A screw machine positioning mechanism

Abstract

The present application relates to the technical field of screw installation, and discloses a screw machine positioning mechanism, which comprises a rack, a mounting plate two, a motion execution system, a vision system and a moving speed regulation system. The motion execution system drives the mounting plate two to translate along the XYZ axis; the vision system collects positioning images; the moving speed regulation system comprises a mechanical dynamic performance evaluation module, a motion path evaluation module, a vision system confidence evaluation module, a negative pressure state evaluation module and a moving speed regulation module. Through the collaborative work of multiple modules, the mechanical performance coefficient, the motion path coefficient, the vision confidence and the negative pressure matching coefficient are output based on the parameters of vibration acceleration, tracking error, load, path curvature, moving distance, image definition, illumination intensity, real-time negative pressure value and vacuum establishment time, and finally the moving speed is dynamically adjusted in real time through the speed regulation model. The present application realizes the intelligent balance of positioning accuracy and efficiency under high-speed operation.

Description

Technical Field

[0001] This invention belongs to the field of screw installation technology, and particularly relates to a screw machine positioning mechanism. Background Technology

[0002] Screw installation is a fundamental assembly process in industrial production, and its level of automation directly affects production efficiency and product quality. Traditional automatic screw machines mostly employ positioning control methods based on fixed programs or simple position feedback, completing screw fastening through preset coordinates or preliminary visual guidance. However, these methods have significant limitations in actual high-speed and precision operations.

[0003] First, traditional systems lack real-time perception and compensation capabilities for mechanical dynamic performance. Vibrations and tracking errors generated by the actuator during high-speed movement can easily lead to positioning deviations, causing stripping or workpiece damage. Second, fixed speed curves are difficult to adapt to changes in path curvature and travel distance, making it impossible to balance efficiency and accuracy under complex trajectories. Third, vision systems are greatly affected by lighting and image quality; traditional solutions lack a confidence assessment mechanism, continuing to execute the original plan even with poor imaging, resulting in low reliability. Furthermore, negative pressure adsorption systems typically operate independently, and the speed and stability of adsorption force establishment are not linked to motion control, posing a risk of screws falling off during high-speed movement.

[0004] Therefore, existing technologies struggle to achieve a dynamic balance between accuracy and efficiency during high-speed operations, hindering further improvements in equipment performance. Summary of the Invention

[0005] The purpose of this invention is to provide a screw machine positioning mechanism to solve the above-mentioned problems.

[0006] This invention is implemented as follows: a screw machine positioning mechanism includes a frame and a second mounting plate. A screw suction tube is mounted on the second mounting plate and connected to a negative pressure device. The mechanism further includes: a motion execution system mounted on the top of the frame, used to drive the second mounting plate to translate along the X, Y, and Z axes of a Cartesian coordinate system; a vision system mounted on the second mounting plate, used to acquire images of the screw and screw hole positions and perform positioning recognition; and a movement speed control system, communicatively connected to the control terminal of the motion execution system, used to adjust the movement speed of the second mounting plate in real time. The movement speed control system includes: a mechanical dynamic performance evaluation module, based on the end effector vibration acceleration, tracking error, and real-time load, and... The system outputs mechanical performance coefficients through a mechanical performance model; the motion path evaluation module, based on path curvature and the distance between the current point and the next target point, outputs motion path coefficients through a motion path model; the vision system confidence evaluation module, based on image clarity and illumination intensity, outputs visual confidence level through a visual confidence model; the negative pressure state evaluation module, under the current mechanical performance coefficients and motion path coefficients, outputs a negative pressure value-vacuum establishment time matching coefficient through a matching model; and the movement speed adjustment module, based on visual confidence level and the negative pressure value-vacuum establishment time matching coefficient, outputs the target speed through a speed adjustment model and adjusts the current speed to the target speed.

[0007] In a further technical solution, the speed adjustment model is configured such that the target speed is determined by multiplying the maximum safe speed designed for the system by the visual confidence level, the negative pressure matching coefficient, and a dynamic safety coefficient in sequence.

[0008] The dynamic safety coefficient is obtained by weighted fusion of a preset basic safety factor and the smaller of the visual confidence level and the negative pressure matching coefficient.

[0009] A further technical solution involves the step of outputting visual confidence based on image sharpness and illumination intensity using a visual confidence model, as follows:

[0010] The image sharpness index is obtained by comparing the current image sharpness with the ideal image sharpness benchmark value and then using the min function to limit the upper limit to 1.

[0011] The absolute value of the difference between the current light intensity and the optimal working light intensity of the vision system is compared with the optimal working light intensity of the vision system, and the light intensity index is obtained by using the min function with an upper limit of 1.

[0012] The visual confidence score is calculated by combining the image sharpness index and the illumination intensity index; the calculation of the visual confidence score is configured to be positively correlated with the image sharpness index and negatively correlated with the illumination intensity index.

[0013] A further technical solution, wherein under the current mechanical performance coefficient and motion path coefficient, the step of outputting the negative pressure value-vacuum establishment time matching coefficient through a matching model based on the real-time negative pressure value and vacuum establishment time inside the screw tube, is as follows:

[0014] Risk coefficients are obtained through a risk model based on mechanical performance coefficients and motion path coefficients.

[0015] The ratio of the real-time negative pressure value inside the screw tube to the minimum negative pressure value that ensures reliable adsorption is processed, and the upper limit of the min function is limited to 1 to obtain the real-time negative pressure value index.

[0016] The vacuum build-up time is calculated by comparing the current vacuum build-up time with the maximum allowable vacuum build-up time, and the upper limit is set to 1 using the min function to obtain the vacuum build-up time index.

[0017] The negative pressure matching coefficient is calculated by combining the vacuum establishment time index, the real-time negative pressure value index, and the risk coefficient.

[0018] The calculation of the negative pressure matching coefficient is configured to be negatively correlated with the vacuum establishment time exponent, positively correlated with the real-time negative pressure value exponent, and negatively correlated with the risk coefficient.

[0019] A further technical solution involves obtaining the risk coefficient through a risk model based on mechanical performance coefficients and motion path coefficients, as follows:

[0020] The risk coefficient is determined by a weighted linear combination of a complementary value of the mechanical performance coefficient and the motion path coefficient.

[0021] The risk coefficient is negatively correlated with the mechanical performance coefficient and positively correlated with the motion path coefficient.

