Electric screwdriver switching and grabbing positioning method for different screw models

Abstract

The electric screwdriver switching and grabbing positioning method comprises the steps that intelligent switching between an electric screwdriver mode and a clamping mode is achieved through an end effector, the grabbing mode is dynamically adjusted according to the actual situation of a target area image, the operation efficiency of an automatic system is improved, and the grabbing precision is improved. The grabbing accuracy is ensured when different targets are processed, and the special requirement for mixed stacking of battery cells and screws in the battery pack disassembling process is met; through self-adaptive selection operation of different screw models and electric screwdriver heads, the rotating speed and torque of the electric screwdriver heads can be automatically adjusted according to the identified screw models, and the problem that a traditional electric screwdriver system is difficult to adapt to screws of multiple models is effectively solved; by introducing the hierarchical analysis module and the attention mechanism module, the relative positions of the battery cell and the screw are accurately calculated, on one hand, the accuracy of the subsequent grabbing action of the mechanical arm is guaranteed, and on the other hand, the flexibility of an automatic system is improved by packaging relative position data.

Description

Technical Field

[0001] The invention relates to the technical field of image recognition and mechanical arm grasping, and in particular to an electric screwdriver switching and grasping positioning method for different screw models. Background Art

[0002] With the rapid development of electric vehicles and energy storage equipment, the widespread use of battery packs has become a trend. However, after long-term use or retirement, the disassembly and recycling of battery packs has become an important issue. Traditional battery pack disassembly mainly relies on manual operation, which is not only inefficient but also has certain safety hazards. Especially during the disassembly of battery packs, it is very common to mix battery cells with other components (such as screws). How to effectively identify and handle these mixed scenarios has become the focus of current technical research.

[0003] At present, the automation systems used on the market still have many deficiencies in identifying and processing mixed stacked batteries and screws for the disordered stacking scene after the battery pack is disassembled. Existing technologies mostly rely on preset models to identify screws and batteries, which often lack flexibility when dealing with complex scenarios. For example, when the battery pack is disassembled, the batteries and screws may be mixed and stacked in disorder. At this time, the traditional electric batch system cannot effectively distinguish these different components, resulting in low accuracy in grasping and processing. Moreover, when faced with mixed scenes of screws and batteries of different models, the existing automation system usually finds it difficult to achieve accurate grasping switching and automatic positioning, and is prone to misoperation or inefficiency. In addition, the existing robotic arm control system usually finds it difficult to achieve accurate identification and grasping switching of mixed targets, especially when faced with randomly stacked batteries and screws of different models, there are high error rates and unstable operations. These problems greatly limit the efficiency and adaptability of the automation system. And it seriously limits the automation level of the battery pack disassembly process, and cannot meet the needs of large-scale battery recycling. Therefore, the research on the switching and grasping positioning methods of electric screwdrivers for different screw models and mixed stacking of battery cells has important practical application value and innovation. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a method for switching and grasping and positioning electric screwdrivers for different screw models to solve the problems raised in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for switching and grasping and positioning electric screwdrivers for different screw models, comprising:

[0007] The target area image is acquired by a camera device, and according to the target area image recognition algorithm, it is determined whether the current target area image is a disordered stacking state after the battery cells are separated;

[0008] If it is determined that the current target area image is a disordered stacking state after the battery cells are separated, the current grasping mode is switched to a clamping mode through the end effector, and the robot arm programmable controller is used to control the robot arm to grasp the battery cell position, and the grasped battery cell is stored;

[0009] Otherwise, if the current target area image is in a state where there is no battery cell and only screws, the current grasping mode is switched to the electric screw mode through the end effector, the screw position in the current target area image is marked, and the screw model is identified according to the screw position in the current target area image, and the robot arm programmable controller is used to control the robot arm to grasp the screw position;

[0010] The results of the current target area image recognition, the current grasping mode of the end effector, the battery cell model and the screw model are displayed in the visual operation panel.

[0011] As a preferred solution of the electric screwdriver switching and grasping positioning method for different screw models described in the present invention, it also includes:

[0012] The electric screwdriver method includes a plurality of electric screwdriver heads and the electric screwdriver heads are composed of an array, and the clamping method is a classified clamping method;

[0013] The visual operation panel is connected to the robot arm programmable controller, and the robot arm programmable controller and the end controller are optimized regularly through the display information in the visual operation panel.

[0014] As a preferred solution of the electric screwdriver switching and grasping positioning method for different screw models described in the present invention, the target area image recognition algorithm includes:

[0015] The target area image acquired by the camera device is represented as unlabeled data, and a scene data set is created to perform scene classification on the unlabeled data;

[0016] After scene classification, the scene data set includes a state where there are no cells but only screws, and a disorderly stacking scene where the cells are separated;

[0017] By inputting the scene dataset into the DINO model, the target image in the current area is identified.

