Six-axis robot control method and system for bolt and nut disassembly

Abstract

The application discloses a six-axis robot control method and system for bolt and nut disassembly, relates to the technical field of robot control, and comprises the following steps: receiving a bolt disassembly task for analysis, and determining the task instruction of a target bolt; performing jaw control on the six-axis robot, integrating jaw clamping control parameters to perform screwing control, and generating real-time disassembly parameters; comparing the torque feedback value with the torque preset value in multiple directions based on the angle feedback value, and performing screwing simulation adjustment to generate screwing simulation control parameters; performing backtracking verification on the screwing simulation control parameters, and updating the screwing simulation control parameters in a closed loop to construct a bolt and nut disassembly control strategy. The application solves the technical problems of existing technologies, such as control strategy solidification, inability to adjust and backtracking verification according to real-time working conditions, low disassembly precision, poor adaptability, and inability to self-optimize the control strategy, and achieves the technical effects of improving the bolt disassembly precision, adaptability and reliability of the six-axis robot under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, specifically to a six-axis robot control method and system for assembling and disassembling bolts and nuts. Background Technology

[0002] The connection quality of bolt and nut connections directly affects the structural safety and operational reliability of equipment. Using industrial robots to replace manual labor for bolt and nut assembly and disassembly has become an important trend to improve production efficiency and ensure assembly consistency. Traditional bolt assembly and disassembly methods often rely on dedicated tightening shafts or torque-controlled tools, typically only capable of fixing bolts along a single axis. This makes them ill-suited for handling bolt groups in complex spatial configurations or at multiple angles. Six-axis industrial robots, with their high mobility, can reach various complex working positions, providing the physical basis for fully automated bolt assembly and disassembly. However, in practical applications, bolt assembly and disassembly control has several limitations. Based on preset torque control, they ignore fluctuations in the friction coefficient, thread deformation, and resistance changes under different working conditions during the actual engagement process, failing to achieve high-precision, smooth assembly. Furthermore, they struggle to dynamically adjust the robot's tightening posture and speed based on real-time torque and angle data. When encountering jamming or stripping, the robot cannot respond promptly, leading to assembly / disassembly failures or even workpiece damage. Additionally, they cannot use the results of a single assembly / disassembly to validate the current control strategy and perform self-optimizing closed-loop updates. This means that in mass production, the system cannot adaptively adjust when workpiece tolerances or conditions change, resulting in a decreased yield.

[0003] Therefore, current technologies suffer from technical problems such as rigid control strategies, inability to adjust and backtest based on real-time operating conditions, resulting in low assembly and disassembly accuracy, poor adaptability, and inability to self-optimize control strategies. Summary of the Invention

[0004] This application provides a six-axis robot control method and system for bolt and nut assembly and disassembly, which solves the technical problems in the prior art, such as fixed control strategies, inability to adjust and backtest according to real-time working conditions, resulting in low assembly and disassembly accuracy, poor adaptability and inability to self-optimize control strategies. It achieves the technical effect of improving the bolt assembly and disassembly accuracy, adaptability and reliability of six-axis robots under complex working conditions.

[0005] This application provides a six-axis robot control method for bolt and nut assembly / disassembly. The method includes: receiving and parsing a bolt assembly / disassembly task to determine the task instruction for the target bolt, the task instruction including specification parameters and a preset torque value; controlling the gripper of the six-axis robot according to the specification parameters, integrating gripper holding control parameters to control the six-axis robot to perform turning control, generating real-time assembly / disassembly parameters, the real-time assembly / disassembly parameters including torque feedback value and angle feedback value; comparing the torque feedback value with the preset torque value in multiple directions based on the angle feedback value, adjusting the six-axis robot to perform turning simulation based on the comparison results, generating turning simulation control parameters; performing backtracking verification on the turning simulation control parameters, updating the turning simulation control parameters in a closed loop based on the verification results, and constructing a bolt and nut assembly / disassembly control strategy.

[0006] In a possible implementation, a bolt disassembly / assembly task is received and parsed to determine the task instruction for the target bolt. The task instruction includes specification parameters and a preset torque value. The method includes: receiving the bolt disassembly / assembly task, which includes the target bolt's model information and process requirements; performing natural language parsing on the bolt disassembly / assembly task, traversing the model information and process requirements to extract key model fields and key process fields; matching the key model fields against a bolt model database to determine the bolt's geometric features and material properties; digitizing the key process fields to determine the torque control target; performing a structured analysis on the target bolt based on the torque control target and the material properties data to determine the specification parameters; and performing a structured analysis on the target bolt based on the torque control target and the bolt's geometric features to determine the preset torque value.

[0007] In a possible implementation, the bolt geometric features and material properties are determined by matching the bolt model database with the key model field. The method includes: analyzing the target bolt based on the key model field to determine the bolt code information and thread specification information; using the bolt code information and thread specification information as primary keys to traverse the bolt model database and generate search results; when a match is found in the search results, the bolt geometric features and material properties are retrieved from the bolt model database; when no match is found, an online learning mode is triggered to visually scan the target bolt, extract its geometric features, and manually synchronize material properties data.

[0008] In a possible implementation, the gripper control of the six-axis robot is performed according to the specified parameters. The gripper clamping control parameters are integrated to control the six-axis robot to perform screwing control, generating real-time assembly / disassembly parameters. The method includes: performing gripping analysis on the six-axis robot based on the specified parameters to determine the gripping torque range; using the gripping torque range as a constraint to perform gripper opening analysis and set gripper opening size parameters; performing gripper morphology analysis on the six-axis robot based on the gripper opening size parameters to determine gripper three-dimensional morphology data; performing integrated gripper morphology control on the six-axis robot based on the gripper three-dimensional morphology data to generate gripper clamping control parameters, which include target clamping morphology parameters; activating the gripper's shape memory alloy drive element, and performing deformation control based on the shape memory alloy drive element according to the target clamping morphology parameters to determine the head contour parameters of the target bolt; controlling the six-axis robot to perform screwing control according to the head contour parameters of the target bolt, and reading the gripper pressure feedback value in real time; mapping the gripper pressure feedback value to the gripping torque range for comparison and adjustment to generate the real-time assembly / disassembly parameters.

[0009] In a possible implementation, the gripper pressure feedback value is mapped to the clamping torque range for comparison and adjustment to generate the real-time disassembly and assembly parameters. The method includes: analyzing the clamping torque range and extracting the maximum and minimum clamping forces; comparing the gripper pressure feedback value with the maximum and minimum clamping forces item by item to determine whether the pressure feedback value is within the clamping force range: S1: calculating the difference between the pressure feedback value and the minimum clamping force to obtain a first difference; S2: calculating the difference between the pressure feedback value and the maximum clamping force to obtain a second difference; performing clamping force analysis based on the first and second differences to determine the clamping force state for comparison and adjustment, and generating real-time disassembly and assembly parameters.

