A speed control system and control method for a robotic arm

Abstract

The present invention relates to the technical field of robotic arm control, and discloses a speed control system and a control method for a robotic arm. The method includes: collecting the operating speed matching the mass of the object grasped by the robotic arm to obtain the comprehensive load of the robotic arm; collecting and screening historical operating data to obtain the standard load range; comparing the comprehensive load with the standard load range to determine whether to adjust the PID gain of the robotic arm; when adjusting, determining the PID gain adjustment scheme according to the fuzzy processing result; comparing the collected feature vector with the historical feature vectors in the historical operating data to determine the adjustment coefficient to adjust the PID gain coefficient and obtain the adjusted operating speed; collecting the final position of the grasped object and comparing it with the expected position to determine whether to give an early warning. This application improves the automatic feeding and assembly accuracy, enhances the production efficiency, and reduces the equipment failure rate and the defective product rate.

Description

Technical Field

[0001] The present invention relates to the technical field of robotic arm control, and more particularly, to a speed control system and method for a robotic arm. Background Art

[0002] The working process of an automatic copper pillar press usually includes steps such as automatic copper pillar feeding, automatic lens holder feeding, automatic pressing, and automatic discharging. This equipment firmly connects the copper pillar to the specified fitting by applying pressure. Due to its high level of automation, the automatic copper pillar press can achieve fast, accurate, and highly repeatable pressing operations, thus significantly improving production efficiency and connection quality.

[0003] Despite the high degree of automation, the automatic copper pillar press still faces some challenges in practical applications. Especially during the automatic feeding and assembly process of the lens holder, factors such as the mass of the clamped object and the load of the robotic arm itself during the task execution have an important impact on the movement. For example, when the mass of the clamped object is large, the load of the robotic arm increases, resulting in a larger movement inertia. If the original speed is maintained, it will cause the robotic arm to vibrate and move unevenly, affecting the accuracy of automatic feeding and assembly.

[0004] Therefore, it is necessary to design a speed control system and method for a robotic arm to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a speed control system and method for a robotic arm, aiming to solve the problem that the running speed cannot be adjusted according to real-time load feedback in the current robotic arm speed control technology.

[0006] On the one hand, the present invention proposes a speed control method for a robotic arm, including:

[0007] Collect the mass of the clamped object of the robotic arm, match the running speed according to the mass of the clamped object, and collect the static load and dynamic load of the robotic arm to obtain the comprehensive load of the robotic arm;

[0008] Collect the historical operation data of the robotic arm, screen the data with the same mass of the clamped object and without PID gain adjustment to obtain the standard load range;

[0009] Compare the comprehensive load with the standard load range, and judge whether to perform PID gain adjustment on the robotic arm according to the comparison result;

[0010] When it is determined to perform PID gain adjustment on the robotic arm, use the fuzzy algorithm to obtain the fuzzy processing result, and determine the PID gain adjustment scheme according to the fuzzy processing result;

[0011] After determining the PID gain adjustment scheme, using the mass of the clamped object and the comprehensive load as feature vectors, comparing the feature vectors with the historical feature vectors in the historical operation data, determining the adjustment coefficient to adjust the PID gain coefficient, and obtaining the adjusted operating speed;

[0012] Operating the robotic arm at the adjusted operating speed, collecting the final position of the clamped object, comparing the final position with the desired position, and judging whether to give an early warning according to the comparison result.

[0013] Further, when collecting the static load and dynamic load of the robotic arm to obtain the comprehensive load of the robotic arm, it includes:

[0014] Installing force sensors at the joints of the robotic arm, collecting the force data of each force sensor, and obtaining the static load according to the force data;

[0015] Installing an acceleration sensor at the end effector of the robotic arm, collecting the acceleration data of the acceleration sensor, and obtaining the dynamic load according to the acceleration data;

[0016] Obtaining the comprehensive load according to the static load and dynamic load, and the comprehensive load is calculated by the following formula:

[0017] ;

[0018] where L represents the comprehensive load, L1 represents the static load, L2 represents the dynamic load, a1 and a2 represent weight coefficients, and a1 + a2 = 1.

[0019] Further, when obtaining the standard load range, it includes:

[0020] Obtaining the standard load data corresponding to the mass of the clamped object;

[0021] Collecting data with the same mass of the historical clamped object as the clamped object and without PID gain adjustment, and extracting the corresponding non-abnormal historical load range from the data;

[0022] Comparing the non-abnormal historical load range with the standard load data, and generating a standard load range according to the numerical size relationship, where the standard load range includes a left boundary value and a right boundary value.

