A robot gripper grasping optimization method and device, a terminal and a medium

Abstract

The application discloses a robot clamp grabbing optimization method and device, a terminal and a medium. The technical scheme provided by the application first determines the model and data of a workpiece to be grabbed through an image of the workpiece to be grabbed, utilizes a preset workpiece recognition model, wherein the workpiece data includes geometric structure parameters, surface material, weight and barycentric position, then determines the surface friction coefficient of the workpiece to be grabbed according to the surface material in the workpiece data, determines the target clamping force according to the surface friction coefficient and the weight, and combines a preset clamping force constraint relationship, determines the target clamping force according to the surface friction coefficient and the weight, combines a preset clamping force constraint relationship, determines the target clamping force, determines the target grabbing point in the workpiece to be grabbed according to the geometric structure parameters and the barycentric position, and finally generates a clamp control instruction according to the calculated optimal grabbing parameter, so that the target clamp grabs the workpiece to be grabbed according to the clamp control instruction, thereby improving the accuracy and stability of grabbing and improving the success rate of grabbing.

Description

Technical Field

[0001] This application relates to the field of automation technology, and in particular to a method, apparatus, terminal and medium for optimizing robot gripping. Background Technology

[0002] Currently, in the construction, renovation, operation, and maintenance of power systems, the timeliness and efficiency of replacing faulty equipment are important indicators for evaluating the overall management level of the power grid. To improve operational efficiency and safety, power departments are deploying robots to replace manual labor in areas with complex terrain and limited space, such as substations. In substation robot operation scenarios, controlling the robotic gripper to grasp objects is one of the most common tasks. However, in actual application, unstable grasping and occasional false grasps still frequently occur. Summary of the Invention

[0003] This application provides a method, apparatus, terminal, and medium for optimizing robot gripping, which addresses the technical problem of false gripping that easily occurs in the prior art.

[0004] To address the aforementioned technical problems, the first aspect of this application provides an optimized method for robot gripper grasping, comprising:

[0005] Obtain the image of the workpiece to be grabbed;

[0006] Based on the workpiece image, the workpiece is identified using a preset workpiece recognition model to determine the workpiece recognition result to be grasped, and the workpiece data corresponding to the workpiece recognition result. The workpiece data includes: geometric structure parameters, surface material, weight, and center of gravity position.

[0007] Based on the surface material, the surface friction coefficient of the workpiece to be gripped is determined. Based on the surface friction coefficient and the weight, and combined with the preset clamping force constraint formula, the target clamping force is determined.

[0008] Based on the geometric parameters and the center of gravity position, the target gripping point in the workpiece to be gripped is determined;

[0009] Based on the target clamping force and the target gripping point, a fixture control command is generated so that the target fixture grips the workpiece to be gripped according to the fixture control command.

[0010] Preferably, the construction method of the workpiece recognition model includes:

[0011] Obtain a preset workpiece sample, and use the workpiece recognition results and workpiece parameters of the workpiece sample as training data to construct a workpiece recognition model.

[0012] Preferably, the clamping force constraint relationship is as follows:

[0013]

[0014] In the formula, For the target clamping force, The surface friction coefficient is... The total weight of the workpiece to be grasped. The weight of the workpiece to be grasped. This is the acceleration due to gravity.

[0015] Preferably, determining the target gripping point in the workpiece to be gripped based on the geometric parameters and the center of gravity position specifically includes:

[0016] Based on the geometric parameters, several candidate gripping points are determined on the surface of the workpiece to be gripped.

[0017] Based on the center of gravity position and combined with the preset torque constraint relationship, the torque calculation value corresponding to each candidate gripping point is calculated.

[0018] Based on the calculated torque value, the candidate gripping point corresponding to the minimum calculated torque value among the workpieces to be gripped is selected as the target gripping point.

[0019] Preferably, the torque constraint relationship is as follows:

[0020]

[0021] In the formula, d is the target clamping force, d is the horizontal distance between the candidate gripping point and the center of gravity, and N is the calculated torque value corresponding to the candidate gripping point.

[0022] Preferably, after generating fixture control instructions to enable the target fixture to grip the workpiece according to the fixture control instructions, the process further includes:

[0023] The actual clamping force of the target fixture and the relative attitude data of the target fixture are collected by the sensors in the target fixture.

[0024] Based on the error between the actual clamping force and the target clamping force, the actual clamping force of the target fixture is adjusted using a PID optimization algorithm;

[0025] Based on the relative posture data, the grasping posture of the target fixture is adjusted using a preset inverse kinematics calculation formula.