[0018] As a preferred solution of the electric screwdriver switching and grasping positioning method for different screw models described in the present invention, the DINO model includes:

[0019] Hierarchical analysis module, attention mechanism module and target detection module.

[0020] As a preferred solution of the electric screwdriver switching and grasping positioning method for different screw models described in the present invention, the hierarchical analysis module includes:

[0021] According to the received scene data set, all screw types are classified into several categories, each category represents the thread shape, size or material of different screws, and the number of electric screwdriver bits in each layer is determined;

[0022] Initialize the electric screwdriver array and set the number of initial array elements to zero;

[0023] According to the current screw type, the electric screwdriver bit types are gradually added to the array.

[0024] As a preferred solution of the electric screwdriver switching and grasping positioning method for different screw models described in the present invention, the attention mechanism module and the target detection module include:

[0025] The attention mechanism module performs weight assignment according to the scene data set, extracts all the batteries and screws in the scene data set, assigns weights to all the batteries and screws, sorts them from high to low according to the weights, and calculates the relative positions of the batteries and screws in the scene in sequence according to the sorting order, thereby obtaining the relative positions of all the batteries and screws in the scene;

[0026] The target detection module receives the relative positions of the battery cell and the screw in the scene, and encapsulates them into operation parameters and transmits them to the programmable controller of the robot arm to generate operation instructions.

[0027] As a preferred solution of the electric screwdriver switching and grasping positioning method for different screw models described in the present invention, the robot arm is controlled by a robot arm programmable controller, including:

[0028] The robot arm receives the operation instruction issued by the DINO model and initializes the position and posture of the robot arm, wherein the posture is composed of the first rotation joint A, the second movement joint B and the third ball joint C;

[0029] The first rotation joint A calculates the angle A1 that the robotic arm currently needs to rotate according to the position of the target object that the robotic arm needs to grasp;

[0030] According to A1 and the placement angle L of the target object, a deviation value M=A1-L is calculated, and at the same time, a distance B1 required to be moved from the second moving joint B to the current object is calculated, and B1 is compared with a set target moving distance B2;

[0031] If the deviation value M is not zero and the distance between B1 and B2 gradually increases, the third ball joint C is always adjusted to make the deviation value M between A1 and L approach zero, and make B1 approach B2 or B2 approach B1. Otherwise, when the deviation value M is equal to zero and B1 overlaps B2, the third ball joint C is not adjusted.

[0032] As a preferred solution of the electric screwdriver switching and grasping positioning method for different screw models described in the present invention, wherein: the electric screwdriver method includes a plurality of electric screwdriver heads and the electric screwdriver heads are composed of an array, and the clamping method is a classified clamping method, including:

[0033] There are several elements in the array, each of which stores a type of electric screwdriver bit, and the number of elements in the array is affected by the results of the hierarchical analysis module in the DINO model;

[0034] Before the clamping method is performed, the battery cells are classified by scanning the QR code on the battery cells, the classification results are saved in the battery cell category database, and the clamping force is set according to the battery cell category.

[0035] As a preferred solution of the electric screwdriver switching and grasping positioning method for different screw models described in the present invention, the grasping process of the disorderly stacked discharge core also includes:

[0036] By considering the clamping tension, each time the robot arm clamps a battery cell, the clamping force changes with the type of battery cell, and the clamping tension changes with the size of the battery cell;

[0037] Taking the shortest distance from the robot arm to the battery cell position as the objective function, the shortest path for the robot arm to grab the battery cell is established.

[0038] As a preferred solution of the electric screwdriver switching and grasping positioning method for different screw models described in the present invention, the screw grasping process also includes:

[0039] The robot arm selects the corresponding electric screwdriver according to the currently identified screw model and sets the speed and torque of the electric screwdriver;

[0040] The speed and torque of the electric screwdriver bit are adjusted by considering the start-stop ratio of the electric screwdriver bit and the motion coefficient of the robot arm.