[0022] A further technical solution involves the step of outputting mechanical performance coefficients based on the end effector vibration acceleration, tracking error, and real-time load through a mechanical performance model, as follows:

[0023] The vibration acceleration of the current end effector is compared with the maximum allowable vibration acceleration of the structure, and the upper limit of the amplitude is limited to 1 using the min function to obtain the vibration acceleration index.

[0024] The current tracking error is compared with the system's maximum allowable tracking error, and the upper limit is limited to 1 using the min function to obtain the tracking error exponent.

[0025] The real-time load is compared with the driver's maximum load capacity, and the load index is obtained by using the min function with a limit of 1.

[0026] The mechanical performance coefficients are determined by weighted linear fusion of the vibration acceleration index, tracking error index, and load index.

[0027] The mechanical performance coefficient is configured to be negatively correlated with the vibration acceleration index, the tracking error index, and the load index.

[0028] A further technical solution involves the step of outputting motion path coefficients based on the path curvature and the distance between the current point and the next target point using a motion path model, as follows:

[0029] The current path curvature is compared with the maximum path curvature that the system can handle, and the upper limit of the amplitude is limited to 1 using the min function to obtain the path curvature exponent.

[0030] The distance index is obtained by comparing the difference between the distance between the current point and the next target point and the minimum effective control distance with the difference between the maximum effective control distance and the maximum effective control distance in the typical workspace, and by using the min function with an upper limit of 1.

[0031] The motion path coefficients are calculated by combining the path curvature index and the distance index.

[0032] The calculation of the motion path coefficient is configured to be positively correlated with the path curvature exponent and negatively correlated with the distance exponent.

[0033] A further technical solution is provided, wherein the motion execution system includes a linear guide rail and a linear module fixed on the top of the frame. The linear guide rail and the linear module are arranged parallel to each other. A mounting plate is fixed on the slider of the linear guide rail. The mounting plate is fixedly connected to the moving end of the linear module. A linear module is fixed on one side wall of the mounting plate and is perpendicular to the length direction of the linear guide rail. A vertical linear module is fixed on the moving end of the linear module. The mounting plate is fixed on the moving end of the linear module.

[0034] In a further technical solution, a vertical linear guide rail and a cylinder are fixed on the mounting plate 2. An electric screwdriver is installed on the slider of the linear guide rail 2. The telescopic end of the cylinder is connected to the slider of the linear guide rail 2. The end of the electric screwdriver extends into the screw suction tube from the upper end of the screw suction tube.

[0035] In a further technical solution, a linear module four and a screw supply device are fixedly mounted on the top of the frame, and a workpiece fixture is fixedly mounted on the slider of the linear module four.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] 1. The mechanical dynamic performance evaluation module monitors vibration acceleration, tracking error and load in real time, and dynamically outputs mechanical performance coefficients, which effectively suppresses mechanical vibration during high-speed motion, reduces the accumulation of tracking error, prevents drive overload, and significantly improves positioning accuracy and system stability.

[0038] 2. The motion path evaluation module calculates motion path coefficients in real time based on path curvature and movement distance, enabling the movement speed to adapt to changes in path complexity. It automatically reduces speed when there are sudden changes in path curvature or short-distance movement, avoiding control instability caused by complex trajectories and optimizing the balance between efficiency and accuracy under the motion trajectory.

[0039] 3. The vision system confidence assessment module comprehensively quantifies the impact of image clarity and illumination intensity on recognition reliability, outputs visual confidence, and automatically triggers a speed reduction mechanism when image quality deteriorates or illumination is abnormal, effectively reducing the risk of misidentification and mislocation, and improving the system's robustness in complex lighting environments.

[0040] 4. The negative pressure status assessment module innovatively links the negative pressure adsorption system with motion control. Based on mechanical performance and path status, it assesses adsorption risk and outputs a matching coefficient by combining real-time negative pressure value and vacuum settling time, ensuring the adsorption reliability of screws during high-speed movement and fundamentally preventing screws from falling off.

[0041] 5. The moving speed adjustment module integrates visual confidence, negative pressure matching coefficient and introduces dynamic safety factor to construct a multi-parameter collaborative speed adjustment model, realizing closed-loop precise control of moving speed, and maximizing the overall efficiency of screw fastening while ensuring safety bottom line. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the structure of a screw machine positioning mechanism provided by the present invention;

[0043] Figure 2 Provided by the present invention Figure 1 A schematic diagram of the structure of the motion execution system;

[0044] Figure 3 Provided by the present invention Figure 1 Schematic diagram of the structure of the middle linear module three;

[0045] Figure 4 A flowchart of the mobile speed control system provided by the present invention.

[0046] In the attached diagram: 1. Frame; 2. Linear guide rail one; 3. Linear module one; 4. Mounting plate one; 5. Linear module two; 6. Linear module three; 7. Mounting plate two; 8. Vision system; 9. Screw suction tube; 10. Linear guide rail two; 11. Cylinder; 12. Electric screwdriver; 13. Linear module four; 14. Workpiece fixture; 15. Screw supply device. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] In existing technologies, screw installation, as a fundamental assembly process in industrial production, directly impacts production efficiency and product quality due to its level of automation. Traditional automatic screw machines often employ fixed programs or simple position feedback-based positioning control methods, completing the screw-on operation through preset coordinates or preliminary visual guidance. However, in high-speed, precision operation scenarios, these methods have significant drawbacks: vibrations and tracking errors generated by the mechanical system during high-speed movement are not detected and compensated for in real time, leading to positioning deviations; fixed speed curves cannot adapt to changes in path curvature and travel distance; the vision system continues to execute according to the original plan even when affected by lighting and image quality, resulting in insufficient reliability; and the lack of linkage between the negative pressure adsorption system and motion control poses a risk of screw detachment.

[0049] To address these issues, the inventors discovered that the core problem with traditional methods lies in the lack of a multi-dimensional dynamic collaborative control mechanism. By analyzing the coupling relationship between mechanical performance, path complexity, visual quality, and adsorption stability, they proposed transforming real-time sensing data into quantifiable control parameters. For example, mechanical vibration and load variations may affect positioning accuracy, necessitating the establishment of a dynamic performance evaluation model; the difference between path curvature and travel distance requires the velocity curve to be adaptive; the reliability of visual recognition results needs to be dynamically adjusted through quantitative indicators; and the stability of negative pressure adsorption needs to be matched with the motion state in real time. Based on this, the inventors constructed a multi-module interconnected closed-loop control system, achieving a balance between efficiency and accuracy through data fusion and dynamic adjustment.