[0026] Preferably, it further includes:

[0027] Obtain the gripping pressure threshold range corresponding to the workpiece identification result, compare the actual clamping force with the gripping pressure threshold range, and issue a gripping alarm message when the actual clamping force exceeds the gripping pressure threshold range.

[0028] Meanwhile, a second aspect of this application provides a robot gripper grasping optimization device, comprising:

[0029] The workpiece image acquisition unit is used to acquire the workpiece image of the workpiece to be grasped;

[0030] The workpiece recognition unit is used to recognize the workpiece based on the workpiece image using a preset workpiece recognition model, determine the workpiece recognition result of the workpiece to be grasped, and the workpiece data corresponding to the workpiece recognition result, wherein the workpiece data includes: geometric structure parameters, surface material, weight, and center of gravity position.

[0031] The target clamping force determination unit is used to determine the surface friction coefficient of the workpiece to be gripped based on the surface material, and to determine the target clamping force based on the surface friction coefficient and the weight, combined with a preset clamping force constraint formula.

[0032] The target gripping point determination unit is used to determine the target gripping point in the workpiece to be gripped based on the geometric structure parameters and the center of gravity position.

[0033] The gripping action execution unit is used to generate a fixture control command based on the target clamping force and the target gripping point, so that the target fixture grips the workpiece to be gripped according to the fixture control command.

[0034] A third aspect of this application provides a robot gripper grasping optimization terminal, including: a memory and a processor;

[0035] The memory is used to store program code, which corresponds to a robot gripper grasping optimization method as provided in the first aspect of this application;

[0036] The processor is used to read and execute the program code.

[0037] The fourth aspect of this application provides a computer-readable storage medium storing program code, which is read and executed by a processor to implement a robot gripper grasping optimization method as provided in the first aspect of this application.

[0038] As can be seen from the above technical solutions, this application has the following advantages:

[0039] The technical solution provided in this application first uses an image of the workpiece to be gripped and a preset workpiece recognition model to determine the workpiece's model and data. The workpiece data includes geometric parameters, surface material, weight, and center of gravity position. Then, based on the surface material in the workpiece data, the surface friction coefficient of the workpiece to be gripped is determined. Based on the surface friction coefficient and weight, combined with a preset clamping force constraint formula, the target clamping force is determined. Based on the geometric parameters and center of gravity position, the target gripping point in the workpiece to be gripped is determined. Finally, based on the calculated optimal gripping parameters, a fixture control command is generated so that the target fixture grips the workpiece to be gripped according to the fixture control command, thereby improving the gripping accuracy and stability, and increasing the gripping success rate. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating an embodiment of a robot gripper grasping optimization method provided in this application.

[0042] Figure 2 This is a flowchart illustrating additional steps in an embodiment of a robot gripper grasping optimization method provided in this application.

[0043] Figure 3 This is a schematic diagram of an embodiment of a robot gripper grasping optimization device provided in this application.

[0044] Figure 4 This is a schematic diagram of an embodiment of a robot gripper optimized terminal provided in this application. Detailed Implementation

[0045] This application provides a method, apparatus, terminal, and medium for optimizing robot gripping, which addresses the technical problem of false gripping that easily occurs in the prior art.

[0046] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] Please see Figure 1 This application provides an embodiment of a robot gripper grasping optimization method, which includes:

[0048] Step 101: Obtain the image of the workpiece to be grabbed.

[0049] Step 102: Based on the workpiece image, identify the workpiece using a preset workpiece recognition model to determine the workpiece recognition result to be grasped, and the workpiece data corresponding to the workpiece recognition result.

[0050] The workpiece data includes: geometric parameters, surface material, weight, and center of gravity position.

[0051] It should be noted that the method provided in this embodiment firstly acquires a workpiece image of the workpiece to be grasped through the vision device built into the robot arm, and then performs recognition based on the workpiece image using a preset workpiece recognition model to determine the workpiece recognition result of the workpiece to be grasped, as well as the workpiece data corresponding to the workpiece recognition result.

[0052] It is understood that the workpiece recognition model mentioned in this embodiment is specifically an AI learning model built based on preset workpiece samples. The specific construction method can be as follows: first, according to the workpiece model that needs to be captured in the application scenario, obtain the corresponding workpiece samples and set a unique workpiece recognition identifier for each workpiece sample. Then, collect the workpiece parameters of each workpiece sample through laser scanning or measuring instruments, etc. The workpiece recognition identifier and workpiece parameters are used as training data to obtain the workpiece recognition model mentioned in this embodiment.