[0041] Compared with the prior art, the invention has the following beneficial effects:

[0042] 1. By introducing the DINO model, the mixed and disordered stacking state of battery cells and screws can be effectively distinguished, thereby achieving accurate scene recognition and classification, greatly improving the adaptability of the automation system in complex scenarios, and effectively reducing operational errors caused by recognition errors in traditional systems;

[0043] 2. The end effector realizes intelligent switching between the electric batch mode and the clamping mode, and dynamically adjusts the grasping mode according to the actual situation of the target area image. This switching method not only improves the operating efficiency of the automation system, but also ensures the grasping accuracy when handling different targets, and adapts to the special needs of mixed stacking of battery cells and screws during battery pack disassembly;

[0044] 3. Through the adaptive selection operation of different screw models and electric screwdriver bits, the speed and torque of the electric screwdriver bit can be automatically adjusted according to the identified screw model, which effectively solves the problem that the traditional electric screwdriver system is difficult to adapt to multiple types of screws, and greatly improves the accuracy and efficiency of the assembly and disassembly process;

[0045] 4. By introducing the hierarchical analysis module and the attention mechanism module into the DINO model, the relative positions of the battery cells and screws can be accurately calculated. On the one hand, this ensures the accuracy of the subsequent robotic arm grasping action, and on the other hand, it improves the flexibility of the automation system by encapsulating the relative position data. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0047] Figure 1 The overall flow chart of the method for switching electric screwdrivers and grasping and positioning screws of different screw types according to an embodiment of the present invention;

[0048] Figure 2 A comparison chart of the grasping accuracy of a robot arm in different scenarios for a method for switching electric screwdrivers and grasping and positioning screws of different screw types according to an embodiment of the present invention;

[0049] Figure 3 This is a robustness test diagram of the DINO model in different scenarios of the electric screwdriver switching and grasping positioning method for different screw models according to an embodiment of the present invention;

[0050] Figure 4 This is a system diagram of a method for switching electric screwdrivers and grasping and positioning screws of different screw models according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0054] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0055] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0056] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] Example 1

[0058] Reference Figure 1, which is the first embodiment of the present invention, and provides a method for switching and grasping and positioning electric screwdrivers for different screw models, including:

[0059] S1. Obtaining a target area image through a camera device, and determining whether the current target area image is a disordered stacking state after the battery cells are separated according to a target area image recognition algorithm;

[0060] Furthermore, the target area image acquired by the camera device is represented as unlabeled data, and a scene data set is created to perform scene classification on the unlabeled data;

[0061] It should be noted that the purpose of setting it as unlabeled data is to simulate the randomness of the target area image, so that the scene data set is a state of mixed and disordered stacking of batteries and screws before the scene is classified;

[0062] Specifically, after scene classification, the scene data set includes a state where there are no cells and only screws, and a disordered stacking scene after the cells are separated;

[0063] Furthermore, by inputting the scene dataset into the DINO model, the target image in the current area is identified;

[0064] It should be explained that the DINO (Distillation with No Labels) model is an unsupervised learning model that can learn image representation without labeled data through self-supervised learning.

[0065] Furthermore, the DINO model includes a hierarchical analysis module, an attention mechanism module, and an object detection module;

[0066] Furthermore, the hierarchical analysis module, based on the received scene data set, firstly, divides all screw types into several categories, each category represents the thread shape, size or material of different screws, and determines the number of electric screwdriver bits in each layer; secondly, initializes the electric screwdriver bit array and sets the number of initial array elements to zero; finally, according to the current screw type, gradually adds electric screwdriver bit types to the electric screwdriver bit array;

[0067] For example, the steps in the hierarchical analysis module are described as follows:

[0068] First, the screw types are divided into three categories: Category 1: small screws, thread shape: fine thread, size: diameter less than 2mm, material: aluminum alloy; Category 2: medium screws, thread shape: coarse thread, size: diameter 2-5mm, material: stainless steel; Category 3: large screws, thread shape: coarse thread, size: diameter greater than 5mm, material: steel; According to the number of screws, the number of electric screwdriver bits in the current layer is 3;

[0069] Secondly, create an electric screwdriver bit array to store electric screwdriver bits suitable for different screw types. The number of initial array elements is zero, that is, the array is empty, represented as: electric_screwdriver_array = [];

[0070] According to the current screw type, gradually add the electric screwdriver bit type to the electric screwdriver bit array:

[0071] Task requirement 1: Compatible with aluminum alloy and fine threads;

[0072] Get processed category 1: small screws;

[0073] Perform action 1: add a small electric screwdriver bit to the array;

[0074] Execution result: electric_screwdriver_array.append("Small-Screwdriver-Head");

[0075] Current array status: electric_screwdriver_array = ["Small-Screwdriver-Head"];

[0076] Task requirement 2: compatible with thick screws with a diameter of 2 to 5 mm;

[0077] Processing category 2: medium screws;

[0078] Perform action 2: add a medium-sized electric screwdriver bit to the array;

[0079] Execution result: electric_screwdriver_array.append("Medium-Screwdriver-Head");