[0010] In a possible implementation, clamping force analysis is performed based on the first difference and the second difference to determine the clamping force state for comparison and adjustment, generating real-time disassembly and assembly parameters. The method includes: based on the first difference, if the clamping force state is that the pressure feedback value is lower than the minimum clamping force, a pressure increase command is generated; the pressure feedback value is gradually increased using the pressure increase command until the pressure feedback value reaches the clamping torque range; based on the second difference, if the clamping force state is that the pressure feedback value is higher than the maximum clamping force, a pressure reduction command is generated; the pressure feedback value is gradually reduced using the pressure reduction command until the pressure feedback value reaches the clamping torque range; based on the first difference and the second difference, if the clamping force state is that the pressure feedback value is within the clamping torque range, a clamping stabilization signal is sent to the six-axis robot according to the clamping force state.

[0011] In a possible implementation, the torque feedback value is compared with the preset torque value in multiple directions based on the angle feedback value. The six-axis robot is then adjusted using a twisting simulation based on the comparison results to generate twisting simulation control parameters. The method includes: using the angle feedback value as a reference, arranging the torque feedback value and the preset torque value in a corresponding direction according to the increasing angle to determine multiple angle node sequences; longitudinally comparing the torque feedback value and the preset torque value according to the multiple angle node sequences to calculate the torque deviation values ​​at multiple angle nodes; performing trend analysis based on the torque deviation values ​​at multiple angle nodes according to the multiple angle node sequences to draw a trend graph; retrieving multiple continuous angle intervals and combining them with the trend graph for a lateral comparison to identify the torque deviation change rate and torque deviation change direction data; integrating the torque deviation values ​​at multiple angle nodes with the torque deviation change rate and torque deviation change direction data to obtain a comparison result, which includes torque deviation distribution information and torque change trend information; and performing a twisting simulation adjustment on the six-axis robot based on the torque deviation distribution information and torque change trend information to generate the twisting simulation control parameters.

[0012] In a possible implementation, the six-axis robot is adjusted using a twisting simulation based on the torque deviation distribution information and the torque change trend information to generate the twisting simulation control parameters. The method includes: extracting the initial torque deviation value based on the torque deviation distribution information; setting initial twisting speed simulation parameters based on the initial torque deviation value; extracting the torque deviation value of characteristic angle nodes based on the torque deviation distribution information; setting torque output limit parameters for multiple angle positions based on the torque deviation value of the characteristic angle nodes; traversing the torque change trend information to perform characteristic change analysis and delineate the characteristic change interval positions; performing simulation analysis based on the initial twisting speed simulation parameters to generate multiple twisting speed candidate values; performing simulation analysis based on the torque output limit parameters to generate multiple torque value candidate values; calculating torque ramp-up based on the characteristic change interval positions to calculate multiple ramp-up rate candidate values; combining the multiple twisting speed candidate values, the multiple torque value candidate values, and the multiple ramp-up rate candidate values ​​to generate multiple sets of candidate twisting simulation control parameters for screening, and finally determining the twisting simulation control parameters.

[0013] In a possible implementation, multiple sets of candidate screwing simulation control parameters are generated and screened to determine the screwing simulation control parameters. The method includes: performing screwing dynamics simulation based on the multiple sets of candidate screwing simulation control parameters to obtain multiple sets of simulation parameters, including a target simulation torque value and a target simulation angle value; comparing the target simulation torque value with a preset torque value to calculate the torque approximation degree; comparing the target simulation angle value with a preset angle value to calculate the angle approximation degree; performing a weighted summation based on the torque approximation degree and the angle approximation degree to generate a weighted summation result; sorting the multiple sets of candidate screwing simulation control parameters in descending order of control effect according to the weighted summation result to generate a control effect sequence; and traversing the control effect sequence to extract the first-order data as the screwing simulation control parameter.

[0014] This application also provides a six-axis robot control system for bolt and nut assembly / disassembly. The system includes: an assembly / disassembly task parsing module, used to receive and parse bolt assembly / disassembly tasks, determine the task instruction for the target bolt, the task instruction including specification parameters and a preset torque value; an assembly / disassembly parameter generation module, used to control the gripper of the six-axis robot according to the specification parameters, integrate gripper holding control parameters to control the six-axis robot to perform turning control, and generate real-time assembly / disassembly parameters, the real-time assembly / disassembly parameters including torque feedback value and angle feedback value; a control parameter generation module, used to perform multi-directional comparison between the torque feedback value and the preset torque value based on the angle feedback value, and perform turning simulation adjustment of the six-axis robot according to the comparison result to generate turning simulation control parameters; and a control strategy construction module, used to perform backtracking verification of the turning simulation control parameters, update the turning simulation control parameters in a closed loop according to the verification result, and construct a bolt and nut assembly / disassembly control strategy.

[0015] This application proposes a six-axis robot control method and system for bolt and nut assembly / disassembly. The method receives and analyzes bolt assembly / disassembly tasks to determine the target bolt's task instruction. It then controls the six-axis robot's gripper, integrating gripper holding control parameters to execute tightening control and generate real-time assembly / disassembly parameters. Based on angle feedback values, it compares torque feedback values ​​with preset torque values ​​in multiple directions and performs tightening simulation adjustments to generate tightening simulation control parameters. Finally, it performs backtracking verification and closed-loop updates of these parameters, constructing a bolt and nut assembly / disassembly control strategy. This solution addresses the technical problems in existing technologies, such as fixed control strategies, inability to adjust and backtrack based on real-time working conditions, resulting in low assembly / disassembly accuracy, poor adaptability, and the inability to self-optimize control strategies. It achieves the technical effect of improving the bolt assembly / disassembly accuracy, adaptability, and reliability of six-axis robots under complex working conditions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a schematic flowchart of a six-axis robot control method for disassembling and assembling bolts and nuts provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of a six-axis robot control system for bolt and nut assembly / disassembly provided in an embodiment of this application.

[0019] Explanation of reference numerals in the attached diagram: Disassembly and assembly task parsing module 10, disassembly and assembly parameter generation module 20, control parameter generation module 30, control strategy construction module 40. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structure, features, and effects of the present invention.

