spirit or scope of this disclosure. The present examples are intended to be illustrative rather than restrictive and are not limited to the details given. For example, various elements or components may be combined or integrated into another system, or certain features may be omitted or not implemented.

[0086] In the several embodiments provided herein, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0087] Furthermore, without departing from the scope of this disclosure, the discrete or individual technologies, systems, subsystems, and methods described and illustrated in the various embodiments may be combined or integrated with other systems, modules, technologies, or methods. Other items shown or discussed as coupled may be directly connected or indirectly coupled or communicated via some interface, device, or intermediate component in an electrical, mechanical, or other manner. Those skilled in the art can identify other examples of changes, substitutions, and modifications without departing from the spirit and scope of this disclosure.

Claims

1. A method for detecting the flipping of battery cell tabs, characterized in that, include: The control module drives the six-axis collaborative robot to flexibly grasp the battery cell from the material table and transfer the battery cell to the detection starting point; The control module acquires images of the battery cell's tabs at the detection starting point through the positioning detection module, calculates the pose deviation of the tab center relative to the preset detection path, and corrects the preset detection path to form the corresponding motion interpolation path. The control module obtains the identification information of the battery cell through the barcode scanning module, and the control module controls the six-axis collaborative robot to rigidly grasp the battery cell. The control module drives the six-axis collaborative robot to hold the battery cell and rotate it around the axis, and the appearance inspection module collects appearance images of each side of the battery cell to obtain the corresponding appearance defect analysis results. The control module drives the six-axis collaborative robot to perform several reciprocating insertion and removal actions by gripping the battery cell according to the motion interpolation path. It also collects multi-focal images of the inner and outer sides of the electrode through each electrode flipping detection module, and fuses the multi-focal images of the inner and outer sides of the electrode to generate a clear electrode feature image in the whole domain, thereby obtaining the corresponding electrode defect analysis results. as well as The control module controls the six-axis collaborative robot to flexibly grasp the battery cell and, based on the results of appearance defect analysis and electrode defect analysis, place the battery cell back into the corresponding placement area of ​​the material platform.

2. The cell tab flipping detection method as described in claim 1, characterized in that, The control module acquires images of the battery cell's tabs at the detection starting point using a positioning detection module, calculates the pose deviation of the tab center relative to a preset detection path, and corrects the preset detection path to form a corresponding motion interpolation path. The method includes: The control module obtains the actual lateral width or actual center position of the electrode stack in the electrode image and compares it with the preset standard value to calculate the pose deviation of the cell in the horizontal direction. The control module dynamically compensates the pose deviation to the preset detection path of the six-axis collaborative robot body to form a corresponding motion interpolation path.

3. The cell tab flipping detection method as described in claim 1, characterized in that, The control module drives the six-axis collaborative robot to perform several reciprocating insertion and removal actions on the battery cell according to the motion interpolation path, and the method of acquiring multifocal images of the inner and outer sides of the electrode tab through the electrode tab folding detection module includes: The control module drives the six-axis collaborative robot to perform at least four reciprocating insertion and removal actions on the battery cell according to the motion interpolation path. The control module drives the six-axis collaborative robot to move the battery cell to the left of the two side-by-side tab folding detection modules, so that the battery cell is pushed in horizontally and the outer side of the right tab of the battery cell enters the detection area of ​​the two tab folding detection modules. It remains in this position for a preset time until the corresponding image is acquired and then exits along the original path. The control module drives the six-axis collaborative robot to move the battery cell horizontally to the right until the prism of the left electrode flipping detection module is located in the middle of the two electrodes of the battery cell, so as to collect the inner image of the left electrode and then exit along the original path. The control module drives the six-axis collaborative robot to move the battery cell horizontally to the right until the prism of the right electrode flipping detection module is located in the middle of the two electrodes of the battery cell, so as to collect the inner image of the right electrode and then exit along the original path. The control module drives the six-axis collaborative robot to move the battery cell horizontally to the right until the battery cell is completely located to the right of the tab flip detection module, so as to collect the outer image of the left tab and then exit along the original path.

4. The cell tab flipping detection method as described in claim 1, characterized in that, The six-axis collaborative robot body is equipped with an end effector, which includes: a gripping base, a gripper cylinder, and a floating gripper; The floating gripper is connected to the gripping base via an elastic reset element, and the gripper cylinder is connected to the elastic reset element. The control module uses a solenoid valve to control the gripper cylinder to drive the floating gripper to flexibly or rigidly grasp the battery cell.