[0023] Further, when judging whether to adjust the PID gain of the robotic arm according to the comparison result, it includes:

[0024] When the comprehensive load is greater than the right boundary of the standard load range or less than the left boundary of the standard load range, it is determined to adjust the PID gain of the robotic arm;

[0025] When the comprehensive load is greater than or equal to the left boundary of the standard load range and less than or equal to the right boundary of the standard load range, it is determined not to adjust the PID gain of the robotic arm.

[0026] Further, when obtaining the fuzzy processing result by using the fuzzy algorithm and determining the PID gain adjustment scheme according to the fuzzy processing result, it includes:

[0027] When the comprehensive load is greater than the right boundary of the standard load range, obtain the first load difference, and reduce the proportional gain of the PID according to the first load difference, where the first load difference is the difference between the comprehensive load and the right boundary;

[0028] When the comprehensive load is less than the left boundary of the standard load range, obtain the second load difference, and increase the proportional gain of the PID according to the second load difference, where the second load difference is the difference between the left boundary and the comprehensive load.

[0029] Further, when comparing the feature vector with the historical feature vectors in the historical operation data, it includes:

[0030] Perform clustering analysis on the historical feature vectors and the feature vector to obtain a clustering result;

[0031] When the cluster where the feature vector is located contains historical feature vectors, adjust the PID gain coefficient according to the historical adjustment coefficient corresponding to the historical feature vector;

[0032] When the cluster where the feature vector is located does not contain historical feature vectors, determine the adjustment coefficient according to the first load difference or the second load difference to adjust the PID gain coefficient.

[0033] Further, when adjusting the PID gain coefficient according to the historical adjustment coefficient corresponding to the historical feature vector, it includes:

[0034] ;

[0035] Where, T represents the adjustment coefficient, Ti represents the historical adjustment coefficient corresponding to the i-th historical feature vector in the cluster where the feature vector is located, T0 represents the average value of the historical adjustment coefficients corresponding to the historical feature vectors in the cluster where the feature vector is located, and N represents the number of historical feature vectors in the cluster where the feature vector is located.

[0036] Further, when adjusting the PID gain coefficient according to the first load difference or the second load difference, it includes:

[0037] When the comprehensive load is greater than the right boundary of the standard load range, adjust the proportional gain of the PID according to the first load difference, and the adjustment coefficient is calculated by the following formula:

[0038] ;

[0039] T represents the adjustment coefficient, represents the first load difference;

[0040] When the comprehensive load is less than the left boundary of the standard load range, adjust the proportional gain of the PID according to the second load difference, and the adjustment coefficient is calculated by the following formula:

[0041] ;

[0042] wherein, T represents the adjustment coefficient, represents the second load difference.

[0043] Further, when comparing the final position with the desired position and determining whether to give an alarm according to the comparison result, it includes:

[0044] Obtain the position deviation value according to the final position and the desired position, compare the position deviation value with the deviation threshold, and determine whether to give an alarm according to the comparison result;

[0045] When the position deviation value is greater than the deviation threshold, it is determined to give an alarm, and the alarm level is directly proportional to the position deviation value;

[0046] When the position deviation value is less than or equal to the deviation threshold, it is determined not to give an alarm.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows: By collecting the mass of the object clamped by the robotic arm, the static load and the dynamic load, calculating the comprehensive load in real time and comparing it with the standard load range in the historical operation data, it is determined whether the PID gain needs to be adjusted, so as to dynamically adjust the running speed of the robotic arm according to the load change. The PID gain adjustment is optimized through a fuzzy algorithm to ensure that the robotic arm can automatically adjust the movement speed in the face of different load conditions, improving the accuracy and stability of the automatic crimping process, and reducing the vibration and uneven movement problems caused by load changes. By comparing the historical data with the real-time load feature vectors, the optimal PID gain can be selected according to different load conditions, improving the automatic feeding and assembly accuracy, enhancing the production efficiency, and reducing the equipment failure rate and the defective product rate. This solution improves the adaptability and intelligence level of the robotic arm and enhances the performance of the automatic copper column press.