[0021] This application provides a six-axis robot control method for assembling and disassembling bolts and nuts, such as... Figure 1 As shown, the method includes: Step S100: Receive bolt disassembly and assembly task, parse it, and determine the task instruction for the target bolt. The task instruction includes specification parameters and preset torque value.

[0022] Step S100 further includes receiving a bolt disassembly / assembly task, the bolt disassembly / assembly task containing the target bolt's model information and process requirement information; performing natural language parsing on the bolt disassembly / assembly task, traversing the model information and process requirement information to extract key model fields and key process fields; matching the bolt model database based on the key model fields to determine the bolt's geometric features and material property data; digitizing the key process fields to determine the torque control target; performing a structured analysis on the target bolt based on the torque control target and the material property data to determine the specification parameters; and performing a structured analysis on the target bolt based on the torque control target and the bolt's geometric features to determine the preset torque value.

[0023] Preferably, the received bolt disassembly and assembly task is parsed to determine the specification parameters and preset torque values, thus forming the task instruction for the target bolt. Specifically, the bolt disassembly and assembly task is the basic text or voice input data of the target bolt and its disassembly and assembly method, including the target bolt's model information and process requirements information. Among them, the model information refers to the identification of the target bolt, such as "M10×1.5-8.8 grade" or "hexagonal head bolt ISO". "4014-M12" refers to the technical constraints on the disassembly and assembly operation, such as "tightening torque 120 N·m" or "disassembling rusted bolts," as well as the target process values. Natural language processing is used to handle the bolt disassembly and assembly task, traversing the model information and process requirement information to extract key model fields, such as "M10" and "8.8 grade," and key process fields, such as "120" and "N·m." The bolt model database is a pre-built database based on standard bolts, used to store parameters for various types of standard bolts, including at least bolt geometric features and material properties. The bolt model database is searched using key model fields as query conditions to determine the geometric features and material properties of the target bolt. Bolt geometric features refer to the bolt's physical dimensions, such as thread outer diameter, pitch, head width across flats, head thickness, and shank length. Material properties refer to the bolt's material and force... The process involves: 1) Analyzing the properties of the bolt, such as tensile strength, yield strength, material grade, and surface treatment; 2) digitizing key process fields and converting them into standardized values ​​that the control system can use for mathematical calculations (e.g., converting the text "120 N·m" into the floating-point number "120.0" and standardizing the units to determine the torque control target); 3) performing a structured comprehensive analysis and verification of the torque control target and the bolt's material properties to confirm whether the torque is within the bolt's elastic deformation range, thereby determining the physical specifications that the robot should follow during clamping and tightening, such as preventing excessive pressure from the grippers from damaging the bolt surface; and 4) performing a structured analysis of the target bolt using the torque control target and the bolt's geometric characteristics, calculating the specific control torque target value that the robot's end effector needs to output during actual tightening using a thread mechanics model (i.e., the torque preset value), which is used to compare with the torque feedback value in real-time control.

[0024] Furthermore, step S100 also includes analyzing the target bolt based on the model key fields to determine the bolt code information and thread specification information; using the bolt code information and the thread specification information as primary keys to traverse the bolt model database for retrieval and generate retrieval results; when the retrieval result shows a matching item, the bolt geometric features and material property data are retrieved based on the bolt model database; when the retrieval result shows no matching item, an online learning mode is triggered to visually scan the target bolt, extract the bolt geometric features, and manually synchronize the material property data.

[0025] Preferably, the target bolt is structurally disassembled and analyzed using key model fields to determine its bolt designation and thread specification information. The bolt designation refers to the standard designation or type name of the bolt, such as "GB / T 5782" (hexagonal head bolt standard) or "ISO 4014," while the thread specification information refers to the specific dimensional parameters of the thread, such as "M10" (nominal diameter 10mm) or "2.0" (pitch 2.0mm). The bolt designation and thread specification information are combined into a joint query condition as the primary key, and a precise search is performed in the bolt model database to generate search results, including "matches exist" and "matches do not exist." If a match exists, it means that a record in the bolt model database completely matches the target bolt's designation and thread specification information. The bolt's geometric features, such as head size and thread length, are then directly read from this record. The search results include information such as the width of opposite sides and material properties, such as yield strength, recommended torque, and material type. If no matching item is found, it indicates that the target bolt is a non-standard bolt or a new model. In this case, the online learning mode is triggered. The six-axis robot uses a camera or laser scanner and other vision sensors to scan and measure the bolt in three dimensions. The robot obtains the bolt's geometric features in real time, such as the head shape, size, and screw length. The human-machine interface prompts the operator to manually input the bolt's material information or select the corresponding material property parameters to complete the data entry.

[0026] Step S200: Control the gripper of the six-axis robot according to the specifications and parameters, integrate the gripper holding control parameters to control the six-axis robot to perform twisting control, and generate real-time disassembly and assembly parameters, which include torque feedback value and angle feedback value.

[0027] Step S200 further includes: performing a clamping analysis on the six-axis robot based on the specified parameters to determine the clamping torque range; using the clamping torque range as a constraint to perform an opening analysis on the gripper and setting gripper opening size parameters; performing a gripper morphology analysis on the six-axis robot based on the gripper opening size parameters to determine the gripper's three-dimensional morphology data; performing integrated gripper morphology control on the six-axis robot according to the gripper's three-dimensional morphology data to generate gripper clamping control parameters, which include target clamping morphology parameters; activating the gripper's shape memory alloy drive element, and performing deformation control based on the shape memory alloy drive element according to the target clamping morphology parameters to determine the head contour parameters of the target bolt; controlling the six-axis robot to perform screwing control according to the head contour parameters of the target bolt, and reading the gripper pressure feedback value in real time; mapping the gripper pressure feedback value to the clamping torque range for comparison and adjustment to generate the real-time disassembly and assembly parameters.

[0028] Preferably, the gripper of the six-axis robot is controlled according to the specifications, and the clamping force is adjusted in real time during the tightening process so that the gripper automatically adapts to the shape of the bolt head and dynamically maintains the optimal clamping torque based on real-time pressure feedback. Specifically, the gripping of the six-axis robot is analyzed according to the specifications, and the minimum and maximum clamping forces that the gripper needs to apply are obtained through mechanical calculations to ensure that the bolt head or threads are not damaged, thus determining the clamping torque range. Then, the clamping torque range is used as a constraint, and combined with the theoretical dimensions of the bolt head, the initial distance that the two clamping surfaces of the gripper should open is calculated, and the gripper opening size parameters are set. Finally, the gripper morphology of the six-axis robot is analyzed according to the gripper opening size parameters to determine the specific shape and position of the gripper in space and obtain the three-dimensional morphological data of the gripper.