[0080] Current array status: electric_screwdriver_array = ["Small-Screwdriver-Head", "Medium-Screwdriver-Head"];

[0081] Task requirement 3: diameter greater than 5mm, steel;

[0082] Processing category 3: large screws;

[0083] Result: electric_screwdriver_array.append("Large-Screwdriver-Head");

[0084] Current array status: electric_screwdriver_array=["Small-Screwdriver-Head", "Medium-Screwdriver-Head", "Large-Screwdriver-Head"];

[0085] In summary, the final array of electric screwdriver bits includes the three types of electric screwdriver bits in the current layer, which are suitable for small, medium and large screws respectively;

[0086] Furthermore, the attention mechanism module performs weight assignment according to the scene data set, extracts all the batteries and screws in the scene data set, assigns weights to all the batteries and screws, sorts them from high to low according to the weights, and calculates the relative positions of the batteries and screws in the scene in turn according to the sorting order, and obtains the relative positions of all the batteries and screws in the scene;

[0087] Preferably, the relative positions of the battery cell and the screw in the scene are calculated using a clustering-based relative position method;

[0088] It should be noted that the clustering algorithm can realize the dynamic update of the surrounding environment of the target object to adapt to the changes of the target object due to human movement or application scenarios, ensuring the accuracy of the robot arm in obtaining the position of the target object;

[0089] Specifically, first, weights are assigned according to the scenes in the scene data set, and then weights are assigned to the cells and screws according to the weights assigned to the scene data set. The weight assignment rules for the cells and screws are determined by location complexity, physical size, material properties, and task time.

[0090] Specifically, the position complexity is expressed by the state of the battery cell or screw in three-dimensional space:

[0091]

[0092] Among them, d x d y , dz are the distance coordinates of the target object (battery cell or screw) in three-dimensional space, expressed as the distance between the target object and the operating reference point (such as the initial position of the robot arm) (in meters or millimeters); for Euclidean and distance;

[0093] Specifically, the physical dimensions are expressed as:

[0094] F m =log(S+1)(2)

[0095] Where S represents the physical size of the target; for battery cells, it is expressed by volume (unit: cubic millimeter); for screws, it is expressed by the product of the outer diameter and length of the thread (unit: square millimeter);

[0096] Specifically, the material properties are expressed as:

[0097]

[0098] Wherein, H represents the hardness of the target material, which is usually expressed in Vickers hardness or Brinell hardness (unit: HV or HB); max Indicates the maximum hardness of the material in the current task; D indicates the density of the target material, usually expressed in kilograms per cubic meter (unit: kg / m 3 ), D max Expressed as the maximum density of the target material;

[0099] Specifically, the task time is expressed as:

[0100]

[0101] Among them, T left The remaining time of the current task, usually expressed in seconds or minutes. The smaller the task time, the higher the weight;

[0102] It should be noted that the weight value P can be obtained by adding the above formulas (1), (2), (3), and (4). By allocating weights, the larger the weight value, the higher the priority;

[0103] Furthermore, the target detection module receives the relative positions of the battery cell and the screw in the scene, and encapsulates them as operation parameters and transmits them to the programmable controller of the robot arm to generate operation instructions;

[0104] It should be noted that the relative position data is encapsulated so that the robot programmable controller does not need to perform complex calculations, but directly generates specific action instructions based on the transmitted parameters to guide the first rotating joint, the second moving joint and the third ball joint to adjust; in addition, the operation instructions are passed through a standardized operation parameter interface so that they can act not only on the current robot arm, but also on different robot arms, so that different robot arms can receive relative position data in the same format, which improves the portability of the automation system. Even when the robot arm is replaced or the system is upgraded, the operation process can still be seamlessly connected;

[0105] S2. If it is determined that the current target area image is a disordered stacking state after the battery cells are separated, the current grasping mode is switched to a clamping mode through the end effector, and the robot arm programmable controller is used to control the robot arm to grasp the battery cell position, and the grasped battery cell is stored;

[0106] Specifically, the clamping method is classified clamping;

[0107] Furthermore, before the clamping method is performed, the battery cells are classified by scanning the QR code on the battery cells, the classification results are saved in the battery cell category database, and the clamping force is set according to the battery cell category;

[0108] It should be noted that the clamping force needs to be set not only according to the chip type, but also according to the contact force distribution, friction coefficient and material of the end effector;

[0109] Furthermore, the process of controlling the robotic arm using a robotic arm programmable controller is as follows:

[0110] The robot receives the operation command issued by the DINO model and initializes the position and posture of the robot. The posture is composed of the first rotation joint A, the second mobile joint B and the third ball joint C.