[0050] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0051] like Figure 1 , Figure 3 and Figure 4As shown, a screw machine positioning mechanism according to an embodiment of the present invention includes a frame 1 and a mounting plate 7. A screw suction tube 9 is mounted on the mounting plate 7 and connected to a negative pressure device. The mechanism further includes: a motion execution system mounted on the top of the frame 1, used to drive the mounting plate 7 to translate along the X, Y, and Z axes of a Cartesian coordinate system; a vision system 8 mounted on the mounting plate 7, used to acquire images of the screw and screw hole positions and perform positioning recognition; and a movement speed control system, communicatively connected to the control terminal of the motion execution system, used to adjust the movement speed of the mounting plate 7 in real time. The movement speed control system includes: a mechanical dynamic performance evaluation module, based on the vibration acceleration of the end effector, tracking error... The system employs a multi-module approach: a mechanical performance model that outputs mechanical performance coefficients based on the difference in performance and real-time load; a motion path evaluation module that outputs motion path coefficients based on path curvature and the distance between the current point and the next target point; a vision system confidence evaluation module that outputs visual confidence based on image clarity and illumination intensity; a negative pressure state evaluation module that, under the current mechanical performance coefficients and motion path coefficients, outputs a negative pressure value-establishment time matching coefficient based on the real-time negative pressure value and vacuum establishment time within the screw tube; and a movement speed adjustment module that, based on visual confidence and the negative pressure value-establishment time matching coefficient, outputs the target speed and adjusts the current speed to the target speed using a speed adjustment model.

[0052] The vision system 8 is a common machine vision component in existing technologies, typically including an industrial camera, lens, and light source. It identifies the position of screw holes by acquiring workpiece images and utilizing mature image processing algorithms (such as feature extraction and template matching), which is well-known to those skilled in the art. The improvement of this invention lies not in the construction of the vision system itself or the recognition algorithm, but in the innovative proposal of a motion speed control system based on its output confidence and deeply integrated with other system parameters; therefore, it will not be elaborated upon here. The mechanical dynamic performance evaluation module evaluates mechanical stability through the vibration acceleration, tracking error, and real-time load data of the end effector. Specifically, it can be implemented using accelerometers, encoders, and force sensors to reflect the constraints of the system's dynamic performance on speed control. The motion path evaluation module calculates path complexity based on path curvature and the distance between the current point and the next target point. Specifically, it can be implemented using trajectory planning algorithms and distance sensors, providing path feature parameters for speed adjustment. The vision system confidence evaluation module evaluates recognition reliability through image clarity and illumination intensity. Specifically, it can be implemented using image processing algorithms and light intensity sensors to ensure active speed reduction when the reliability of the visual data is insufficient. The negative pressure state assessment module combines mechanical performance coefficients and motion path coefficients to analyze the matching between real-time negative pressure values ​​and vacuum establishment time. This can be achieved using pressure sensors and timers to ensure the adaptation of adsorption stability and movement speed. The movement speed adjustment module generates a target speed by integrating visual confidence and negative pressure matching coefficients. This can be achieved using a PID controller to form a closed-loop linkage control.

[0053] Specifically, the motion execution system drives the mounting plate two to move in three-dimensional space, while the vision system acquires images of the target position in real time. The mechanical dynamic performance evaluation module collects vibration, tracking error, and load data, and outputs mechanical performance coefficients through weighted calculations to reflect system stability. The motion path evaluation module generates path coefficients based on path curvature and movement distance, quantifying trajectory complexity. The vision system confidence evaluation module analyzes image quality and lighting conditions, outputting a visual confidence index. The negative pressure state evaluation module combines mechanical performance and path state to calculate a matching coefficient between the negative pressure value and vacuum establishment time. The movement speed adjustment module dynamically adjusts the movement speed based on the visual confidence and negative pressure matching coefficient through a speed adjustment model, enabling the system to balance positioning accuracy and operational efficiency during high-speed movement.

[0054] Compared to existing technologies, traditional solutions employ fixed speed curves and independent control modules, failing to achieve dynamic coordination of multi-dimensional parameters. This solution constructs a closed-loop control mechanism through real-time data fusion of mechanical performance, path status, visual quality, and adsorption stability. For example, when the path curvature increases, the motion path evaluation module automatically reduces the speed coefficient, while the mechanical dynamic performance evaluation module monitors vibration data; if the vibration exceeds a threshold, the speed is further limited. When the confidence level of the vision system decreases due to insufficient lighting, the speed adjustment module actively reduces the speed to improve positioning reliability. The negative pressure state evaluation module matches the adsorption force and motion status in real time during high-speed movement, preventing screws from falling off.

[0055] Through the above technical solution, this application solves the positioning deviation problem caused by mechanical vibration under high-speed operation. By dynamically adjusting the speed to adapt to different path complexities, it reduces the risk of misoperation caused by unreliable visual recognition, while ensuring real-time matching between the stability of negative pressure adsorption and the motion state. This achieves a dynamic balance between efficiency and precision in precision assembly scenarios, reducing the occurrence of screw loosening and workpiece damage.

[0056] Preferably, the speed adjustment model is configured such that the target speed is determined by multiplying the maximum safe speed designed for the system by the visual confidence level, the negative pressure matching coefficient, and a dynamic safety factor in sequence;

[0057] The dynamic safety coefficient is obtained by weighted fusion of a preset basic safety factor and the smaller of the visual confidence level and the negative pressure matching coefficient;

[0058] The speed regulation model can be specifically as follows:

[0059]

[0060]

[0061] in, For the target speed, The maximum safe speed for system design represents the highest permissible speed under ideal conditions. Visual confidence is a parameter that assesses the reliability of image recognition by considering image sharpness and illumination intensity. Specifically, it can be achieved by analyzing image data collected by a vision system in real time and calculating a combination of sharpness index and illumination intensity index. This is used to reduce movement speed to avoid mislocalization when visual conditions deteriorate. The negative pressure value-setup time matching coefficient is a parameter that reflects the degree of matching between the real-time state of the negative pressure adsorption system and the motion control. Specifically, it can be achieved through a comprehensive evaluation of the real-time negative pressure value and the vacuum setup time. It is used to automatically adjust the speed to prevent the screw from falling off when the adsorption is unstable. The dynamic safety factor is an additional factor to ensure operational safety; the closer the value is to 1, the safer the operation. It refers to the redundant safety parameter generated by combining the minimum value of the basic safety factor with the visual and negative pressure matching coefficients. Specifically, it can be achieved by preset the basic safety factor and dynamically calculating the weighted value of the minimum matching coefficient, in order to ensure the minimum safety margin and adapt to the safety requirements of different working conditions. The basic safety factor refers to a pre-defined safety level parameter, which can be adjusted through the system configuration interface. It is used to forcibly increase safety redundancy in high-precision or high-risk scenarios to ensure a minimum safety margin. The larger the value, the higher the safety requirement. Greater than or equal to 0 and less than or equal to 1.