[0053] More specifically, the workpiece data includes: geometric structural parameters, surface material, weight, and center of gravity position. Among them, the geometric structural parameters can be the three-dimensional geometric model of the workpiece. The acquisition method can be geometric analysis, which involves obtaining the three-dimensional contour data of the workpiece through laser scanning or measuring instruments, and establishing a three-dimensional geometric model of the workpiece based on this. With the bottom of the workpiece as the origin, a rectangular coordinate system can be established to determine the geometric parameters of the workpiece, such as height and diameter.

[0054] The weight W and center of gravity G can be determined using physical measuring instruments. The formula for calculating the center of gravity is:

[0055]

[0056] Here, represents the mass of a certain part of the workpiece. It is the distance from the centroid of this part to the bottom of the workpiece in the vertical direction. It is the total mass of the workpiece.

[0057] Step 103: Determine the surface friction coefficient of the workpiece to be gripped based on the surface material. Based on the surface friction coefficient and weight, and combined with the preset clamping force constraint formula, determine the target clamping force.

[0058] Next, a detailed analysis and calculation are performed based on the characteristic parameters of the workpiece to be gripped. Assume the workpiece is a substation porcelain insulator, cylindrical in shape with a smooth surface, weighing W, and its center of gravity is located at the geometric center of the cylinder, 50cm from the bottom. These characteristics of the insulator directly affect the force applied during gripping by the fixture.

[0059] To ensure stable gripping, the clamping force of the fixture must be large enough to overcome the weight of the porcelain bottle and prevent it from slipping. The required clamping force is determined by calculating friction, specifically the coefficient of friction between the fixture and the porcelain bottle surface. The weight of the porcelain bottle is W, the acceleration due to gravity is g, and the total weight of the porcelain bottle is:

[0060]

[0061] To prevent the porcelain bottle from slipping, the clamping force required by the fixture The following conditions must be met:

[0062]

[0063] Step 104: Determine the target gripping point in the workpiece to be gripped based on the geometric structure parameters and the position of the center of gravity.

[0064] Next, during the application of force by the fixture, the workpiece is subjected not only to vertical clamping force but also to horizontal force and torque. These torques are primarily caused by the distance between the gripping point and the center of gravity. If the gripping point deviates from the workpiece's center of gravity, a torque around the center of gravity will be generated during gripping, affecting the stability of the grip. Assuming the horizontal distance between the gripping point and the center of gravity is d, the torque N on the workpiece is:

[0065]

[0066] This formula is used to calculate the torque acting on the workpiece. If the torque is too large, the workpiece will rotate or tilt during gripping. Therefore, the gripping position should be as close as possible to the workpiece's center of gravity to reduce such torque.

[0067] Step 105: Generate fixture control instructions based on the target clamping force and the target gripping point, so that the target fixture can grip the workpiece to be gripped according to the fixture control instructions.

[0068] It should be noted that the geometric center of the workpiece to be grasped is assumed to have the following coordinates in the robot arm's coordinate system: The workpiece's center of gravity is located at its geometric center, and the fixture's design parameters include the maximum opening angle of the grippers. and minimum opening angle The gripping point should be located close to the workpiece's center of gravity to reduce the generation of additional torque and ensure gripping stability.

[0069] First, the pose of the robotic arm's end effector is described using a forward kinematics model. Assuming the robotic arm is a 6-DOF robot, the pose of its end effector is determined by a homogeneous transformation matrix. Description. This matrix can be represented as:

[0070]

[0071] Where R is a 3x3 rotation matrix, represents the direction of the end effector, and is a displacement vector. , indicating the position of the end effector.

[0072] Next, the joint angles of the robotic arm are calculated using inverse kinematics to position its end effector to grasp the workpiece. The geometric center of the workpiece is known to be... By adjusting the pose of the robotic arm, the displacement vector p of the end effector is made to satisfy the following condition:

[0073]

[0074] At the same time, the rotation matrix R needs to ensure that the direction of the end effector is parallel to the axis of the workpiece to ensure that the fixture can properly clamp the workpiece. For example, for a cylindrical porcelain bottle, the rotation matrix should align the clamping direction of the fixture with the axis of the workpiece, which can be represented by Euler angles or quaternions.