[0111] It should be explained that the first revolute joint refers to allowing the robot arm to rotate around a fixed axis, the second mobile joint refers to allowing the robot arm to move in a straight line on the basis of the first revolute joint, and the third ball joint allows the end of the robot arm to rotate freely on multiple planes on the basis of the first revolute joint and the second mobile joint, that is, to control the rotation of the end effector;

[0112] The first rotation joint A calculates the angle A1 that the robotic arm currently needs to rotate according to the position of the target object that the robotic arm needs to grasp;

[0113] Specifically, the target object grasped by the robot arm here is a battery cell or a screw;

[0114] According to A1 and the placement angle L of the target object, the deviation value M=A1-L is calculated, and the distance B1 required to move from the second moving joint B to the current object is calculated, and B1 is compared with the set target moving distance B2;

[0115] If the deviation value M is not zero and the distance between B1 and B2 gradually increases, the third ball joint C is always adjusted to make the deviation value M between A1 and L approach zero, and make B1 approach B2 or B2 approach B1. Otherwise, when the deviation value M is equal to zero and B1 overlaps B2, the third ball joint C is not adjusted.

[0116] It should be noted that by adjusting the third ball joint, the end effector does not need to pay too much attention to its own posture and angle, which improves the working efficiency of the robot arm;

[0117] Furthermore, by considering the clamping tension, each time the robot arm clamps a battery cell, the clamping force changes with the type of battery cell, and the clamping tension changes with the size of the battery cell;

[0118] It should be noted that by scanning the QR code on the battery cell, you can only identify the type and model of the battery cell, but not the current size of the battery cell. The physical size obtained before is the size of the battery cell relative to the picture, not the actual size of the battery cell. Therefore, you need to consider the actual size of the battery cell and set the clamping tension.

[0119] It should be explained that the clamping tension is the maximum opening distance between the two jaws (or other forms of clamps) when the robot arm is clamping an object;

[0120] Furthermore, the shortest distance from the robot arm to the battery cell position is used as the objective function to establish the shortest path for the robot arm to grab the battery cell;

[0121] Specifically, the objective function Min(J) is:

[0122]

[0123] Where n is the number of cells, d(h i―1 ,h i ) indicates that the robot arm moves from position h i―1 Move to position h i The Euclidean sum distance d, P i Indicates the priority of cell i. The smaller the value, the higher the priority. i Expressed as the size of cell i, I i It is represented by the type of battery cell i;

[0124] It should be noted that the shortest path for grabbing the battery cell can be obtained by calculating the minimum objective function value, and since the relative position is obtained by the clustering algorithm before, the accuracy of the distance between the robot and the object does not need to be considered in the formula here, which greatly optimizes the shortest path calculation time;

[0125] S3. Otherwise, if the current target area image shows that there is no battery cell and only screws, the current grasping mode is switched to the electric screw mode through the end effector, the screw position in the current target area image is marked, and the screw model is identified according to the screw position in the current target area image, and the robot arm programmable controller is used to control the robot arm to grasp the screw position;

[0126] Specifically, the electric screwdriver method includes a plurality of electric screwdriver heads and the electric screwdriver heads are formed by an array;

[0127] Furthermore, there are several elements in the electric screwdriver bit array, each of which stores a type of electric screwdriver bit. The number of elements in the array is affected by the results of the hierarchical analysis module in the DINO model.

[0128] Furthermore, the robot arm selects a corresponding electric screwdriver bit according to the currently identified screw model, and sets the speed and torque of the electric screwdriver bit;

[0129] Specifically, at this time, the end effector will find the corresponding electric screwdriver bit through the electric screwdriver bit array to match the current screw model;

[0130] It should be noted that the speed and torque are taken into account because the traditional electric screwdriver operation can be used not only to collect screws but also to assemble and disassemble;

[0131] Furthermore, by considering the start-stop ratio of the electric screwdriver and the motion coefficient of the robot arm, the speed and torque of the electric screwdriver are adjusted;

[0132] It should be explained that the motion-stop ratio here refers to the ratio of the indexing time and the rest time of the end effector controlling the electric screwdriver array each time; the robot arm motion coefficient here refers to the period ratio of the robot arm to the rotation of the electric screwdriver array each time;

[0133] Specifically, the formula for the start-stop ratio k of the electric screwdriver is:

[0134]

[0135] Where z is the number of elements in the electric screwdriver bit array;

[0136] Specifically, the formula for the motion coefficient v of the robot arm is:

[0137]

[0138] It should be noted that the motion-stop ratio directly reflects the working efficiency of the end effector. A too high motion-stop ratio means that the end effector is performing the electric batching task most of the time, with less rest time and high efficiency. On the contrary, a too low motion-stop ratio indicates that the end effector spends more time waiting or adjusting, which is inefficient. Similarly, a too high motion coefficient means that the robot arm frequently performs high-energy consumption operations.