[0048] On the other hand, the present application also provides a speed control system for a robotic arm for applying the above-mentioned speed control method for a robotic arm, including:

[0049] A force sensor, an acceleration sensor and a control device, the control device is connected to the force sensor and the acceleration sensor, and the control device includes a collection unit, a judgment unit, an adjustment unit and a warning unit;

[0050] Among them, the collection unit is configured to collect the mass of the object clamped by the robotic arm, match the running speed according to the mass of the object clamped, and collect the static load and the dynamic load of the robotic arm to obtain the comprehensive load of the robotic arm; collect the historical operation data of the robotic arm, and screen the data with the same mass of the object clamped and without PID gain adjustment to obtain the standard load range;

[0051] The judgment unit is configured to compare the comprehensive load with the standard load range, and judge whether to adjust the PID gain of the robotic arm according to the comparison result; when it is determined to adjust the PID gain of the robotic arm, a fuzzy algorithm is used to obtain a fuzzy processing result, and a PID gain adjustment scheme is determined according to the fuzzy processing result;

[0052] The adjustment unit is configured to, after determining the PID gain adjustment scheme, use the mass of the object clamped and the comprehensive load as feature vectors, compare the feature vectors with the historical feature vectors in the historical operation data, determine an adjustment coefficient to adjust the PID gain coefficient, and obtain an adjusted running speed;

[0053] The warning unit is configured to run the robotic arm at the adjusted running speed, collect the final position of the object clamped, compare the final position with the expected position, and judge whether to give a warning according to the comparison result.

[0054] It is understandable that the above speed control system and control method for the robotic arm have the same beneficial effects, which will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered as limiting the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0056] Figure 1 is a flowchart of the speed control method for the robotic arm provided by an embodiment of the present invention;

[0057] Figure 2 is a block diagram of the structure of the speed control system for the robotic arm provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in conjunction with the embodiments.

[0059] In some embodiments of the present application, referring to Figure 1 as shown, a speed control method for a robotic arm includes:

[0060] S100: Collect the mass of the object grasped by the robotic arm, match the operating speed according to the mass of the object grasped, and collect the static load and dynamic load of the robotic arm to obtain the comprehensive load of the robotic arm.

[0061] S200: Collect the historical operation data of the robotic arm, screen the data with the same mass of the object grasped and without PID gain adjustment to obtain the standard load range.

[0062] S300: Compare the comprehensive load with the standard load range, and judge whether to adjust the PID gain of the robotic arm according to the comparison result.

[0063] S400: When it is determined to adjust the PID gain of the robotic arm, use the fuzzy algorithm to obtain the fuzzy processing result, and determine the PID gain adjustment scheme according to the fuzzy processing result.

[0064] S500: After determining the PID gain adjustment scheme, use the mass of the clamped object and the combined load as feature vectors, compare the feature vectors with the historical feature vectors in the historical operation data, determine the adjustment coefficient, adjust the PID gain coefficient, and obtain the adjusted operating speed.

[0065] S600: Operate the robotic arm at the adjusted operating speed, collect the final position of the clamped object, compare the final position with the desired position, and determine whether to give an early warning based on the comparison result.

[0066] Specifically, in S100, the mass of the object clamped by the robotic arm is collected through a sensor. Based on the mass of the clamped object, an appropriate operating speed is initially matched. When the robotic arm performs a task, the mass of the clamped object directly affects the required force application and motion inertia. Collect the static load and dynamic load information of the robotic arm. The static load refers to the load applied to the robotic arm when it is in a stationary state, and the dynamic load takes into account the inertia and external forces generated during the movement of the robotic arm. Through the load data, the combined load is obtained. In S200, collect the historical operation data of the robotic arm, especially the records with the same mass of the clamped object and without PID gain adjustment. Based on the historical data, establish a standard load range, that is, the normal working range of the robotic arm under similar load conditions. In S300, after obtaining the combined load and the standard load range, compare the current combined load with the standard load range. If the current combined load exceeds the standard load range, indicating that the load is over-standard or reduced, determine whether to adjust the PID gain according to the comparison result. The goal of PID gain adjustment is to ensure that the robotic arm can maintain stable motion control under different load conditions. In S400, when it is determined that the PID gain needs to be adjusted, a fuzzy algorithm is used to obtain the fuzzy processing result. By inputting the load difference, a fuzzy processing result is obtained, which further provides a scheme for PID gain adjustment. In S500, after determining the PID gain adjustment scheme, use the mass of the clamped object and the combined load information as feature vectors, and compare them with the feature vectors in the historical operation data. Through this comparison, the adjustment coefficient is determined, and the PID gain is fine-tuned according to this coefficient, so as to obtain an optimal operating speed. Through the data-driven comparison of feature vectors, the operating speed can be adaptively adjusted according to different load conditions. In S600, the robotic arm performs the task at the adjusted operating speed, and the final position of the clamped object is collected through a sensor. Compare this position with the desired position. If the actual position of the clamped object is significantly different from the desired position, give an early warning according to the deviation result to remind the operator or the system to make further adjustments to ensure the final assembly accuracy.