[0075] To determine the gripping point location and the clamping angle, the workpiece dimensions and surface characteristics are further analyzed. Assume the workpiece diameter is d and the clamping jaw radius is... So, what is the opening angle of the clamp? It can be calculated using geometric relationships:

[0076]

[0077] The opening angle of the clamp It should be within the operable range of the fixture, that is:

[0078]

[0079] This formula ensures that the opening angle of the fixture is suitable for the size of the workpiece, guaranteeing the stability of the gripping process.

[0080] In the robotic arm coordinate system, the optimal gripping posture depends not only on the workpiece's position and the gripper's opening and closing angles, but also on the forces acting on the workpiece. The gripping force of the fixture was calculated. Let N be the torque of the workpiece. To minimize torque and optimize gripping posture, the gripping point should be close to the workpiece's center of gravity. Assume the offset of the workpiece's center of gravity relative to its geometric center is... Location of the capture point It should meet the following requirements:

[0081]

[0082] The position of the gripping point reduces the torque generated during clamping, ensuring the balance of the grip. An optimization algorithm calculates the optimal pose of the robotic arm's end effector, thereby obtaining the gripping point position of the workpiece to be gripped and the opening angle of the fixture. Based on the optimal gripping parameters calculated above, corresponding fixture control commands are generated, enabling the target fixture to grip the workpiece according to the control commands.

[0083] Furthermore, such as Figure 2 As shown, the process of controlling the target fixture to grip the workpiece based on fixture control commands may also include the following steps:

[0084] Step 106: Collect the actual clamping force and relative attitude data of the target fixture using the sensors in the target fixture;

[0085] Step 107: Based on the error between the actual clamping force and the target clamping force, adjust the actual clamping force of the target fixture using a PID optimization algorithm;

[0086] Step 108: Based on the relative posture data, adjust the gripping posture of the target fixture using a preset inverse kinematics calculation formula.

[0087] It should be noted that sensor feedback is a key factor in the actual gripping process, mainly including pressure sensors and position sensors. Pressure sensors are installed on the gripping parts of the gripper to monitor the pressure applied to the workpiece surface in real time. Position sensors monitor the attitude changes of the robotic arm and gripper, ensuring they match the position and attitude of the workpiece.

[0088] Based on the gripping force value fed back by the pressure sensor, the system determines whether the currently applied gripping force is appropriate. If the pressure value detected by the sensor is lower than the set minimum gripping force value, it indicates unstable gripping and a potential risk of slippage, and the system will automatically increase the clamping force of the fixture. Conversely, if the pressure is too high, exceeding the safe stress range of the workpiece material, the system will reduce the pressure of the fixture to avoid workpiece damage. The specific parameter calculation for adjusting the force is achieved by a PID controller (proportional, integral, derivative controller). By comparing the set target gripping force with the real-time measured gripping force, the system automatically adjusts the magnitude of the clamping force. The calculation formula is:

[0089]

[0090] in, It is the difference between the target pressure and the actual pressure. These are the proportional, integral, and differential gain coefficients, respectively. This is the adjustment signal output by the system. This formula updates the gripping force in real time, making the actual gripping force approach the target value.

[0091] Simultaneously, position and angle sensors are used to monitor the relative posture of the gripper and the robotic arm. If workpiece misalignment or inaccurate robotic arm posture occurs during gripping, the system detects the posture error through sensor feedback. The parameters for posture adjustment can be calculated using inverse kinematics, and the gripping point's pose is changed by adjusting the joint angles of the robotic arm. The angle adjustment amount for each joint of the robotic arm can be calculated using inverse kinematics formulas:

[0092]

[0093] in, It refers to the angle adjustment of each joint of the robotic arm. It is the inverse of the Jacobian matrix. It is the deviation between the current pose of the workpiece and the target pose. By continuously adjusting the joint angles of the robotic arm, the workpiece is always kept in the predetermined grasping posture.

[0094] Furthermore, it also includes:

[0095] Obtain the gripping pressure threshold range corresponding to the workpiece recognition result, compare the actual clamping force with the gripping pressure threshold range, and issue a gripping alarm message when the actual clamping force exceeds the gripping pressure threshold range.