[0029] Preferably, the three-dimensional morphological data of the gripper is converted into control commands to perform integrated gripper morphological control on the six-axis robot, obtaining gripper clamping control parameters. These parameters include target clamping morphological parameters, the core of which is the specific shape of the gripper deformation. Next, the shape memory alloy driving element inside the gripper is energized or heated, causing it to undergo a phase change and deformation according to the control commands. During deformation, the gripper makes actual contact with the bolt head. The shape memory alloy senses stress and naturally conforms to the actual contour of the bolt head, thereby accurately measuring the current true contour parameters of the bolt head and determining the head contour parameters of the target bolt. The gripper controls the six-axis robot to perform tightening or loosening operations based on the head contour parameters of the target bolt. During tightening, the current real-time clamping pressure data is continuously read from the pressure sensor on the gripper as the gripper pressure feedback value. Finally, the gripper pressure feedback value is mapped to a clamping torque range for comparison. If the pressure is too low, slippage may occur, so the clamping force is increased; if the pressure is too high, damage to the bolt may occur, so the clamping force is decreased. Ultimately, real-time disassembly and assembly parameters containing the current stable torque and angle feedback values ​​are generated.

[0030] Furthermore, step S200 also includes: analyzing the clamping torque range, extracting the maximum and minimum clamping forces; comparing the gripper pressure feedback value with the maximum and minimum clamping forces item by item to determine whether the pressure feedback value is within the clamping force range: S1: calculating the difference between the pressure feedback value and the minimum clamping force to obtain a first difference; S2: calculating the difference between the pressure feedback value and the maximum clamping force to obtain a second difference; performing clamping force analysis based on the first and second differences, determining the clamping force state for comparison and adjustment, and generating real-time disassembly and assembly parameters.

[0031] Preferably, the real-time read gripper pressure data is quantitatively compared with the safe clamping force range, and the current clamping state is determined by difference calculation. This allows for numerical analysis and state determination of the pressure feedback value. Specifically, the clamping torque range is analyzed to extract the minimum and maximum clamping force values, representing the minimum pressure value required for safe clamping and the maximum pressure value allowed for safe clamping, respectively. Below the minimum clamping force value, clamping may be insufficient or slippage may occur; above the maximum clamping force value, bolts or tools may be damaged. Then, the gripper pressure feedback value is compared item by item with the maximum and minimum clamping force values ​​to determine whether the pressure feedback value is within the clamping force range. This includes calculating the deviation between the gripper pressure feedback value and the maximum and minimum clamping force values, and determining the first and second differences. Specifically, if the first difference is positive, it indicates that the current pressure is higher than the minimum clamping force; if the first difference is negative, it indicates that the current pressure is lower than the lower safety limit. If the second difference is negative, it indicates that the current pressure is lower than the maximum clamping force; if the second difference is positive, it indicates that the current pressure exceeds the upper safety limit. Finally, the clamping force is analyzed by combining the sign and magnitude of the first and second differences to determine the clamping force status. If the first difference is less than 0, the status is "pressure too low"; if the second difference is greater than 0, the status is "pressure too high"; if the first difference is greater than or equal to 0 and the second difference is less than or equal to 0, the status is "pressure normal". Finally, based on the determined clamping force status, it is decided whether to increase, decrease, or maintain the pressure, and the real-time disassembly and assembly parameters are recorded based on the adjusted stable state.

[0032] Furthermore, step S200 also includes: based on the first difference, if the clamping force state is that the pressure feedback value is lower than the minimum clamping force, then a pressure increase command is generated; the pressure feedback value is gradually increased through the pressure increase command until the pressure feedback value reaches the clamping torque range; based on the second difference, if the clamping force state is that the pressure feedback value is higher than the maximum clamping force, then a pressure reduction command is generated; the pressure feedback value is gradually reduced through the pressure reduction command until the pressure feedback value reaches the clamping torque range; based on the first difference and the second difference, if the clamping force state is that the pressure feedback value is within the clamping torque range, then a clamping stabilization signal is sent to the six-axis robot according to the clamping force state.

[0033] Preferably, based on the first difference, if the clamping force status is such that the pressure feedback value is lower than the minimum clamping force, it indicates that the current actual clamping pressure has not reached the safety lower limit. In this case, a pressure increase command is generated to increase the clamping force of the robot gripper. The pressure feedback value is gradually increased according to the pressure increase command, for example, by a fixed step size. After each increase, the real-time pressure feedback value is continuously read and compared with the clamping torque range until the pressure feedback value enters the clamping torque range between the safety minimum and maximum values, at which point the pressure increase stops. Similarly, based on the second difference, if the clamping force status is such that the pressure feedback value is higher than the maximum clamping force, it indicates that the current actual clamping pressure has not reached the safety lower limit. If the clamping pressure exceeds the safety limit, a pressure reduction command is generated to reduce the clamping force of the robot gripper. The pressure feedback value is gradually reduced according to the pressure reduction command, for example, by a fixed step size. After each reduction step, the real-time pressure feedback value is continuously read and compared with the clamping torque range until the pressure feedback value falls within the safe minimum and maximum clamping torque range, at which point the pressure reduction stops. A judgment is made by combining the first and second differences. If the clamping force state is such that the pressure feedback value is within the clamping torque range, it indicates that the robot's clamping state is stable and reliable. A clamping stability signal is then sent to the main controller of the six-axis robot, allowing the robot to continue the twisting operation.

[0034] Step S300: Based on the angle feedback value, the torque feedback value is compared with the torque preset value in multiple directions. Based on the comparison results, the six-axis robot is adjusted by a twisting simulation to generate twisting simulation control parameters.

[0035] Step S300 further includes: using the angle feedback value as a benchmark, arranging the torque feedback value and the preset torque value in a corresponding manner according to the direction of angle increase to determine multiple angle node sequences; comparing the torque feedback value and the preset torque value longitudinally according to the multiple angle node sequences to calculate the torque deviation value of multiple angle nodes; performing trend analysis based on the torque deviation value of multiple angle nodes according to the multiple angle node sequences to draw a trend graph; retrieving multiple continuous angle intervals and combining them with the trend graph for a lateral comparison to identify the torque deviation change rate and torque deviation change direction data; integrating the torque deviation value of multiple angle nodes with the torque deviation change rate and torque deviation change direction data to obtain a comparison result, the comparison result including torque deviation distribution information and torque change trend information; and performing a twisting simulation adjustment on the six-axis robot according to the torque deviation distribution information and the torque change trend information to generate the twisting simulation control parameters.