[0067] It can be understood that by integrating load monitoring, historical data comparison, fuzzy control, and PID gain dynamic adjustment technologies, adaptive control of the robotic arm under different load conditions is achieved. By real-time collecting the mass of the grasped object and load information, and with the support of historical data and fuzzy algorithms, it is judged whether PID gain needs to be adjusted, and the operating speed of the robotic arm is optimized. Compared with the traditional fixed PID gain adjustment method, it effectively copes with load changes, reduces vibration, improves motion stability, enhances the accuracy, stability, and production efficiency in the automated crimping process. At the same time, it reduces the equipment failure rate and the defective product rate.

[0068] In some embodiments of the present application, when collecting the static load and dynamic load of the robotic arm to obtain the comprehensive load of the robotic arm, it includes: installing force sensors at the joints of the robotic arm, collecting the force data of each force sensor, and obtaining the static load according to the force data. Installing an acceleration sensor at the end effector of the robotic arm, collecting the acceleration data of the acceleration sensor, and obtaining the dynamic load according to the acceleration data. Obtaining the comprehensive load according to the static load and the dynamic load, and the comprehensive load is calculated by the following formula:

[0069] ;

[0070] where, L represents the comprehensive load, L1 represents the static load, L2 represents the dynamic load, a1 and a2 represent weight coefficients, and a1 + a2 = 1.

[0071] It can be understood that by separately collecting static load and dynamic load data, the load changes of the robotic arm in the stationary and moving states are captured. Compared with the traditional method that only focuses on the static load, this embodiment is more comprehensive and improves the accuracy of load monitoring. Adjust the weight coefficients a1 and a2 of the static and dynamic loads according to different requirements of the task to ensure that the system can respond flexibly under different load conditions. For example, in different grasped object masses and different motion modes, the best load adaptation and speed control can be achieved. In some embodiments of the present application, when obtaining the standard load range, it includes: obtaining the standard load data corresponding to the grasped object mass. Collecting the data of the historical grasped object mass that is the same as the current grasped object mass and has not undergone PID gain adjustment, and extracting the corresponding non-abnormal historical load range from the data. Comparing the non-abnormal historical load range with the standard load data, and generating the standard load range according to the numerical size relationship, where the standard load range includes the left boundary value and the right boundary value.

[0072] In some embodiments of the present application, when determining whether to adjust the PID gain of the robotic arm according to the comparison result, it includes: when the comprehensive load is greater than the right boundary of the standard load range or less than the left boundary of the standard load range, it is determined to adjust the PID gain of the robotic arm. When the comprehensive load is greater than or equal to the left boundary of the standard load range and less than or equal to the right boundary of the standard load range, it is determined not to adjust the PID gain of the robotic arm.

[0073] Specifically, when the load changes, it will directly affect the motion inertia and acceleration of the robotic arm. If the load exceeds the normal range, the response of the robotic arm will vibrate or become unstable, resulting in an increase in action error and affecting the working accuracy. By comparing the load with the standard load range in real time, the abnormal load situation is judged, and the PID gain is dynamically adjusted to make the motion speed of the robotic arm match the load, thereby ensuring the stability and control accuracy of the robotic arm.

[0074] It can be understood that by analyzing historical data, a standard load range is generated, and whether to adjust the PID gain is judged according to the comparison result between the comprehensive load and the standard load range. The load change of the robotic arm is detected in real time, and the PID gain is automatically adjusted according to the situation where the load exceeds the standard range, thereby avoiding the vibration and instability caused by the load change and ensuring the precise control of the robotic arm under different working conditions. Compared with the traditional fixed PID gain method, it can adapt to different load conditions, improve the operation stability and efficiency of the robotic arm, and reduce unnecessary adjustments. Through dynamic PID gain adjustment, it can effectively adapt to load changes, ensure the best performance of the robotic arm in different working environments, and avoid precision errors and production stagnation caused by load fluctuations.