[0096] It should be noted that, when dealing with target workpieces, such as porcelain insulators in substations, the gripping force applied by the grippers is gradually adjusted for different types of insulators, and the pressure generated by the grippers is monitored in real time by a built-in pressure sensor. During each gripping process, the success of the gripping, as well as whether any false gripping or damage occurs, are recorded. Through these experimental samples, the pressure range required for successful gripping can be obtained. After statistical analysis, the maximum pressure required for successful gripping can be calculated. and minimum pressure These values ​​will be used to establish a baseline for gripping pressure. Next, the average successful gripping pressure value will be calculated. And based on this, set warnings and safety thresholds. For example, a warning limit. ( ) and lower limit ( This will ensure that the gripper force is effectively adjusted within the control range, thereby avoiding damage caused by exceeding the safety threshold or false gripping when it is below the lower limit.

[0097] Ultimately, a suitable pressure threshold range is established for each workpiece type, and the control system algorithm automatically selects the corresponding pressure threshold based on the identified workpiece type, achieving real-time monitoring and adjustment. This method ensures optimized gripping of different workpieces, thereby minimizing the occurrence of false grips. The pressure within this range represents the appropriate gripping degree. Exceeding this range may damage the gripped object, while falling below this range may result in a false grip.

[0098] During the actual gripping process, the pressure applied to the workpiece by the grippers will be monitored in real time, and the real-time pressure value will be obtained through the built-in pressure sensor. First, set an initial gripping force. Based on the previously determined pressure threshold range, i.e. Determine the current capture status. If < This indicates insufficient gripping force, which may result in a weak grip. In this case, it's necessary to increase the pressure applied to the object by adjusting the gripper's force F. Typically, an increment can be set. If k < 1, If the current gripping force is maintained, the gripping process will continue. Conversely, if the force is too high, it indicates that excessive force has been applied, which may damage the workpiece. In this case, the gripping action must be stopped immediately, the grippers released, and the pressure gradually reduced until it returns to a safe range. Furthermore, the operator should be alerted via an alarm system or indicator lights to prevent repeated errors. The system must continuously monitor the force after each adjustment. This dynamic feedback mechanism determines the gripping state and the next action, ensuring that the gripper's gripping strength remains within an acceptable range, thus preventing accidents.

[0099] By following the steps above, the degree of grasp can be determined, minimizing the possibility of false grasps. This improves the accuracy and stability of the grasp, protects the integrity of the grasped object, and increases the success rate of the grasp.

[0100] The above is a detailed description of an embodiment of a robot gripper grasping optimization method provided in this application. The following is a detailed description of an embodiment of a robot gripper grasping optimization device provided in this application.

[0101] Please see Figure 3 This application provides an embodiment of a robot gripper grasping optimization device, comprising:

[0102] The workpiece image acquisition unit 201 is used to acquire the workpiece image of the workpiece to be grasped;

[0103] The workpiece recognition unit 202 is used to recognize the workpiece based on the workpiece image by using a preset workpiece recognition model, and to determine the workpiece recognition result of the workpiece to be grasped, as well as the workpiece data corresponding to the workpiece recognition result. The workpiece data includes: geometric structure parameters, surface material, weight and center of gravity position.

[0104] The target clamping force determination unit 203 is used to determine the surface friction coefficient of the workpiece to be gripped based on the surface material, and to determine the target clamping force based on the surface friction coefficient and weight, combined with the preset clamping force constraint relationship.

[0105] The target gripping point determination unit 204 is used to determine the target gripping point in the workpiece to be gripped based on the geometric structure parameters and the position of the center of gravity.

[0106] The gripping action execution unit 205 is used to generate fixture control commands based on the target clamping force and the target gripping point, so that the target fixture grips the workpiece to be gripped according to the fixture control commands.

[0107] In addition, this application also provides a detailed description of an embodiment of a robot gripper grasping optimization terminal and an embodiment of a computer-readable storage medium, as follows:

[0108] like Figure 4 As shown, the third aspect of this application provides a robot gripper grasping optimization method, including: a memory 33 and a processor 31, wherein the memory 33 and the processor 31 can be connected via a communication bus 34;

[0109] The memory 33 is used to store program code, which corresponds to a robot gripper grasping optimization method provided in the above embodiments;

[0110] Processor 31 is used to read and execute program code.

[0111] This application provides an embodiment of a computer-readable storage medium, in which program code is stored. The program code is read and executed by a processor to implement a robot gripper grasping optimization method as provided in the above embodiment.