[0139] S4, displaying the result of the current target area image recognition, the current grasping mode of the end effector, the battery cell model and the screw model in the visual operation panel;

[0140] Furthermore, the visual operation panel is connected to the robot arm programmable controller, and the robot arm programmable controller and the end controller are regularly optimized through the display information in the visual operation panel;

[0141] It should be noted that the programmable controller of the robot arm can be updated regularly by displaying and recording the move-stop ratio of the electric screwdriver bit and the motion coefficient of the robot arm in the visual operation panel, and submitting the recorded values ​​to the DINO model for processing. The DINO model controls the number of electric screwdriver bits in the array of the end effector according to the screw types of all image scenes containing screws and no battery cells, so as to improve the move-stop ratio of the end effector and reduce the energy consumption of the robot arm; the programmable controller of the robot arm can be optimized by adjusting the weight distribution rules of the battery cells and screws to fine-tune the relative positions of the battery cells and screws to achieve the purpose of controlling the robot arm.

[0142] Example 2

[0143] Reference Figure 2 and Figure 3 , which is the second embodiment of the present invention, and this embodiment provides a method for switching and grasping and positioning an electric screwdriver for different screw models, including: In this embodiment, it is intended to verify the effectiveness and superiority of a method for switching and grasping and positioning an electric screwdriver for different screw models;

[0144] Experimental preparation: In order to make the simulation experiment real and effective, different types of battery cells and screws were selected as test objects. The specific screw types include: small aluminum alloy screws, medium stainless steel screws, large steel screws, and three different types of battery cells include: LG INR18650 M36, Samsung SDI 94Ah and BYD Blade LFP; the experimental environment is set up on a standard industrial robot arm ABB IRB 2600, equipped with an end effector Schunk PGN-plus 64 and a robot arm programmable controller ABB IRC5. The camera used is a Basler a2A1920-160umPRO industrial camera, equipped with a DINO model for image recognition and target detection;

[0145] Experimental process: Screws and batteries of different types are randomly stacked in a 2m 2 In the target area, the disordered stacking scene after the actual battery pack is disassembled is simulated; the camera device is installed above the robotic arm to take images of the area, and the image data is input into the DINO model;

[0146] The DINO model classifies and locates different types of battery cells and screws through hierarchical analysis and attention mechanism modules. Based on the recognition results, the end effector’s grasping mode is adjusted: when a battery cell is recognized, it switches to the clamping mode to accurately grasp and store the battery cell; when a screw is recognized, it switches to the electric screwdriver mode, selects the corresponding electric screwdriver head, and adjusts the speed and torque according to the screw characteristics.

[0147] In this simulation experiment, a variety of control groups were set up, including Swin Transformer, ViT (Vision Transformer), ResNet-50 models, as well as traditional preset models (rule-based image recognition and robotic arm control methods) and manual operation groups, in order to conduct a comprehensive comparative analysis. The results are shown in Table 1.

[0148] Table 1

[0149]

[0150]

[0151] From Table 1, we can see that the DINO model performs best, with a recognition accuracy of 98.5%, which is 1.5% higher than SwinTransformer, 2.2% higher than ViT, and 4.5% higher than ResNet-50. This shows that the DINO model has stronger recognition ability and accuracy when dealing with complex and diverse mixed stacking scenarios of screws and battery cells.

[0152] In terms of grasping efficiency, the DINO model can accurately grasp 20 cells per minute, surpassing the 18 of Swin Transformer and 17 of ViT. The grasping efficiency of the ResNet-50 model is 16, while the traditional preset model is only 12. This shows that the DINO model can complete tasks more efficiently in actual operation, especially in scenarios that require efficient and continuous operation.

[0153] In terms of screw installation rate, the DINO model has a success rate of 95% in screw installation, which means that when the DINO model handles different screw types, it can achieve more accurate parameter adjustment and operation guidance for the robot arm, effectively reducing installation errors and improving the overall stability of the production line;

[0154] In terms of average recognition time, the DINO model only takes 15 milliseconds to complete a recognition, which is 3 milliseconds faster than the SwinTransformer model and 5 milliseconds faster than the ViT model, and significantly better than the 22 milliseconds of the ResNet-50 model. This enables the DINO model to respond more quickly even in a high-paced production environment, reducing the cumulative effect of screw and battery cell recognition time.