[0075] In some embodiments of the present application, when obtaining the fuzzy processing result by using the fuzzy algorithm and determining the PID gain adjustment scheme according to the fuzzy processing result, it includes: when the comprehensive load is greater than the right boundary of the standard load range, obtaining the first load difference, and reducing the proportional gain of the PID according to the first load difference, where the first load difference is the difference between the comprehensive load and the right boundary. When the comprehensive load is less than the left boundary of the standard load range, obtaining the second load difference, and increasing the proportional gain of the PID according to the second load difference, where the second load difference is the difference between the left boundary and the comprehensive load.

[0076] Specifically, the fuzzy algorithm defines fuzzy sets for inputs and outputs, and uses fuzzy rules to map the input of the system (such as the load difference) to the corresponding fuzzy output (such as the adjustment trend of the PID gain). First, according to the magnitude of the load difference, these data are converted into fuzzy linguistic variables (such as "high load" or "low load"), and then reasoning is carried out through empirical rules (such as "when the load difference is large, reduce the PID gain") to obtain corresponding suggestions for adjusting the PID gain, ensuring the efficient and stable operation of the robotic arm under complex working conditions.

[0077] Specifically, when the combined load exceeds the right boundary of the standard load range, the load is on the high side, resulting in an increase in the inertia of the robotic arm, thereby causing vibration or unstable movement. In this case, by calculating the first load difference, that is, the difference between the combined load and the right boundary, the proportional gain of the PID is reduced according to this difference. By reducing the proportional gain, the acceleration of the robotic arm is reduced, and the influence brought by inertia is reduced, thereby maintaining the smoothness of the movement. When the combined load is lower than the left boundary of the standard load range, the load is on the low side, and the response of the robotic arm becomes sluggish. To improve the response speed, calculate the second load difference, that is, the difference between the left boundary and the combined load, and increase the proportional gain of the PID according to this difference. By increasing the proportional gain, the acceleration of the robotic arm is increased, enabling the robotic arm to respond to control commands faster and reducing the lag of the movement.

[0078] It can be understood that when the load is too large, reducing the proportional gain effectively avoids excessive acceleration and vibration, ensuring the smoothness of the movement of the robotic arm; when the load is too small, increasing the proportional gain can improve the response speed of the robotic arm and avoid control lag. The dynamic PID gain adjustment based on the load difference improves the adaptability of the robotic arm and avoids errors or instability problems caused by load fluctuations. With the support of the fuzzy algorithm, the adjustment of the PID gain is more flexible and intelligent, and can ensure that the robotic arm is always in the best operating state in a changing working environment, improving work efficiency and operation accuracy.

[0079] In some embodiments of the present application, when comparing the feature vector with the historical feature vector in the historical operation data, it includes: performing clustering analysis on the historical feature vector and the feature vector to obtain a clustering result. When the cluster where the feature vector is located contains the historical feature vector, the adjustment coefficient is determined according to the historical adjustment coefficient corresponding to the historical feature vector to adjust the PID gain coefficient. When the cluster where the feature vector is located does not contain the historical feature vector, the adjustment coefficient is determined according to the first load difference or the second load difference to adjust the PID gain coefficient.

[0080] In some embodiments of the present application, when determining the adjustment coefficient according to the historical adjustment coefficient corresponding to the historical feature vector to adjust the PID gain coefficient, it includes:

[0081] ;

[0082] Wherein, T represents an adjustment coefficient, Ti represents the historical adjustment coefficient corresponding to the i-th historical feature vector in the cluster where the feature vector is located, T0 represents the average value of the historical adjustment coefficients corresponding to the historical feature vectors in the cluster where the feature vector is located, and N represents the number of historical feature vectors in the cluster where the feature vector is located.

[0083] In some embodiments of the present application, when adjusting the PID gain coefficient according to the first load difference or the second load difference to determine the adjustment coefficient, it includes:

[0084] When the combined load is greater than the right boundary of the standard load range, adjust the proportional gain of the PID according to the first load difference to determine the adjustment coefficient, and the adjustment coefficient is calculated by the following formula:

[0085] ;

[0086] T represents the adjustment coefficient, represents the first load difference.

[0087] When the combined load is less than the left boundary of the standard load range, adjust the proportional gain of the PID according to the second load difference to determine the adjustment coefficient, and the adjustment coefficient is calculated by the following formula:

[0088] ;

[0089] Wherein, T represents the adjustment coefficient, represents the second load difference.