[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the terminals, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0114] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0115] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0116] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0117] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0119] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An optimized method for robot gripper grasping, characterized in that, include: Obtain the image of the workpiece to be grabbed; Based on the workpiece image, the workpiece is identified using a preset workpiece recognition model to determine the workpiece recognition result to be grasped, and the workpiece data corresponding to the workpiece recognition result. The workpiece data includes: geometric structure parameters, surface material, weight, and center of gravity position. Based on the surface material, the surface friction coefficient of the workpiece to be gripped is determined. Based on the surface friction coefficient and the weight, and combined with the preset clamping force constraint formula, the target clamping force is determined. Based on the geometric parameters, several candidate gripping points are determined on the surface of the workpiece to be gripped. Based on the center of gravity position and combined with the preset torque constraint relationship, the torque calculation value corresponding to each candidate gripping point is calculated. Based on the calculated torque value, the candidate gripping point corresponding to the minimum calculated torque value in the workpiece to be gripped is taken as the target gripping point; Based on the target clamping force and the target gripping point, a fixture control command is generated so that the target fixture grips the workpiece to be gripped according to the fixture control command. The actual clamping force of the target fixture and the relative attitude data of the target fixture are collected by the sensors in the target fixture. Based on the error between the actual clamping force and the target clamping force, the actual clamping force of the target fixture is adjusted using a PID optimization algorithm; Based on the relative posture data, the grasping posture of the target fixture is adjusted using a preset inverse kinematics calculation formula; The clamping force constraint relationship is specifically as follows: In the formula, For the target clamping force, The surface friction coefficient is... The total weight of the workpiece to be grasped. The weight of the workpiece to be grasped. It is the acceleration due to gravity; The torque constraint relationship is specifically as follows: In the formula, d is the target clamping force, d is the horizontal distance between the candidate gripping point and the center of gravity, and N is the calculated torque value corresponding to the candidate gripping point.

2. The robot gripper grasping optimization method according to claim 1, characterized in that, The construction methods of the workpiece recognition model include: Obtain a preset workpiece sample, and use the workpiece recognition results and workpiece parameters of the workpiece sample as training data to construct a workpiece recognition model.

3. The robot gripper grasping optimization method according to claim 1, characterized in that, Also includes: Obtain the gripping pressure threshold range corresponding to the workpiece identification result, compare the actual clamping force with the gripping pressure threshold range, and issue a gripping alarm message when the actual clamping force exceeds the gripping pressure threshold range.

4. A robot gripper grasping optimization device, characterized in that, include: The workpiece image acquisition unit is used to acquire the workpiece image of the workpiece to be grasped; The workpiece recognition unit is used to recognize the workpiece based on the workpiece image using a preset workpiece recognition model, determine the workpiece recognition result of the workpiece to be grasped, and the workpiece data corresponding to the workpiece recognition result, wherein the workpiece data includes: geometric structure parameters, surface material, weight, and center of gravity position. The target clamping force determination unit is used to determine the surface friction coefficient of the workpiece to be gripped based on the surface material, and to determine the target clamping force based on the surface friction coefficient and the weight, combined with a preset clamping force constraint formula. The target gripping point determination unit is used to determine a number of candidate gripping points on the surface of the workpiece to be gripped according to the geometric structure parameters, calculate the torque calculation value corresponding to each candidate gripping point according to the center of gravity position and combined with the preset torque constraint relationship, and select the candidate gripping point corresponding to the minimum torque calculation value in the workpiece to be gripped as the target gripping point according to the torque calculation value. The gripping action execution unit is used to generate a fixture control command based on the target clamping force and the target gripping point, so that the target fixture grips the workpiece to be gripped according to the fixture control command. The gripping motion adjustment unit is used to collect the actual gripping force and relative posture data of the target clamp through sensors in the target clamp, adjust the actual gripping force of the target clamp based on the error between the actual gripping force and the target gripping force through a PID optimization algorithm, and adjust the gripping posture of the target clamp based on the relative posture data through a preset inverse kinematics calculation formula. The clamping force constraint relationship is specifically as follows: In the formula, For the target clamping force, The surface friction coefficient is... The total weight of the workpiece to be grasped. The weight of the workpiece to be grasped. It is the acceleration due to gravity; The torque constraint relationship is specifically as follows: In the formula, d is the target clamping force, d is the horizontal distance between the candidate gripping point and the center of gravity, and N is the calculated torque value corresponding to the candidate gripping point.

5. A robot gripper optimized terminal, characterized in that, include: Memory and processor; The memory is used to store program code, which corresponds to a robot gripper grasping optimization method as described in any one of claims 1 to 3; The processor is used to read and execute the program code.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that is read and executed by a processor to implement a robot gripper grasping optimization method as described in any one of claims 1 to 3.

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