[0062] Specifically, the speed adjustment model dynamically corrects the system's maximum safe speed by multiplying the visual confidence level, the negative pressure matching coefficient, and the dynamic safety factor. When the confidence level of the vision system decreases due to insufficient lighting or image blurring, the visual confidence level decreases, directly reducing the target speed to avoid the risk of mispositioning. Simultaneously, if the negative pressure adsorption system experiences prolonged vacuum build-up time or insufficient real-time negative pressure, the negative pressure matching coefficient decreases accordingly, forcing the target speed to decrease to maintain screw adsorption stability. The dynamic safety factor is generated by weighting the minimum values ​​of the basic safety factor and the visual and negative pressure matching coefficients. Even if the visual or negative pressure parameters are temporarily abnormal, a minimum safety margin can still be maintained through the basic safety factor. For example, when the basic safety factor is set to 0.3, the dynamic safety factor is at least 0.3, ensuring that the target speed does not completely drop to zero, causing a shutdown. This model, through the product constraint relationship of multidimensional parameters, enables the speed adjustment process to respond in real-time to the synergistic effects of visual quality, adsorption state, and preset safety level, achieving a dynamic balance between positioning accuracy and operational safety under high-speed movement.

[0063] Compared to existing technologies, traditional screw-making machines, which employ fixed speeds or single-parameter adjustments, cannot simultaneously address the combined risks arising from fluctuations in vision system reliability and changes in negative pressure adsorption. In existing technologies, speed control, adsorption, and vision systems operate independently, making them prone to screw detachment or positioning errors when image recognition deviations or adsorption delays occur. This solution, however, establishes a coupled calculation model of visual confidence and negative pressure matching coefficients, integrating image quality assessment, adsorption performance monitoring, and a dynamic safety redundancy mechanism into speed adjustment. This allows the moving speed to be automatically optimized based on real-time operating conditions, avoiding the limitations of single-parameter adjustments and ensuring the system's minimum safety threshold through preset basic safety factors.

[0064] Through the above technical solution, this application solves the problem of imbalance between positioning accuracy and safety caused by fluctuations in visual reliability and changes in negative pressure during high-speed movement. The introduction of visual confidence effectively reduces the risk of misoperation when image quality deteriorates, and the negative pressure matching coefficient reflects the performance of the adsorption system in real time, preventing screws from falling off due to vacuum establishment delays. The dynamic safety factor, weighted by the minimum value of the basic safety factor and real-time parameters, ensures minimum safety redundancy under extreme conditions while allowing for improved operational efficiency based on real-time evaluation results under normal conditions. This speed adjustment model, through the synergistic effect of multi-dimensional parameters, achieves an optimized balance between positioning accuracy, operational safety, and operational efficiency in high-speed precision operation scenarios.

[0065] Preferably, the step of outputting visual confidence based on image sharpness and illumination intensity using a visual confidence model is as follows:

[0066] The image sharpness index is obtained by comparing the current image sharpness with the ideal image sharpness benchmark value and then using the min function to limit the upper limit to 1.

[0067] The absolute value of the difference between the current light intensity and the optimal working light intensity of the vision system is compared with the optimal working light intensity of the vision system, and the light intensity index is obtained by using the min function with an upper limit of 1.

[0068] The visual confidence score is calculated by combining the image sharpness index and the illumination intensity index; the calculation of the visual confidence score is configured to be positively correlated with the image sharpness index and negatively correlated with the illumination intensity index.

[0069] The visual confidence score can be calculated by importing the image sharpness index and the illumination intensity index into the formula. Obtain visual confidence .

[0070] Among them, visual confidence This indicates the reliability of the image recognition results; the closer the value is to 1, the higher the confidence level. The range of values ​​is... ; Image sharpness index is the ratio of the current image sharpness to the ideal benchmark value. Specifically, the sharpness value can be obtained by using an image edge sharpness detection algorithm, and then the index can be obtained by normalization. It is used to quantify the impact of image quality on recognition accuracy. The light intensity index refers to the degree to which the current light intensity deviates from the optimal working conditions. Specifically, it can be obtained by collecting ambient light data using a photosensitive sensor, calculating the relative deviation and normalizing it, and is used to reflect the interference of abnormal lighting on the visual system.

[0071] Specifically, this scheme collects image sharpness data in real time and compares it with a preset ideal benchmark. When the sharpness is lower than the benchmark, the index decreases proportionally, but is limited to prevent the index from exceeding 1, ensuring that the evaluation result remains within a reasonable range. Simultaneously, by calculating and normalizing the absolute difference between the current light intensity and the optimal working conditions, the index increases when the light is too strong or too weak, reflecting the negative impact of abnormal lighting on visual recognition. Substituting the two indices into the formula, the image sharpness index and the light intensity deviation index are multiplied and averaged. This ensures that visual confidence decreases when either insufficient sharpness or abnormal lighting occurs, triggering a dynamic adjustment mechanism for movement speed. For example, when the light intensity index reaches 0.8, even if the image sharpness index is 1, the calculated visual confidence will drop to 0.1. At this point, the system automatically reduces its movement speed to avoid mispositioning.

[0072] Compared to existing technologies, traditional methods judge image quality solely based on fixed thresholds without establishing a quantitative evaluation model, making them prone to misjudgment when there are sudden changes in illumination or fluctuations in local sharpness. In contrast, this method, through dynamic ratio calculation and normalization, accurately reflects the combined impact of image quality and illumination conditions, and uses a limiting function to avoid evaluation distortion caused by anomalies in a single parameter.

[0073] Through the above technical solution, this application can automatically reduce the adjustment weight of the moving speed when the light intensity exceeds the optimal working range of the vision system or the image clarity is insufficient, thus avoiding positioning errors caused by a decrease in the reliability of visual recognition. Simultaneously, by coupling the influence of image quality and lighting conditions in calculations, it ensures that a protection mechanism can be triggered when either condition is not met, improving the environmental adaptability of the screw positioning process.