[0090] Specifically, the historical feature vectors are compared with the current feature vector through cluster analysis, and the cluster analysis helps to group similar feature vectors into the same cluster. If the current feature vector is similar to the historical feature vectors, then they will be in the same cluster. When the current feature vector is grouped into the same cluster as some historical feature vectors, the adjustment coefficient is determined by looking up the historical adjustment coefficients corresponding to the historical feature vectors in this cluster. These historical adjustment coefficients represent the past adjustment experience of the PID gain under similar load conditions and can provide a reference for the current PID adjustment. It avoids the influence of extreme situations of single historical data on the adjustment of the PID gain. If the current feature vector is not in any historical cluster, it means that the current load situation is relatively special, and the adjustment coefficient will be directly calculated according to the first load difference (when the load exceeds the right boundary of the standard load range) or the second load difference (when the load is lower than the left boundary of the standard load range). The greater the load difference, the greater the adjustment range of the PID gain.

[0091] It is understandable that based on the actual adjustment experience of historical data, combined with clustering analysis and load difference, the PID gain is precisely and dynamically adjusted. When the current operating situation is similar to the historical data, the historical adjustment coefficient is automatically extracted, avoiding repeated calculations and misadjustments, and ensuring the smooth transition and efficient adjustment of the PID gain. If the current situation is special, the adjustment coefficient is calculated based on the load difference to ensure that the PID gain adjustment adapts to the change of the load, thus avoiding the hysteresis or overshoot problems of traditional PID control in the face of complex load changes. It improves the stability and accuracy of the robotic arm control, effectively reduces the vibration and error of the robotic arm in dynamic load changes, and enhances the intelligent level and automation efficiency of the system.

[0092] In some embodiments of the present application, when comparing the final position with the desired position and determining whether to give an alarm according to the comparison result, it includes: obtaining a position deviation value according to the final position and the desired position, comparing the position deviation value with a deviation threshold, and determining whether to give an alarm according to the comparison result. When the position deviation value is greater than the deviation threshold, it is determined to give an alarm, and the alarm level is directly proportional to the position deviation value. When the position deviation value is less than or equal to the deviation threshold, it is determined not to give an alarm.

[0093] It is understandable that the position accuracy of the robotic arm is monitored, and whether there is a control accuracy problem is judged according to the comparison between the position deviation value and the deviation threshold. If the position deviation exceeds the set threshold, the alarm mechanism is triggered to prevent task failure or component damage caused by excessive errors. Through the setting of the alarm level, different levels of response measures are taken in a timely manner according to the severity of the error, such as adjusting control parameters or pausing operations. It effectively improves the reliability of the automation system, ensures the accuracy and stability of the robotic arm during task execution, and avoids unnecessary losses or operation interruptions caused by abnormal deviations.

[0094] In the above embodiments, by collecting the mass of the object grasped by the robotic arm, the static load and the dynamic load, the comprehensive load is calculated in real time and compared with the standard load range in the historical operation data to determine whether the PID gain needs to be adjusted, so as to dynamically adjust the running speed of the robotic arm according to the load change. The PID gain adjustment is optimized through the fuzzy algorithm to ensure that the robotic arm can automatically adjust the movement speed in the face of different load conditions, improving the accuracy and stability of the automatic crimping process, and reducing the vibration and uneven movement problems caused by load changes. By comparing the historical data with the real-time load feature vector, the optimal PID gain can be selected according to different load conditions, improving the automatic feeding and assembly accuracy, enhancing the production efficiency, and reducing the equipment failure rate and the defective product rate. This solution improves the self-adaptability and intelligent level of the robotic arm and enhances the performance of the automatic copper column press.

[0095] In another preferred manner based on the above embodiments, refer toFigure 2 As shown in the figure, this embodiment provides a speed control system for a robotic arm, which is used to apply the above-mentioned speed control method for a robotic arm, and includes:

[0096] A force sensor, an acceleration sensor, and a control device. The control device is connected to the force sensor and the acceleration sensor. The control device includes an acquisition unit, a judgment unit, an adjustment unit, and an early warning unit;

[0097] Among them, the acquisition unit is configured to acquire the mass of the object grasped by the robotic arm, match the operating speed according to the mass of the object grasped, and acquire the static load and dynamic load of the robotic arm to obtain the comprehensive load of the robotic arm; acquire the historical operation data of the robotic arm, and screen the data with the same mass of the object grasped and without PID gain adjustment to obtain the standard load range;