[0155] In terms of the rotation time of the electric screwdriver bit, the DINO model's electric screwdriver bit rotation time is 2 seconds, which is faster than the 3 seconds of the Swin Transformer model. The ViT and ResNet-50 models require 3.5 seconds and 4 seconds of replacement time, respectively. As we all know, the rotation time of the electric screwdriver bit directly affects the continuity and efficiency of the production line. By reducing the rotation time, the production downtime can be significantly reduced in actual operation, thereby improving the overall production efficiency.

[0156] In terms of robot arm energy consumption, the DINO model's robot arm energy consumption is 0.45kWh / hour, which is lower than other models; this shows that under the same operating conditions, the robot arm controlled by the DINO model consumes less electricity and is more energy efficient; in comparison, the energy consumption of the Swin Transformer model and the ViT model are 0.50kWh / hour and 0.55kWh / hour respectively, while the energy consumption of the traditional preset model and manual operation is higher, at 0.75kWh / hour and 0.70kWh / hour respectively; this shows that the DINO model is not only superior in performance, but also has the advantage of reducing operating costs in energy consumption management;

[0157] Finally, combine Figure 2 It can be seen that even when the scene is changed, the accuracy of the push robot arm in grasping the target object can always be maintained at 99%; and reference Figure 3 ,After testing the robustness of the DINO model after the changed scene, it was found that the screw installation success rate and the mixed stacking recognition accuracy were always high, while the misoperation rate and the rotation time of the electric screwdriver were always low, indicating that the DINO model was highly robust;

[0158] In summary, this simulation experiment further demonstrated the significant advantages of the DINO model in controlling the robotic arm in terms of recognition accuracy, grasping efficiency, operational stability, and energy consumption management by introducing a variety of different types of battery cells and screws, demonstrating its potential and innovation in actual industrial applications.

[0159] Example 3

[0160] Reference Figure 4 , which is the third embodiment of the present invention, provides an electric screwdriver switching and gripping positioning system for different screw models, including:

[0161] The target area image acquisition and recognition module first uses the Basler a2A1920-160umPRO industrial camera installed on the robot arm to acquire images of the target area, which includes randomly stacked batteries and screws of different models. Secondly, the captured image data is input into the DINO model for analysis. Finally, the DINO model classifies all screws and batteries through its hierarchical analysis module, and identifies the specific location and model of each target object.

[0162] Grasping mode switching and operation module: First, according to the recognition result of the DINO model, the system controls the end effector to switch modes; when a battery cell is recognized, the end effector switches to the clamping mode, accurately grasps the battery cell and stores it in the specified location; when a screw is recognized, the end effector switches to the electric screwdriver mode and automatically selects the appropriate electric screwdriver bit according to the screw model; secondly, before grasping the screw, the robot arm will calculate the best grasping path through the controller to ensure efficient and accurate grasping operation; finally, the speed and torque of the electric screwdriver bit will be adjusted according to the specific model of the screw to ensure the success rate and stability of the installation;

[0163] Operation feedback and optimization module. First, during all operations, the robot’s operating status, current gripping mode, battery cell model, screw model and other information will be displayed in real time on the Siemens Simatic HMI TP1200 visual operation panel. Secondly, the operator can monitor the operation of the system through the panel and make manual interventions or adjustments as needed. Finally, the weight distribution and hierarchical analysis module of the DINO model will pass through the robot’s programmable controller and provide data feedback based on the robot’s operating results. The end effector will be optimized regularly to ensure the stability and efficiency of the robot’s programmable controller and end controller in long-term operation.

[0164] Those skilled in the art will appreciate that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0165] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0166] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0168] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0169] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for switching and grasping and positioning electric screwdrivers for different screw types, characterized in that: include: The target area image is acquired by a camera device, and according to the target area image recognition algorithm, it is determined whether the current target area image is a disordered stacking state after the battery cells are separated; If it is determined that the current target area image is a disordered stacking state after the battery cells are separated, the current grasping mode is switched to a clamping mode through the end effector, and the robot arm programmable controller is used to control the robot arm to grasp the battery cell position, and the grasped battery cell is stored; Otherwise, if the current target area image is in a state where there is no battery cell and only screws, the current grasping mode is switched to the electric screw mode through the end effector, the screw position in the current target area image is marked, and the screw model is identified according to the screw position in the current target area image, and the robot arm programmable controller is used to control the robot arm to grasp the screw position; The results of the current target area image recognition, the current grasping mode of the end effector, the battery cell model and the screw model are displayed in the visual operation panel.