[0036] Preferably, using the rotation angle feedback value as a benchmark, a multi-dimensional data comparison analysis is performed between the actual torque and the preset torque, and the twisting simulation control parameters are generated based on the analysis results. Specifically, using the actual rotation angle feedback value as a benchmark, the actual torque feedback value corresponding to each angle position is arranged in an increasing direction with the theoretical preset torque value, forming multiple angle node sequences arranged in angle order. Then, at each selected angle node, the actual torque feedback value is compared longitudinally with the preset torque value to calculate the torque deviation value at that specific angle position, determining the torque deviation values ​​of multiple angle nodes. For example, the extent to which the actual torque feedback value is greater than the preset torque value when rotating 30°. Then, the torque deviation values ​​of multiple angle nodes are analyzed for trends according to the multiple angle node sequences, and the deviation values ​​at all angle nodes are connected in angle order to draw a trend graph, representing the torque deviation with rotation angle. The overall trend of change is observed, such as the deviation increasing or stabilizing. Next, multiple continuous angle intervals, such as 30°~60°, are retrieved and compared laterally with the trend graph. The rate of change of the deviation value per unit angle is calculated to determine the torque deviation change rate, i.e., the slope of the curve. The direction of torque deviation change is calculated to determine whether the deviation is increasing positively or decreasing negatively, i.e., the direction of the curve. The torque deviation values ​​at multiple angle nodes are integrated with the torque deviation change rate and torque deviation change direction data to generate a comparison result, including torque deviation distribution information and torque change trend information, representing the torque deviation at all rotation angles and the evolution trend of the deviation. Finally, using the torque deviation distribution information and torque change trend information, a twisting simulation adjustment is performed on the six-axis robot. For example, if the deviation change rate is found to be too large in a certain angle interval, the twisting speed or torque output in that interval is adjusted to generate the final twisting simulation control parameters.

[0037] Furthermore, step S300 also includes: extracting the initial torque deviation value based on the torque deviation distribution information; setting initial twisting speed simulation parameters based on the initial torque deviation value; extracting characteristic angle node torque deviation values ​​based on the torque deviation distribution information; setting torque output limit parameters for multiple angle positions based on the characteristic angle node torque deviation values; traversing the torque change trend information to perform characteristic change analysis and delineating characteristic change interval positions; performing simulation analysis based on the initial twisting speed simulation parameters to generate multiple twisting speed candidate values; performing simulation analysis based on the torque output limit parameters to generate multiple torque value candidate values; performing torque ramp-up calculation based on the characteristic change interval positions to calculate multiple ramp-up rate candidate values; combining the multiple twisting speed candidate values, the multiple torque value candidate values, and the multiple ramp-up rate candidate values ​​to generate multiple sets of candidate twisting simulation control parameters for screening and determining the twisting simulation control parameters.

[0038] Preferably, the torque deviation characteristics are transformed into specific control parameter candidate values, and multiple candidate control schemes for twisting simulation are generated by combination. Specifically, the torque deviation value at the instant of twisting begins is identified from the torque deviation distribution information to determine the initial torque deviation value, and the initial twisting speed simulation parameters are set according to the initial torque deviation value. If the initial torque deviation value is large, it indicates that there is abnormal resistance from the beginning, so a slower initial twisting speed simulation parameter is set. From the torque deviation distribution information, the torque deviation values ​​at representative angle nodes such as deviation peaks and valleys are identified. For the torque deviation values ​​at these key locations, torque output limit parameters at multiple angle positions are set respectively, that is, the maximum allowable output torque value is determined to prevent overshoot. The torque change trend information is traversed and feature change analysis is performed to identify and determine the intervals where the trend changes significantly, such as the inflection point region where the deviation changes from a slow increase to a sharp increase. The process involves defining the characteristic variation intervals, including marking the start and end positions of these intervals on the angle axis. Then, based on the initial twisting speed simulation parameters, simulation analysis is performed using simulation software to generate multiple possible twisting speed values. For example, multiple candidate twisting speed values ​​are obtained by randomly floating around the initial twisting speed. Next, simulation analysis is performed based on the torque output limit parameters at each angle position to generate multiple possible torque output values ​​as torque value candidates. Then, torque ramp-up calculation is performed according to the characteristic variation interval positions, i.e., calculating the rate of torque change with angle, to determine multiple candidate ramp-up rates for possible torque, i.e., the torque increase per degree of rotation. Finally, the multiple candidate twisting speed values, multiple candidate torque values, and multiple candidate ramp-up rates are arranged and combined to generate multiple sets of candidate twisting simulation control parameters containing speed, torque limits, and ramp-up rates. These are then selected to determine the optimal twisting simulation control parameters.

[0039] Furthermore, step S300 also includes performing a twisting dynamics simulation based on the multiple sets of candidate twisting simulation control parameters to obtain multiple sets of simulation parameters, the multiple sets of simulation parameters including a target simulation torque value and a target simulation angle value; comparing the target simulation torque value with a preset torque value to calculate the torque approximation degree; comparing the target simulation angle value with a preset angle value to calculate the angle approximation degree; performing a weighted summation based on the torque approximation degree and the angle approximation degree to generate a weighted summation result; sorting the multiple sets of candidate twisting simulation control parameters in descending order of control effect according to the weighted summation result to generate a control effect sequence; and traversing the control effect sequence to extract the first-order data as the twisting simulation control parameter.

[0040] Preferably, multiple sets of candidate twisting simulation control parameters are input into twisting dynamics simulation software for twisting dynamics simulation. After each set of parameters is simulated, corresponding simulation result data is generated, including the target simulated torque value and the target simulated angle value. These represent the final torque value reached at the end of the simulation under the candidate twisting simulation control parameters and the total twisting angle value during the simulation, respectively, thus determining multiple sets of simulation parameters. For each set of simulation parameters, the target simulated torque value is compared with the preset torque value, and the torque approximation degree is calculated through difference normalization. This is used to measure the accuracy of the set of simulation parameters in torque control. The closer the target simulated torque value is to the preset torque value, the higher the torque approximation degree. Similarly, the total twisting angle obtained from the simulation is compared with the preset angle value required by the process. The angle approximation degree is calculated to measure the accuracy of the simulation parameters in angle control. The closer the target simulated angle value is to the preset angle value, the higher the angle approximation degree. Based on the importance of torque and angle in the actual process, different weight coefficients are assigned to the torque approximation degree and the angle approximation degree, respectively. Then, the torque approximation degree and the angle approximation degree are weighted and summed to obtain a weighted sum result, which is used to comprehensively evaluate the overall control effect of the simulation parameters. Finally, the weighted sum results of all candidate screwing simulation control parameters are sorted from high to low to generate a control effect sequence from best to worst. The first-order data of the control effect sequence is then extracted as the screwing simulation control parameter for actual six-axis robot control to ensure the bolt assembly and disassembly accuracy and reliability of the six-axis robot under complex working conditions.