[0098] The judgment unit is configured to compare the comprehensive load with the standard load range, and judge whether to perform PID gain adjustment on the robotic arm according to the comparison result; when it is determined to perform PID gain adjustment on the robotic arm, a fuzzy algorithm is used to obtain a fuzzy processing result, and a PID gain adjustment scheme is determined according to the fuzzy processing result;

[0099] The adjustment unit is configured to, after determining the PID gain adjustment scheme, use the mass of the object grasped and the comprehensive load as feature vectors, compare the feature vectors with the historical feature vectors in the historical operation data, determine the adjustment coefficient to adjust the PID gain coefficient, and obtain the adjusted operating speed;

[0100] The early warning unit is configured to operate the robotic arm at the adjusted operating speed, acquire the final position of the object grasped, compare the final position with the expected position, and judge whether to give an early warning according to the comparison result.

[0101] It can be understood that by acquiring the mass of the object grasped by the robotic arm, the static load and the dynamic load, calculating the comprehensive load in real time and comparing it with the standard load range in the historical operation data, it is judged whether PID gain adjustment is required, so as to dynamically adjust the operating speed of the robotic arm according to the load change. By optimizing the PID gain adjustment through the fuzzy algorithm, it is ensured that the robotic arm can automatically adjust the movement speed in the face of different load conditions, improving the accuracy and stability of the automatic crimping process, and reducing the vibration and uneven movement problems caused by load changes. By comparing the historical data with the real-time load feature vectors, the optimal PID gain can be selected according to different load conditions, improving the automatic feeding and assembly accuracy, enhancing the production efficiency, and reducing the equipment failure rate and the defective product rate. This solution improves the adaptability and intelligence level of the robotic arm and enhances the performance of the automatic copper column press.

[0102] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0104] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A speed control method for a robotic arm, characterized in that: include: Collecting the mass of the object gripped by the robot arm, matching the running speed according to the mass of the object gripped, and collecting the static load and dynamic load of the robot arm to obtain the comprehensive load of the robot arm; Collect historical operation data of the robotic arm, select data with the same mass as the clamped object and without PID gain adjustment, and obtain a standard load range; Comparing the comprehensive load with the standard load range, and determining whether to adjust the PID gain of the robot arm according to the comparison result; When it is determined that the PID gain of the robot arm is to be adjusted, a fuzzy algorithm is used to obtain a fuzzy processing result, and a PID gain adjustment scheme is determined according to the fuzzy processing result; After determining the PID gain adjustment scheme, the mass of the clamped object and the comprehensive load are used as feature vectors, the feature vectors are compared with the historical feature vectors in the historical operation data, and the adjustment coefficient is determined to adjust the PID gain coefficient to obtain the adjusted operation speed; Running the robot arm at the adjusted running speed, collecting the final position of the clamped object, comparing the final position with the expected position, and determining whether to issue an early warning based on the comparison result; When obtaining the standard load range, it includes: Acquiring standard load data corresponding to the mass of the clamped object; Collect data on the mass of historical gripped objects that is the same as the mass of the gripped objects and has not undergone PID gain adjustment, and extract the corresponding non-abnormal historical load range from the data; Comparing the non-abnormal historical load range with the standard load data, and generating a standard load range according to the numerical value relationship, wherein the standard load range includes a left boundary value and a right boundary value; When judging whether to adjust the PID gain of the robot arm according to the comparison result, it includes: When the comprehensive load is greater than the right boundary of the standard load range or less than the left boundary of the standard load range, it is determined to adjust the PID gain of the robot arm; When the comprehensive load is greater than or equal to the left boundary of the standard load range and less than or equal to the right boundary of the standard load range, it is determined that the PID gain of the robot arm is not adjusted; When a fuzzy processing result is obtained by using a fuzzy algorithm and a PID gain adjustment scheme is determined according to the fuzzy processing result, the method includes: When the combined load is greater than the right boundary of the standard load range, a first load difference is obtained, and a proportional gain of the PID is reduced according to the first load difference, wherein the first load difference is a difference between the combined load and the right boundary; When the comprehensive load is less than the left boundary of the standard load range, a second load difference is obtained, and the proportional gain of the PID is increased according to the second load difference, where the second load difference is the difference between the left boundary and the comprehensive load; When comparing the feature vector with the historical feature vector in the historical operation data, it includes: Clustering the historical feature vector and the feature vector using cluster analysis to obtain a clustering result; When the cluster containing the feature vector includes a historical feature vector, determining an adjustment coefficient according to a historical adjustment coefficient corresponding to the historical feature vector to adjust the PID gain coefficient; When the cluster containing the characteristic vector does not contain a historical characteristic vector, determining an adjustment coefficient according to the first load difference or the second load difference to adjust the PID gain coefficient; When the adjustment coefficient is determined according to the historical adjustment coefficient corresponding to the historical characteristic vector to adjust the PID gain coefficient, it includes: ； Among them, T represents the adjustment coefficient, Ti represents the historical adjustment coefficient corresponding to the i-th historical feature vector in the cluster where the feature vector is located, T0 represents the mean of the historical adjustment coefficients corresponding to the historical feature vectors in the cluster where the feature vector is located, and N represents the number of historical feature vectors in the cluster where the feature vector is located.