[0074] Preferably, the step of outputting the negative pressure value-vacuum establishment time matching coefficient through a matching model based on the real-time negative pressure value and vacuum establishment time within the screw tube under the current mechanical performance coefficient and motion path coefficient is as follows:

[0075] Risk coefficients are obtained through a risk model based on mechanical performance coefficients and motion path coefficients.

[0076] The ratio of the real-time negative pressure value inside the screw tube to the minimum negative pressure value that ensures reliable adsorption is processed, and the upper limit of the min function is limited to 1 to obtain the real-time negative pressure value index.

[0077] The vacuum build-up time is calculated by comparing the current vacuum build-up time with the maximum allowable vacuum build-up time, and the upper limit is set to 1 using the min function to obtain the vacuum build-up time index.

[0078] The negative pressure matching coefficient is calculated by combining the vacuum establishment time index, the real-time negative pressure value index, and the risk coefficient.

[0079] The calculation of the negative pressure matching coefficient is configured to be negatively correlated with the vacuum establishment time exponentially, positively correlated with the real-time negative pressure value exponentially, and negatively correlated with the risk coefficient;

[0080] Specifically, this could involve importing the risk coefficient, real-time negative pressure index, and vacuum establishment time index into the formula. Obtain pressure value - establish time matching coefficient .

[0081] Among them, the pressure value - the matching coefficient of the setup time Range of values Pressure value - setup time matching coefficient The closer the value is to 1, the more reliable the fixation. The vacuum set-up time index refers to the time required from starting the negative pressure device to reaching the set negative pressure threshold. It can be measured by linking a timer and a pressure sensor to reflect the response speed of the negative pressure system. The real-time negative pressure index refers to the instantaneous vacuum pressure value inside the suction screw tube, which can be monitored in real time using a vacuum pressure sensor to characterize the sufficiency of the adsorption force. The risk coefficient is a quantitative value that combines mechanical performance and path complexity. Specifically, it can be calculated by weighted fusion of mechanical performance coefficient and motion path coefficient, and is used to assess the overall risk level of system operation.

[0082] Specifically, during high-speed movement, the mechanical performance coefficient and motion path coefficient are input into the risk model to generate a risk coefficient, which integrates information on mechanical vibration, tracking error, load status, and path complexity. Simultaneously, the real-time negative pressure value is converted into a standardized index by being processed as a ratio to the minimum required value, ensuring that the adsorption force meets basic requirements; the vacuum build-up time is converted into a response speed index by being processed as a ratio to the maximum allowable time. These parameters are then substituted into the matching model formula for calculation. When the vacuum build-up time is too long or the risk coefficient is too high, the matching coefficient will significantly decrease to limit the movement speed; when the negative pressure is sufficient and builds up rapidly, the matching coefficient approaches 1 to allow high-speed operation. This process achieves dynamic coupling between the negative pressure system's response capability and motion control parameters, ensuring reliable screw fixation under complex working conditions.

[0083] Compared to existing technologies, traditional solutions involve an independent negative pressure system that is not linked to motion control, making it impossible to dynamically adjust adsorption parameters based on mechanical vibration and path complexity. This solution, however, establishes a correlation model between the risk coefficient and adsorption parameters, enabling real-time sensing of mechanical state and path characteristics during high-speed motion. This allows for simultaneous optimization of negative pressure response and movement speed, resolving the conflict between adsorption stability and motion efficiency.

[0084] Through the above technical solution, this application can automatically adjust the negative pressure adsorption strength and response speed according to the mechanical vibration amplitude and path curvature during high-speed movement, effectively preventing screws from falling off due to sudden vibration or path change; at the same time, through the dynamic matching of vacuum establishment time and risk factor, it avoids positioning deviation caused by negative pressure response delay, and improves the screw fixing reliability under high-speed operation.

[0085] Preferably, the step of obtaining the risk coefficient through a risk model based on the mechanical performance coefficient and the motion path coefficient is as follows:

[0086] The risk coefficient is determined by a weighted linear combination of a complementary value of the mechanical performance coefficient and the motion path coefficient.

[0087] The risk coefficient is negatively correlated with the mechanical performance coefficient and positively correlated with the motion path coefficient;

[0088] Specifically, this could be: importing the formula Obtain the risk coefficient .

[0089] Among them, the risk coefficient Range of values Risk coefficient This is used to comprehensively assess mechanical performance and path risk; a higher value indicates a higher risk. Among these, The mechanical performance coefficient is a quantitative indicator that reflects the influence of end effector vibration acceleration, tracking error, and real-time load on mechanical stability. Specifically, it can be calculated by weighting the vibration acceleration index, tracking error index, and load index, and is used to characterize the real-time state of the system's mechanical dynamic performance. The motion path coefficient is a quantitative indicator that reflects the impact of path curvature and travel distance on path complexity. Specifically, it can be calculated by combining the path curvature index and the distance index, and is used to characterize the complexity of the motion trajectory. and These are all weighting coefficients, which are adjustment parameters used to adjust the proportion of the contribution of mechanical performance degradation and path complexity to risk. Specifically, they can be preset or adjusted in real time, and are used to balance the weight allocation between mechanical stability and path risk according to the working conditions. ,and and All are greater than or equal to 0 and less than or equal to 1.

[0090] Specifically, by acquiring mechanical performance coefficients and motion path coefficients in real time, both are weighted and calculated with their corresponding weighting coefficients. When the mechanical performance coefficient decreases, it indicates a reduction in system mechanical stability. In this case, increasing the weighting coefficient of mechanical performance can strengthen its impact on risk. When the motion path coefficient increases, it indicates an increase in path complexity. In this case, adjusting the weighting coefficient of path risk can increase the proportion of path factors in risk assessment. The resulting risk coefficient dynamically reflects the combined risk level of mechanical performance and path complexity under the current operating conditions, providing dynamic safety parameter input for the subsequent speed adjustment module. For example, if increased vibration or overload is detected during high-speed movement, the mechanical performance coefficient will decrease, and the risk coefficient will increase accordingly to trigger a speed reduction operation. When the path curvature suddenly increases, the motion path coefficient increases, and the risk coefficient increases simultaneously to reduce the movement speed and ensure path tracking accuracy.

[0091] Compared to existing technologies, traditional solutions typically rely solely on fixed thresholds to assess mechanical condition or path risk, failing to achieve dynamic risk assessment involving multiple factors. This solution, however, constructs a weighted model of mechanical performance and path risk, enabling real-time quantification of their combined impact on system stability and dynamic adjustment of weight allocation based on operational requirements. For instance, existing technologies may rely solely on path curvature as a single parameter for speed limiting when dealing with high-curvature paths, while this solution further incorporates dynamic parameters such as mechanical vibration and load status to achieve more accurate risk prediction.