[0041] S400, perform backtracking verification on the screwing simulation control parameters, update the screwing simulation control parameters in a closed loop based on the verification results, and construct a bolt and nut disassembly and assembly control strategy.

[0042] Preferably, the screwing simulation control parameters are applied to an actual six-axis robot to perform real bolt disassembly and assembly operations. The actual physical execution results are used for backtracking verification to check whether the screwing simulation control parameters determined in the simulation stage are true and effective. Then, the actual torque value, angle value, and fluctuations achieved during the verification process are collected and compared with the expected target to obtain the verification results. If the verification results are ideal, the screwing simulation control parameters are confirmed. If there are deviations in the verification results, such as the actual torque not reaching the preset value or abnormal fluctuations occurring during the process, the parameter adjustment process is initiated based on the actual deviation data to correct and optimize the screwing simulation control parameters. Finally, the screwing control parameters that have been actually tested and are stable and reliable are determined and integrated with control processes including gripper control, real-time comparison, and adjustment logic to generate a bolt and nut disassembly and assembly control strategy for the current bolt disassembly and assembly task, so that similar bolt disassembly and assembly tasks can directly call or refer to it, thereby ensuring the bolt disassembly and assembly accuracy and adaptability of the six-axis robot under complex working conditions.

[0043] In the above text, refer to Figure 1 A six-axis robot control method for assembling and disassembling bolts and nuts according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 A six-axis robot control system for bolt and nut assembly / disassembly is described according to an embodiment of the present invention.

[0044] The six-axis robot control system for bolt and nut assembly / disassembly according to embodiments of the present invention addresses the technical problems in the prior art, such as fixed control strategies, inability to adjust and backtest based on real-time working conditions, resulting in low assembly / disassembly accuracy, poor adaptability, and inability to self-optimize control strategies. It achieves the technical effect of improving the bolt assembly / disassembly accuracy, adaptability, and reliability of the six-axis robot under complex working conditions. Figure 2 As shown, the six-axis robot control system for bolt and nut assembly and disassembly includes: assembly and disassembly task analysis module 10, assembly and disassembly parameter generation module 20, control parameter generation module 30, and control strategy construction module 40.

[0045] The disassembly and assembly task parsing module 10 is used to receive and parse bolt disassembly and assembly tasks, determine the task instruction for the target bolt, and the task instruction includes specification parameters and a preset torque value; the disassembly and assembly parameter generation module 20 is used to control the gripper of the six-axis robot according to the specification parameters, integrate gripper holding control parameters to control the six-axis robot to perform turning control, and generate real-time disassembly and assembly parameters, which include torque feedback values ​​and angle feedback values; the control parameter generation module 30 is used to perform multi-directional comparison between the torque feedback value and the preset torque value based on the angle feedback value, and perform turning simulation adjustment of the six-axis robot according to the comparison results to generate turning simulation control parameters; the control strategy construction module 40 is used to perform backtracking verification on the turning simulation control parameters, update the turning simulation control parameters in a closed loop according to the verification results, and construct a bolt and nut disassembly and assembly control strategy.

[0046] The following will describe in detail the specific configuration of the disassembly and assembly task parsing module 10. The disassembly and assembly task parsing module 10 further includes: receiving bolt disassembly and assembly tasks, wherein the bolt disassembly and assembly tasks contain target bolt model information and process requirement information; performing natural language parsing on the bolt disassembly and assembly tasks, traversing the model information and process requirement information to extract key model fields and key process fields; matching the bolt model database based on the key model fields to determine the bolt geometric features and material property data; digitizing the key process fields to determine the torque control target; performing structured analysis on the target bolt based on the torque control target and the material property data to determine the specification parameters; and performing structured analysis on the target bolt based on the torque control target and the bolt geometric features to determine the preset torque value.

[0047] The following will describe in detail the specific configuration of the disassembly and assembly task parsing module 10. The disassembly and assembly task parsing module 10 further includes: analyzing the target bolt based on the aforementioned model key fields to determine the bolt code information and thread specification information; using the bolt code information and thread specification information as primary keys to traverse the bolt model database for retrieval, generating retrieval results; when the retrieval result shows a matching item, then calling the bolt geometric features and material property data based on the bolt model database; when the retrieval result shows no matching item, then triggering an online learning mode to visually scan the target bolt, extracting the bolt geometric features, and manually synchronizing the material property data.

[0048] The specific configuration of the disassembly and assembly parameter generation module 20 will be described in detail below. The disassembly and assembly parameter generation module 20 further includes: performing clamping analysis on the six-axis robot based on the specified parameters to determine the clamping torque range; using the clamping torque range as a constraint to perform opening analysis on the gripper and set gripper opening size parameters; performing gripper morphology analysis on the six-axis robot based on the gripper opening size parameters to determine the gripper's three-dimensional morphology data; performing gripper morphology integrated control on the six-axis robot according to the gripper's three-dimensional morphology data to generate gripper clamping control parameters, which include target clamping morphology parameters; activating the gripper's shape memory alloy drive element, and performing deformation control based on the shape memory alloy drive element according to the target clamping morphology parameters to determine the head contour parameters of the target bolt; controlling the six-axis robot to perform screwing control according to the head contour parameters of the target bolt, and reading the gripper pressure feedback value in real time; mapping the gripper pressure feedback value to the clamping torque range for comparison and adjustment to generate the real-time disassembly and assembly parameters.

[0049] The following will describe in detail the specific configuration of the disassembly and assembly parameter generation module 20. The disassembly and assembly parameter generation module 20 further includes: analyzing the clamping torque range, extracting the maximum and minimum clamping force values; comparing the gripper pressure feedback value with the maximum and minimum clamping force values ​​item by item to determine whether the pressure feedback value is within the clamping force range: S1: Calculating the difference between the pressure feedback value and the minimum clamping force value to obtain a first difference; S2: Calculating the difference between the pressure feedback value and the maximum clamping force value to obtain a second difference; performing clamping force analysis based on the first and second differences, determining the clamping force state for comparison and adjustment, and generating real-time disassembly and assembly parameters.