5. The cell tab flipping detection method as described in claim 1, characterized in that, The scanning module is configured to read the QR code information on the surface of the battery cell; The positioning and detection module is configured to capture images of the electrode ears; The appearance inspection module is configured to capture images of the battery cell's appearance; The electrode folding detection module is equipped with a ring light source, a liquid lens, a camera, and a prism to acquire images of the inside and outside of the electrode.

6. A battery cell tab flipping detection system, characterized in that, include: Control module, six-axis collaborative robot body, material platform, positioning and detection module, barcode scanning module, appearance inspection module and bipolar ear folding detection module; in The positioning detection module, barcode scanning module, appearance inspection module, and each tab folding detection module are installed on the body of the six-axis collaborative robot. The six-axis collaborative robot body, the positioning detection module, barcode scanning module, appearance inspection module, and each tab folding detection module are electrically connected to the control module. The control module is configured to drive the six-axis collaborative robot to flexibly grasp the battery cell from the material table and transfer the battery cell to the detection starting point; The control module is configured to acquire images of the battery cell's tabs at the detection starting point through the positioning detection module, calculate the pose deviation of the tab center relative to the preset detection path, and correct the preset detection path to form a corresponding motion interpolation path. The control module is configured to obtain the identification information of the battery cell through the barcode scanning module, and the control module controls the six-axis collaborative robot to rigidly grasp the battery cell. The control module is configured to drive the six-axis collaborative robot body to hold the battery cell and rotate it around the axis, and to collect images of the appearance of each side of the battery cell through the appearance inspection module in order to obtain the corresponding appearance defect analysis results. The control module is configured to drive the six-axis collaborative robot body to perform several reciprocating insertion and removal actions by gripping the battery cell according to the motion interpolation path, and to collect multi-focal images of the inner and outer sides of the electrode through each electrode flipping detection module. The multi-focal images of the inner and outer sides of the electrode are fused and processed to generate a clear electrode feature image in the whole domain, thereby obtaining the corresponding electrode defect analysis results. as well as The control module is configured to control the six-axis collaborative robot to flexibly grasp the battery cell and, based on the results of appearance defect analysis and electrode defect analysis, place the battery cell back into the corresponding placement area of ​​the material platform.

7. The cell tab flipping detection system as described in claim 6, characterized in that, The control module executes the steps of the cell tab flipping detection method as described in any one of claims 1-5.

8. An integrated robot, characterized in that, include: Control module, six-axis collaborative robot body, positioning and detection module, barcode scanning module, appearance inspection module and bipolar ear folding detection module; in The positioning detection module, barcode scanning module, appearance inspection module, and each tab folding detection module are installed on the body of the six-axis collaborative robot. The six-axis collaborative robot body, the positioning detection module, barcode scanning module, appearance inspection module, and each tab folding detection module are electrically connected to the control module. The control module is configured to drive the six-axis collaborative robot to flexibly grasp the battery cell and move it to the detection starting point; The control module is configured to acquire images of the battery cell's tabs at the detection starting point through the positioning detection module, calculate the pose deviation of the tab center relative to the preset detection path, and correct the preset detection path to form a corresponding motion interpolation path. The control module is configured to obtain the identification information of the battery cell through the barcode scanning module, and the control module controls the six-axis collaborative robot to rigidly grasp the battery cell. The control module is configured to drive the six-axis collaborative robot body to hold the battery cell and rotate it around the axis, and to collect images of the appearance of each side of the battery cell through the appearance inspection module in order to obtain the corresponding appearance defect analysis results. The control module is configured to drive the six-axis collaborative robot body to perform several reciprocating insertion and removal actions by gripping the battery cell according to the motion interpolation path, and to collect multi-focal images of the inner and outer sides of the electrode through each electrode flipping detection module. The multi-focal images of the inner and outer sides of the electrode are fused and processed to generate a clear electrode feature image in the whole domain, thereby obtaining the corresponding electrode defect analysis results. as well as The control module is configured to control the six-axis collaborative robot to flexibly grasp the battery cell and place the battery cell at the corresponding position based on the results of appearance defect analysis and electrode defect analysis.

9. The integrated robot as described in claim 8, characterized in that, The control module executes the steps of the cell tab flipping detection method as described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the control module, it implements the steps of the cell tab flipping detection method as described in any one of claims 1-5.

Images