[0092] Through the above technical solution, this application effectively solves the problem of comprehensive evaluation of mechanical dynamic performance and motion path risk, and realizes the generation of dynamic safety control parameters. During high-speed movement, when mechanical performance deteriorates or path complexity increases, the risk coefficient can promptly reflect the comprehensive risk level, triggering the speed adjustment module to reduce the movement speed and avoid screw fastening failure caused by excessive mechanical vibration or path tracking deviation. Simultaneously, the adjustability of the weighting coefficients allows the system to adapt to different working conditions. For example, in precision assembly scenarios, the mechanical performance weight can be increased to prioritize stability, while in complex path scenarios, the path risk weight can be enhanced to optimize trajectory tracking accuracy.

[0093] Preferably, the step of outputting mechanical performance coefficients based on the end effector vibration acceleration, tracking error, and real-time load through a mechanical performance model is as follows:

[0094] The vibration acceleration of the current end effector (i.e., the vibration acceleration of mounting plate 27) is compared with the maximum allowable vibration acceleration of the structure, and the upper limit of the amplitude is limited to 1 using the min function to obtain the vibration acceleration index.

[0095] The current tracking error is compared with the system's maximum allowable tracking error, and the upper limit is limited to 1 using the min function to obtain the tracking error exponent.

[0096] The real-time load is compared with the driver's maximum load capacity, and the load index is obtained by using the min function with a limit of 1.

[0097] The mechanical performance coefficients are determined by weighted linear fusion of the vibration acceleration index, tracking error index, and load index.

[0098] The calculation of the mechanical performance coefficient is configured to be negatively correlated with the vibration acceleration index, the tracking error index, and the load index.

[0099] Weighted linear fusion can be achieved by importing the vibration acceleration exponent, tracking error exponent, and load exponent into the formula. Obtain mechanical performance coefficients .

[0100] Among them, mechanical performance coefficient Range of values Mechanical performance coefficient Used to reflect the stability of the system's mechanical state; the closer the value is to 1, the better the performance. The vibration acceleration index is calculated by measuring the vibration acceleration of the end effector using an accelerometer and comparing it with the preset maximum allowable vibration acceleration of the structure. Specifically, it can be achieved by normalization combined with an amplitude limiting function, and is used to quantify the impact of vibration on mechanical stability. The tracking error index is calculated by comparing the deviation between the actual position and the commanded position obtained by the encoder with the maximum tracking error allowed by the system. Specifically, it can be achieved by comparing real-time position feedback data with a preset threshold. It is used to evaluate the accuracy of motion control. The tracking error reflects the deviation between the actual position and the commanded position. The larger the value, the worse the control accuracy. The load index is calculated by comparing the real-time load measured by a force sensor on the drive with the drive's rated maximum load capacity. This can be achieved through dynamic load monitoring combined with proportional conversion. It characterizes the load state of the drive system; the real-time load reflects the load force on the drive system, with a higher value indicating a heavier load. , and These are all weighting coefficients, which refer to the different weighting ratios assigned to vibration acceleration, tracking error, and load index according to the working conditions. Specifically, they can be set using empirical values ​​or adjusted using adaptive algorithms, and are used to balance the contribution of each parameter to the mechanical performance coefficient. ,and , and All are greater than or equal to 0 and less than or equal to 1.

[0101] Specifically, by collecting vibration acceleration, tracking error, and load data in real time, these data are normalized and limited with corresponding thresholds to eliminate the interference of outlier values ​​on the evaluation results. The three indices are input into a weighted calculation formula and linearly combined according to preset or dynamically adjusted weighting coefficients to generate a performance coefficient that comprehensively reflects the stability of the mechanical system. This coefficient is input as an upstream parameter into the risk model, and the moving speed is dynamically adjusted through a closed-loop control mechanism. When vibration intensifies, errors increase, or the load is overloaded, the moving speed is actively reduced to maintain system stability, while operating efficiency is improved when the mechanical condition is good.

[0102] Compared to existing technologies, traditional solutions only use fixed thresholds for single-parameter alarms, failing to achieve integrated evaluation of multi-dimensional dynamic parameters. This solution, through real-time comprehensive calculation of vibration, error, and load, combined with configurable weighting coefficients, can more comprehensively characterize the trend of mechanical performance changes. Existing technologies lack quantitative feedback on mechanical state, preventing the formation of closed-loop control. This solution embeds performance coefficients into the speed regulation logic, establishing a direct link between mechanical state and motion control, achieving dynamic compensation.

[0103] Through the above technical solution, this application solves the positioning deviation problem caused by the degradation of mechanical performance in traditional screw machines. By monitoring vibration, error and load parameters in real time, the mechanical stability is dynamically evaluated and fed back to the control system. The speed is automatically adjusted during high-speed movement to suppress vibration amplitude, reduce the accumulation of tracking error and prevent drive overload, thereby improving positioning accuracy and system reliability.

[0104] Preferably, the step of outputting motion path coefficients based on the path curvature and the distance between the current point and the next target point using the motion path model is as follows:

[0105] The current path curvature is compared with the maximum path curvature that the system can handle, and the upper limit of the amplitude is limited to 1 using the min function to obtain the path curvature exponent.

[0106] The distance index is obtained by comparing the difference between the distance between the current point and the next target point and the minimum effective control distance with the difference between the maximum effective control distance and the maximum effective control distance in the typical workspace, and by using the min function with an upper limit of 1.

[0107] The motion path coefficients are calculated by combining the path curvature index and the distance index.

[0108] The calculation of the motion path coefficient is configured to be positively correlated with the path curvature exponent and negatively correlated with the distance exponent;

[0109] Specifically, this can be achieved by importing the path curvature index and the path curvature index into the formula. Obtain motion path coefficients .

[0110] Among them, motion path coefficient Range of values Motion path coefficient Used to evaluate path complexity; the closer the value is to 1, the more complex the path. The path curvature index is a value obtained by normalizing the current path curvature and the maximum allowable curvature of the system. Specifically, it can be achieved through ratio calculation and amplitude limiting. This index reflects the degree of challenge that path curvature poses to mechanical performance. Path curvature refers to the rate of change of the direction of motion. Specifically, the path curvature can be calculated in real time using curvature sensors or trajectory planning algorithms. The larger the path curvature, the higher the degree of path curvature, which directly affects the dynamic stability of the mechanical system. The distance index is a value obtained by standardizing the current movement distance with the typical workspace range. Specifically, it can be calculated using the difference ratio method. This index characterizes the potential impact of movement distance on control stability. The distance between the current point and the next target point refers to the straight-line distance that the actuator needs to move in the spatial coordinate system. Specifically, it can be measured in real time by an encoder or laser ranging device. When the distance is too short or close to the limit range, it is easy to cause a decrease in control accuracy.