[0050] The specific configuration of the disassembly and assembly parameter generation module 20 will be described in detail below. The disassembly and assembly parameter generation module 20 further includes: based on the first difference, if the clamping force state is that the pressure feedback value is lower than the minimum clamping force, then generating a pressure increase command; gradually increasing the pressure feedback value through the pressure increase command until the pressure feedback value reaches the clamping torque range; based on the second difference, if the clamping force state is that the pressure feedback value is higher than the maximum clamping force, then generating a pressure reduction command; gradually reducing the pressure feedback value through the pressure reduction command until the pressure feedback value reaches the clamping torque range; based on the first difference and the second difference, if the clamping force state is that the pressure feedback value is within the clamping torque range, then sending a clamping stabilization signal to the six-axis robot according to the clamping force state.

[0051] The specific configuration of the control parameter generation module 30 will be described in detail below. The control parameter generation module 30 further includes: using the angle feedback value as a reference, arranging the torque feedback value and the torque preset value in a corresponding manner according to the direction of angle increase to determine multiple angle node sequences; longitudinally comparing the torque feedback value and the torque preset value according to the multiple angle node sequences to calculate the torque deviation value of the multiple angle nodes; performing trend analysis based on the torque deviation values ​​of the multiple angle nodes according to the multiple angle node sequences to draw a trend graph; retrieving multiple continuous angle intervals and combining them with the trend graph for lateral comparison to identify the torque deviation change rate and torque deviation change direction data; integrating the torque deviation values ​​of the multiple angle nodes with the torque deviation change rate and torque deviation change direction data to obtain a comparison result, the comparison result including torque deviation distribution information and torque change trend information; and performing a twisting simulation adjustment on the six-axis robot based on the torque deviation distribution information and the torque change trend information to generate the twisting simulation control parameters.

[0052] The specific configuration of the control parameter generation module 30 will be described in detail below. The control parameter generation module 30 further includes: extracting the initial torque deviation value based on the torque deviation distribution information; setting initial twisting speed simulation parameters based on the initial torque deviation value; extracting characteristic angle node torque deviation values ​​based on the torque deviation distribution information; setting torque output limit parameters for multiple angle positions based on the characteristic angle node torque deviation values; traversing the torque change trend information to perform characteristic change analysis and delineate characteristic change interval positions; performing simulation analysis based on the initial twisting speed simulation parameters to generate multiple twisting speed candidate values; performing simulation analysis based on the torque output limit parameters to generate multiple torque value candidate values; performing torque ramp-up calculation based on the characteristic change interval positions to calculate multiple ramp-up rate candidate values; combining the multiple twisting speed candidate values, the multiple torque value candidate values, and the multiple ramp-up rate candidate values ​​to generate multiple sets of candidate twisting simulation control parameters for screening and determining the twisting simulation control parameters.

[0053] The specific configuration of the control parameter generation module 30 will be described in detail below. The control parameter generation module 30 further includes: performing a twisting dynamics simulation based on the multiple sets of candidate twisting simulation control parameters to obtain multiple sets of simulation parameters, including a target simulation torque value and a target simulation angle value; comparing the target simulation torque value with a preset torque value to calculate the torque approximation degree; comparing the target simulation angle value with a preset angle value to calculate the angle approximation degree; performing a weighted summation based on the torque approximation degree and the angle approximation degree to generate a weighted summation result; sorting the multiple sets of candidate twisting simulation control parameters in descending order of control effect according to the weighted summation result to generate a control effect sequence; and traversing the control effect sequence to extract the first-order data as the twisting simulation control parameter.

[0054] The six-axis robot control system for bolt and nut assembly / disassembly provided in this embodiment of the invention can execute the six-axis robot control method for bolt and nut assembly / disassembly provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A six-axis robot control method for disassembling and assembling bolts and nuts, characterized in that, The method includes: The task of receiving bolt disassembly and assembly is analyzed to determine the task instruction for the target bolt, which includes specification parameters and preset torque values. The six-axis robot is controlled by grippers according to the specifications and parameters. The gripper holding control parameters are integrated to control the six-axis robot to perform twisting control and generate real-time disassembly and assembly parameters, which include torque feedback value and angle feedback value. Based on the angle feedback value, the torque feedback value is compared with the torque preset value in multiple directions. According to the comparison results, the six-axis robot is adjusted by a twisting simulation to generate twisting simulation control parameters. The screwing simulation control parameters are backtracked and verified. Based on the verification results, the screwing simulation control parameters are updated in a closed loop to construct a bolt and nut disassembly and assembly control strategy.

2. The six-axis robot control method for bolt and nut assembly / disassembly as described in claim 1, characterized in that, The process involves receiving and parsing bolt disassembly / assembly tasks to determine the task instruction for the target bolt. This task instruction includes specifications and a preset torque value. The method includes: Receive bolt disassembly and assembly tasks, wherein the bolt disassembly and assembly tasks include the target bolt's model information and process requirements information; The bolt disassembly and assembly task is parsed using natural language, and the model information and process requirement information are traversed to extract key model fields and key process fields; Based on the key model fields, match the bolt model database to determine the bolt's geometric features and material properties. The key process fields are digitized to determine the torque control target; Based on the torque control target and the material property data, a structural analysis is performed on the target bolt to determine the specification parameters; Based on the torque control target and the bolt geometry, a structural analysis is performed on the target bolt to determine the preset torque value.

3. The six-axis robot control method for bolt and nut assembly / disassembly as described in claim 2, characterized in that, Based on the aforementioned key model fields, a bolt model database is matched to determine the bolt's geometric features and material properties. The method includes: Based on the aforementioned key fields of the model, the target bolt is analyzed to determine the bolt code information and thread specification information; The bolt code information and the thread specification information are used as primary keys to traverse the bolt model database and retrieve the search results. When the search result indicates that a match exists, the bolt geometric features and material properties data are retrieved based on the bolt model database. When the search result shows no matching item, the online learning mode is triggered. The target bolt is visually scanned through the online learning mode to extract the bolt's geometric features, and the material property data is synchronized manually.

4. The six-axis robot control method for bolt and nut assembly / disassembly as described in claim 1, characterized in that, The six-axis robot is controlled by grippers according to the specifications and parameters. The gripper holding control parameters are integrated to control the six-axis robot to perform twisting control, and real-time assembly / disassembly parameters are generated. The method includes: Based on the aforementioned specifications, a clamping analysis was performed on the six-axis robot to determine the clamping torque range. The clamping torque range is used as a constraint to perform an opening analysis on the gripper, and the gripper opening size parameters are set. Based on the gripper opening size parameters, the gripper morphology of the six-axis robot is analyzed to determine the three-dimensional morphology data of the gripper. The gripper morphology integrated control of the six-axis robot is performed according to the gripper 3D morphology data to generate gripper clamping control parameters, which include target clamping morphology parameters. The shape memory alloy driving element of the gripper is activated, and the deformation is controlled according to the target clamping shape parameters based on the shape memory alloy driving element to determine the head contour parameters of the target bolt. The six-axis robot is controlled to perform screwing control according to the head contour parameters of the target bolt, and the gripper pressure feedback value is read in real time. The gripper pressure feedback value is mapped to the clamping torque range for comparison and adjustment to generate the real-time assembly and disassembly parameters.