2. The method for switching and grasping and positioning electric screwdrivers for different screw types as claimed in claim 1, characterized in that: Also includes: The electric screwdriver method includes a plurality of electric screwdriver heads and the electric screwdriver heads are composed of an array, and the clamping method is a classified clamping method; The visual operation panel is connected to the robot arm programmable controller, and the robot arm programmable controller and the end controller are optimized regularly through the display information in the visual operation panel.

3. The method for switching and grasping and positioning electric screwdrivers for different screw types as claimed in claim 1, characterized in that: The target area image recognition algorithm comprises: The target area image acquired by the camera device is represented as unlabeled data, and a scene data set is created to perform scene classification on the unlabeled data; After scene classification, the scene data set includes a state where there are no cells but only screws, and a disorderly stacking scene where the cells are separated; By inputting the scene dataset into the DINO model, the target image in the current area is identified.

4. The method for switching and grasping and positioning electric screwdrivers for different screw types as claimed in claim 3, characterized in that: The DINO model includes: Hierarchical analysis module, attention mechanism module and target detection module.

5. The method for switching and grasping and positioning electric screwdrivers for different screw types as claimed in claim 4, characterized in that: Hierarchical analysis module, including: According to the received scene data set, all screw types are classified into several categories, each category represents the thread shape, size or material of different screws, and the number of electric screwdriver bits in each layer is determined; Initialize the electric screwdriver array and set the number of initial array elements to zero; According to the current screw type, the electric screwdriver bit types are gradually added to the array.

6. The method for switching and grasping and positioning electric screwdrivers for different screw types as claimed in claim 4, characterized in that: The attention mechanism module and the target detection module include: The attention mechanism module performs weight assignment according to the scene data set, extracts all the batteries and screws in the scene data set, assigns weights to all the batteries and screws, sorts them from high to low according to the weights, and calculates the relative positions of the batteries and screws in the scene in sequence according to the sorting order, thereby obtaining the relative positions of all the batteries and screws in the scene; The target detection module receives the relative positions of the battery cell and the screw in the scene, and encapsulates them into operation parameters and transmits them to the programmable controller of the robot arm to generate operation instructions.

7. The method for switching and grasping and positioning electric screwdrivers for different screw types as claimed in claim 1, characterized in that: Use the robot arm programmable controller to control the robot arm, including: The robot arm receives the operation instruction issued by the DINO model and initializes the position and posture of the robot arm, wherein the posture is composed of the first rotation joint A, the second movement joint B and the third ball joint C; The first rotation joint A calculates the angle A1 that the robotic arm currently needs to rotate according to the position of the target object that the robotic arm needs to grasp; According to A1 and the placement angle L of the target object, a deviation value M=A1-L is calculated, and at the same time, a distance B1 required to be moved from the second moving joint B to the current object is calculated, and B1 is compared with a set target moving distance B2; If the deviation value M is not zero and the distance between B1 and B2 gradually increases, the third ball joint C is always adjusted to make the deviation value M between A1 and L approach zero, and make B1 approach B2 or B2 approach B1. Otherwise, when the deviation value M is equal to zero and B1 overlaps B2, the third ball joint C is not adjusted.

8. The method for switching and grasping and positioning electric screwdrivers for different screw types as claimed in claim 2 or 5, characterized in that: The electric screwdriver method includes a plurality of electric screwdriver heads and the electric screwdriver heads are formed by an array, and the clamping method is a classified clamping method, including: There are several elements in the array, each of which stores a type of electric screwdriver bit, and the number of elements in the array is affected by the results of the hierarchical analysis module in the DINO model; Before the clamping method is performed, the battery cells are classified by scanning the QR code on the battery cells, the classification results are saved in the battery cell category database, and the clamping force is set according to the battery cell category.

9. The method for switching and grasping and positioning electric screwdrivers for different screw types according to claim 1 or 8, characterized in that: The grabbing process of disorderly stacked discharge cores also includes: By considering the clamping tension, each time the robot arm clamps a battery cell, the clamping force changes with the type of battery cell, and the clamping tension changes with the size of the battery cell; Taking the shortest distance from the robot arm to the battery cell position as the objective function, the shortest path for the robot arm to grab the battery cell is established.

10. The method for switching and grasping and positioning electric screwdrivers for different screw types according to claim 1, characterized in that: The screw grabbing process also includes: The robot arm selects the corresponding electric screwdriver according to the currently identified screw model and sets the speed and torque of the electric screwdriver; The speed and torque of the electric screwdriver bit are adjusted by considering the start-stop ratio of the electric screwdriver bit and the motion coefficient of the robot arm.

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