[0111] Specifically, a path curvature index is generated by measuring the path curvature in real time and comparing it with the maximum allowable curvature of the system. For example, when the path curvature reaches 80% of the maximum allowable value, the index is limited to 0.8. Simultaneously, the relative difference between the current travel distance and the minimum effective control distance is calculated and standardized against the workspace range. For instance, when the travel distance approaches the minimum effective control distance, the distance index approaches 1. The path curvature index and the distance index are then fused into a motion path coefficient using a formula, where the path curvature index positively influences the coefficient value, and the distance index adjusts the coefficient through an inverse relationship. When the path is curved and the travel distance is within a critical range, the motion path coefficient approaches 1, triggering the speed adjustment module to reduce the travel speed, thereby avoiding vibration or tracking errors caused by path complexity.

[0112] Compared to existing technologies, traditional solutions lack a dynamic evaluation mechanism for path complexity, relying solely on fixed speed curves or simple distance feedback control. This fails to proactively adjust speed when path curvature changes abruptly or when the travel distance becomes critical. This new solution quantifies the coupled influence of path curvature and travel distance, constructing a motion path coefficient as a key parameter for speed adjustment, thus resolving the dynamic matching problem between path complexity and speed control.

[0113] Through the above technical solution, this application realizes real-time quantitative evaluation of path complexity, and can dynamically adjust the speed according to the curvature of the path and the distance of movement during high-speed movement, avoiding mechanical vibration or positioning deviation caused by sudden changes in the path, while ensuring control accuracy under short-distance movement or extreme paths, effectively balancing the efficiency and stability of automated screw fastening operations.

[0114] like Figures 1-3 As shown, in a preferred embodiment of the present invention, the motion execution system includes a linear guide rail 2 and a linear module 3 fixedly mounted on the top of the frame 1. The linear guide rail 2 and the linear module 3 are arranged parallel to each other. A mounting plate 4 is fixedly mounted on the slider of the linear guide rail 2. The mounting plate 4 is fixedly connected to the moving end of the linear module 3. A second linear module 5 perpendicular to the length direction of the linear guide rail 2 is fixed on the side wall of the mounting plate 4. A third vertical linear module 6 is fixedly mounted on the moving end of the second linear module 5. The mounting plate 7 is fixed on the moving end of the third linear module 6.

[0115] In this embodiment of the invention, X-axis movement is achieved through the parallel arrangement of linear guide rail 2 and linear module 3. Linear guide rail 2 and linear module 3 restrict the degrees of freedom of mounting plate 4, and linear module 3 provides driving force to ensure no lateral deviation during movement. Mounting plate 4 serves as a lateral support platform, and linear module 5 mounted on its sidewall extends in an orthogonal direction to form a Y-axis motion mechanism, avoiding spatial interference with the X-axis assembly. Linear module 3 6 is vertically fixed to the moving end of linear module 2 5, directly driving mounting plate 2 7 to complete the Z-axis lifting action. The three sets of linear modules form a spatial rectangular coordinate system through orthogonal arrangement. The movement of each axis is controlled by an independent drive unit. The movement speed control system controls the three sets of linear modules in real time, thereby adjusting the positioning speed in real time. The rigid connection of mounting plate 4 ensures the coordination of the three-axis movement. The position of screw suction tube 9 in three-dimensional space is accurately positioned through the superposition of displacements in the three axes.

[0116] like Figures 1-3 As shown, in a preferred embodiment of the present invention, a vertical linear guide rail 10 and a cylinder 11 are fixed on the mounting plate 2 7. An electric screwdriver 12 is installed on the slider of the linear guide rail 2 10. The telescopic end of the cylinder 11 is connected to the slider of the linear guide rail 2 10. The end of the electric screwdriver 12 extends into the screw suction tube 9 from the upper end of the screw suction tube 9.

[0117] In this embodiment of the invention, the extension of the end of the electric screwdriver 12 into the screw-collecting tube 9 means that the screwdriver tip is embedded inside the suction tube, which can be specifically designed using a coaxial nested structure. The rigid connection between the linear guide rail 10 and the cylinder 11 forms a vertical motion mechanism. The guiding effect of the guide rail restricts the degree of freedom of the electric screwdriver 12 in the Z-axis direction, and the extension and retraction stroke of the cylinder 11 controls the lifting height of the electric screwdriver 12. When the cylinder 11 pushes the slider down along the guide rail, the end of the electric screwdriver 12 moves downward inside the screw-collecting tube 9, so that the screwdriver tip directly contacts the top of the suction screw. During the screw-collecting stage, the electric screwdriver 12 remains retracted to avoid interference, and the electric screwdriver 12 blocks the upper end of the screw-collecting tube 9; during the fastening stage, the cylinder 11 drives the electric screwdriver 12 to press down, and the screwdriver tip passes through the screw-collecting tube 9 to apply rotational torque to the screw. The coordinated action of the guide rail and the cylinder controls the positional error of the electric screwdriver in the vertical direction within the repeatability accuracy range of the guide rail.

[0118] like Figures 1-3 As shown, in a preferred embodiment of the present invention, a linear module 13 and a screw supply device 15 are fixedly provided on the top of the frame 1, and a workpiece fixture 14 is fixedly provided on the slider of the linear module 13.

[0119] In this embodiment of the invention, the independent drive characteristic of the linear module 4 13 decouples it from the motion execution system, avoiding multi-axis motion interference, while improving equipment scalability through modular layout. The workpiece fixture 14 is fixed on the slider of the linear module 4 13 and can move precisely in a specific direction, facilitating workpiece loading and unloading. The parallel arrangement of the screw supply device and the linear module 4 13 shortens the screw conveying path, allowing the screw suction tube 9 to directly fasten the workpiece after picking up the screw, without frequent adjustments to the motion path. The precise control capability of the linear module 4 13 ensures that the workpiece remains stable during movement, reducing positioning errors caused by vibration or load changes.