5. The six-axis robot control method for bolt and nut assembly / disassembly as described in claim 4, characterized in that, The method involves mapping the gripper pressure feedback value to the gripping torque range for comparison and adjustment, and generating the real-time assembly / disassembly parameters. Analyze the clamping torque range and extract the maximum and minimum clamping force values; The pressure feedback value of the gripper is compared item by item with the maximum and minimum clamping forces to determine whether the pressure feedback value is within the clamping force range. S1: Calculate the difference between the pressure feedback value and the minimum clamping force to obtain a first difference; S2: Calculate the difference between the pressure feedback value and the maximum clamping force to obtain a second difference; Based on the first difference and the second difference, clamping force analysis is performed to determine the clamping force state, which is then compared and adjusted to generate real-time disassembly and assembly parameters.

6. The six-axis robot control method for bolt and nut assembly / disassembly as described in claim 5, characterized in that, Based on the first difference and the second difference, clamping force analysis is performed to determine the clamping force state for comparison and adjustment, generating real-time disassembly and assembly parameters. The method includes: Based on the first difference, if the clamping force state is that the pressure feedback value is lower than the minimum clamping force value, then a pressure boosting command is generated. The pressure feedback value is gradually increased by the pressure increase command until the pressure feedback value reaches the clamping torque range; Based on the second difference, if the clamping force state is such that the pressure feedback value is higher than the maximum clamping force value, then a decompression command is generated. The pressure feedback value is gradually reduced by the pressure reduction command until the pressure feedback value reaches the clamping torque range. Based on the first difference and the second difference, if the clamping force state is such that the pressure feedback value is within the clamping torque range, then a clamping stabilization signal is sent to the six-axis robot according to the clamping force state.

7. The six-axis robot control method for bolt and nut assembly / disassembly as described in claim 1, characterized in that, Based on the angle feedback value, the torque feedback value is compared with the preset torque value in multiple directions. Based on the comparison results, the six-axis robot is adjusted using a turning simulation to generate turning simulation control parameters. The method includes: Using the angle feedback value as a reference, the torque feedback value and the torque preset value are arranged in a corresponding manner according to the direction of angle increase to determine multiple angle node sequences; The torque feedback value is longitudinally compared with the preset torque value according to the multiple angle node sequence, and the torque deviation value of multiple angle nodes is calculated. Based on the torque deviation values ​​of the multiple angle nodes, a trend analysis is performed according to the sequence of multiple angle nodes, and a trend graph is drawn. By retrieving multiple continuous angle intervals and comparing them with the aforementioned trend chart, the data on torque deviation change rate and torque deviation change direction are identified. The torque deviation values ​​of the multiple angle nodes are integrated with the torque deviation change rate and the torque deviation change direction data to obtain a comparison result, which includes torque deviation distribution information and torque change trend information. The six-axis robot is adjusted by a turning simulation based on the torque deviation distribution information and the torque change trend information, and the turning simulation control parameters are generated.

8. The six-axis robot control method for bolt and nut assembly / disassembly as described in claim 7, characterized in that, Based on the torque deviation distribution information and the torque change trend information, a six-axis robot is adjusted using a turning simulation to generate the turning simulation control parameters. The method includes: Extract the initial torque deviation value based on the torque deviation distribution information, and set the initial twisting speed simulation parameters based on the initial torque deviation value; Based on the torque deviation distribution information, extract the torque deviation value of the characteristic angle node, and set the torque output limit parameters for multiple angle positions based on the torque deviation value of the characteristic angle node. The torque change trend information is traversed to perform feature change analysis, and the location of the feature change interval is determined. Based on the initial twisting speed simulation parameters, simulation analysis is performed to generate multiple candidate twisting speed values; Based on the torque output limit parameters, simulation analysis is performed to generate multiple candidate torque values; Torque ramp calculation is performed based on the location of the characteristic change interval, and multiple ramp rate candidate values ​​are calculated. The multiple candidate values ​​of turning speed, torque, and climbing rate are combined to generate multiple sets of candidate turning simulation control parameters for screening, and the turning simulation control parameters are determined.

9. The six-axis robot control method for bolt and nut assembly / disassembly as described in claim 8, characterized in that, Multiple sets of candidate control parameters for screwing simulation are generated and filtered to determine the screwing simulation control parameters. The method includes: Based on the multiple sets of candidate screwing simulation control parameters, screwing dynamics simulation is performed to obtain multiple sets of simulation parameters, including the target simulation torque value and the target simulation angle value. The target simulated torque value is compared with the preset torque value to calculate the torque approximation degree; The target simulated angle value is compared with the preset angle value to calculate the angle approximation degree; The weighted sum is generated by performing a weighted summation based on the torque approximation and the angle approximation. The multiple candidate screwing simulation control parameters are sorted in descending order of control effect according to the weighted summation result to generate a control effect sequence. The first-order data of the control effect sequence is extracted as the screwing simulation control parameter.

10. A six-axis robot control system for disassembling and assembling bolts and nuts, characterized in that, The system is used to implement the six-axis robot control method for bolt and nut assembly / disassembly as described in any one of claims 1 to 9, and the system comprises: The disassembly and assembly task parsing module is used to receive bolt disassembly and assembly tasks, parse them, and determine the task instruction for the target bolt. The task instruction includes specification parameters and preset torque values. The disassembly and assembly parameter generation module is used to control the gripper of the six-axis robot according to the specifications and parameters. It integrates gripper holding control parameters to control the six-axis robot to perform twisting control and generates real-time disassembly and assembly parameters, which include torque feedback value and angle feedback value. The control parameter generation module is used to perform a multi-directional comparison between the torque feedback value and the torque preset value based on the angle feedback value, and to perform a twisting simulation adjustment on the six-axis robot according to the comparison result, thereby generating twisting simulation control parameters. The control strategy construction module is used to perform backtracking verification on the screwing simulation control parameters, update the screwing simulation control parameters in a closed loop based on the verification results, and construct a bolt and nut disassembly and assembly control strategy.

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