2. The speed control method for a robot arm according to claim 1, characterized in that: Collecting the static load and the dynamic load of the robot arm to obtain the comprehensive load of the robot arm includes: Installing force sensors at the joints of the mechanical arm, collecting force data of each force sensor, and obtaining the static load according to the force data; Installing an acceleration sensor on the end effector of the robotic arm, collecting acceleration data of the acceleration sensor, and obtaining the dynamic load according to the acceleration data; A comprehensive load is obtained according to the static load and the dynamic load, and the comprehensive load is calculated by the following formula: ； Among them, L represents the comprehensive load, L1 represents the static load, L2 represents the dynamic load, a1 and a2 represent the weight coefficients, and a1+a2=1.

3. The speed control method for a robot arm according to claim 1, characterized in that: When the adjustment coefficient is determined according to the first load difference or the second load difference to adjust the PID gain coefficient, it includes: When the comprehensive load is greater than the right boundary of the standard load range, the proportional gain of the PID is adjusted by determining an adjustment coefficient according to the first load difference, and the adjustment coefficient is calculated by the following formula: ； T represents the adjustment coefficient, ∆L1 represents the first load difference; When the comprehensive load is less than the left boundary of the standard load range, the proportional gain of the PID is adjusted by determining an adjustment coefficient according to the second load difference, and the adjustment coefficient is calculated by the following formula: ； Wherein, T represents the adjustment coefficient and ∆L2 represents the second load difference.

4. The speed control method for a robot arm according to claim 1, characterized in that: The final position is compared with the expected position, and judging whether to issue an early warning according to the comparison result includes: Obtaining a position deviation value according to the final position and the expected position, comparing the position deviation value with a deviation threshold, and determining whether to issue an early warning according to the comparison result; When the position deviation value is greater than the deviation threshold, it is determined to issue an early warning, and the early warning level is proportional to the position deviation value; When the position deviation value is less than or equal to the deviation threshold, it is determined that no warning is issued.

5. A speed control system for a robot arm, used to apply the speed control method for a robot arm as claimed in any one of claims 1 to 4, characterized in that: include: A force sensor, an acceleration sensor and a control device, wherein the control device is connected to the force sensor and the acceleration sensor, and the control device includes a collection unit, a judgment unit, an adjustment unit and an early warning unit; The acquisition unit is configured to acquire the mass of the object gripped by the robot arm, match the operating speed according to the mass of the object gripped, and acquire the static load and dynamic load of the robot arm to obtain the comprehensive load of the robot arm; acquire the historical operation data of the robot arm, filter the data with the same mass as the object gripped and without PID gain adjustment, and acquire the standard load range; The judgment unit is configured to compare the comprehensive load with the standard load range, and judge whether to perform PID gain adjustment on the robot arm according to the comparison result; when it is determined that the PID gain adjustment is to be performed on the robot arm, a fuzzy algorithm is used to obtain a fuzzy processing result, and a PID gain adjustment scheme is determined according to the fuzzy processing result; The adjustment unit is configured to, after determining the PID gain adjustment scheme, use the clamped object mass and the comprehensive load as feature vectors, compare the feature vectors with historical feature vectors in the historical operation data, determine an adjustment coefficient, adjust the PID gain coefficient, and obtain an adjusted operating speed; The early warning unit is configured to operate the robotic arm at the adjusted operating speed, collect the final position of the clamped object, compare the final position with the expected position, and determine whether to issue an early warning based on the comparison result.

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