[0120] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A screw machine positioning mechanism, comprising a frame and a second mounting plate, wherein a screw suction tube is mounted on the second mounting plate, and the screw suction tube is connected to a negative pressure device, characterized in that... Also includes: The motion execution system, installed on the top of the frame, is used to drive the mounting plate two to translate along the X, Y and Z axes of the spatial rectangular coordinate system; The vision system, mounted on mounting plate two, is used to acquire images of the screw and screw hole positions and perform positioning recognition. A movement speed control system, which communicates with the control terminal of the motion execution system, is used to adjust the movement speed of mounting plate two in real time. The movement speed control system includes: The mechanical dynamic performance evaluation module outputs mechanical performance coefficients based on the end effector vibration acceleration, tracking error, and real-time load through a mechanical performance model. The motion path evaluation module outputs motion path coefficients based on the path curvature and the distance between the current point and the next target point through the motion path model. The visual system confidence assessment module outputs visual confidence scores based on image sharpness and illumination intensity using a visual confidence model. The negative pressure state assessment module, under the current mechanical performance coefficient and motion path coefficient, outputs the negative pressure value-vacuum establishment time matching coefficient based on the real-time negative pressure value and vacuum establishment time in the screw tube through a matching model. The movement speed adjustment module establishes a time matching coefficient based on visual confidence and negative pressure value, outputs the target speed through the speed adjustment model, and adjusts the current speed to the target speed.

2. The screw machine positioning mechanism according to claim 1, characterized in that, The speed adjustment model is configured such that the target speed is determined by multiplying the maximum safe speed designed by the system by the visual confidence level, the negative pressure matching coefficient, and a dynamic safety factor in sequence. The dynamic safety coefficient is obtained by weighted fusion of a preset basic safety factor and the smaller of the visual confidence level and the negative pressure matching coefficient.

3. The screw machine positioning mechanism according to claim 2, characterized in that, The step of outputting visual confidence based on image sharpness and illumination intensity using a visual confidence model is as follows: The image sharpness index is obtained by comparing the current image sharpness with the ideal image sharpness benchmark value and then using the min function to limit the upper limit to 1. The absolute value of the difference between the current light intensity and the optimal working light intensity of the vision system is compared with the optimal working light intensity of the vision system, and the light intensity index is obtained by using the min function with an upper limit of 1. The visual confidence score is calculated by combining the image sharpness index and the illumination intensity index; the calculation of the visual confidence score is configured to be positively correlated with the image sharpness index and negatively correlated with the illumination intensity index.

4. The screw machine positioning mechanism according to claim 2, characterized in that, The steps for outputting the negative pressure value-vacuum establishment time matching coefficient through a matching model, based on the real-time negative pressure value and vacuum establishment time within the screw tube under the current mechanical performance coefficient and motion path coefficient, are as follows: Risk coefficients are obtained through a risk model based on mechanical performance coefficients and motion path coefficients. The ratio of the real-time negative pressure value inside the screw tube to the minimum negative pressure value that ensures reliable adsorption is processed, and the upper limit of the min function is limited to 1 to obtain the real-time negative pressure value index. The vacuum build-up time is calculated by comparing the current vacuum build-up time with the maximum allowable vacuum build-up time, and the upper limit is set to 1 using the min function to obtain the vacuum build-up time index. The negative pressure matching coefficient is calculated by combining the vacuum establishment time index, the real-time negative pressure value index, and the risk coefficient. The calculation of the negative pressure matching coefficient is configured to be negatively correlated with the vacuum establishment time exponent, positively correlated with the real-time negative pressure value exponent, and negatively correlated with the risk coefficient.

5. The screw machine positioning mechanism according to claim 4, characterized in that, The steps for obtaining the risk coefficient based on the mechanical performance coefficient and the motion path coefficient through the risk model are as follows: The risk coefficient is determined by a weighted linear combination of a complementary value of the mechanical performance coefficient and the motion path coefficient. The risk coefficient is negatively correlated with the mechanical performance coefficient and positively correlated with the motion path coefficient.

6. The screw machine positioning mechanism according to claim 5, characterized in that, The step of outputting mechanical performance coefficients based on the end effector vibration acceleration, tracking error, and real-time load through a mechanical performance model is as follows: The vibration acceleration of the current end effector is compared with the maximum allowable vibration acceleration of the structure, and the upper limit of the amplitude is limited to 1 using the min function to obtain the vibration acceleration index. The current tracking error is compared with the system's maximum allowable tracking error, and the upper limit is limited to 1 using the min function to obtain the tracking error exponent. The real-time load is compared with the driver's maximum load capacity, and the load index is obtained by using the min function with a limit of 1. The mechanical performance coefficients are determined by weighted linear fusion of the vibration acceleration index, tracking error index, and load index. The mechanical performance coefficient is configured to be negatively correlated with the vibration acceleration index, the tracking error index, and the load index.

7. The screw machine positioning mechanism according to claim 5, characterized in that, The step of outputting motion path coefficients based on path curvature and the distance between the current point and the next target point using the motion path model is as follows: The current path curvature is compared with the maximum path curvature that the system can handle, and the upper limit of the amplitude is limited to 1 using the min function to obtain the path curvature exponent. The distance index is obtained by comparing the difference between the distance between the current point and the next target point and the minimum effective control distance with the difference between the maximum effective control distance and the maximum effective control distance in the typical workspace, and by using the min function with an upper limit of 1. The motion path coefficients are calculated by combining the path curvature index and the distance index. The calculation of the motion path coefficient is configured to be positively correlated with the path curvature exponent and negatively correlated with the distance exponent.

8. The screw machine positioning mechanism according to claim 1, characterized in that, The motion execution system includes a linear guide rail and a linear module fixed to the top of the frame. The linear guide rail and the linear module are arranged parallel to each other. A mounting plate is fixed on the slider of the linear guide rail. The mounting plate is fixedly connected to the moving end of the linear module. A linear module is fixed on one side wall of the mounting plate, perpendicular to the length direction of the linear guide rail. A vertical linear module is fixed on the moving end of the linear module. The mounting plate is fixed on the moving end of the linear module.

9. The screw machine positioning mechanism according to claim 1 or 8, characterized in that, The mounting plate 2 is fixed with a vertical linear guide rail 2 and a cylinder. An electric screwdriver is installed on the slider of the linear guide rail 2. The telescopic end of the cylinder is connected to the slider of the linear guide rail 2. The end of the electric screwdriver extends into the screw suction tube from the upper end of the screw suction tube.

10. The screw machine positioning mechanism according to claim 1, characterized in that, The top of the frame is fixedly equipped with a linear module four and a screw supply device, and a workpiece fixture is fixedly equipped on the slider of the linear module four.

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