Speed control system and control method for mechanical arm

Abstract

The invention relates to the technical field of mechanical arm control, and discloses a speed control system and method for a mechanical arm, and the method comprises the steps: collecting the mass of a clamped object of the mechanical arm, matching the mass with the operation speed, and obtaining the comprehensive load of the mechanical arm; historical operation data are collected and screened, and a standard load range is obtained; the comprehensive load is compared with a standard load range, and whether PID gain adjustment is conducted on the mechanical arm or not is judged; during adjustment, determining a PID gain adjustment scheme according to a fuzzy processing result; the collected feature vectors are compared with historical feature vectors in historical operation data, an adjustment coefficient is determined to adjust a PID gain coefficient, and the adjusted operation speed is obtained; and the final position of the clamped object is collected and compared with the expected position, and whether early warning is conducted or not is judged. The automatic feeding and assembling precision is improved, the production efficiency is improved, and the equipment failure rate and the defective product rate are reduced.

Description

Technical Field

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

[0002] The workflow of the copper pillar automatic crimping machine usually includes the steps of automatic copper pillar loading, automatic lens mount loading, automatic crimping and automatic unloading. The equipment firmly connects the copper pillar to the specified accessories by applying pressure. Due to its high level of automation, the copper pillar automatic crimping machine can achieve fast, accurate and repeatable crimping operations, thereby significantly improving production efficiency and connection quality.

[0003] Despite the high degree of automation, the copper column automatic crimping machine still faces some challenges in practical applications, especially in the automatic loading and assembly process of the lens mount. When the robot arm is performing tasks, factors such as the mass of the clamped object and the load of the robot arm itself will have an important impact on the movement. For example, when the mass of the clamped object is large, the load of the robot arm increases, causing its movement inertia to increase. If the original speed is maintained, the robot arm will vibrate and move unsteadily, affecting the automatic loading and assembly accuracy.

[0004] Therefore, it is necessary to design a speed control system and control 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 control method for a robotic arm, aiming to solve the problem existing in the current robotic arm speed control technology that the operating speed cannot be adjusted according to real-time load feedback.

[0006] In one aspect, the present invention provides a speed control method for a robotic arm, comprising: 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; The robot arm is operated at the adjusted operating speed, and the final position of the clamped object is acquired, and the final position is compared with the expected position, and it is determined whether to issue an early warning according to the comparison result.

[0007] Furthermore, collecting the static load and the dynamic load of the robotic arm to obtain the comprehensive load of the robotic 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 robot 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.

[0008] Furthermore, when obtaining the standard load range, it includes: Acquiring standard load data corresponding to the mass of the clamped object; Collect data of historical clamped object mass that is the same as the clamped object mass and has not been subjected to PID gain adjustment, and extract the corresponding non-abnormal historical load range from the data; The non-abnormal historical load range is compared with the standard load data, and a standard load range is generated according to the numerical value relationship, wherein the standard load range includes a left boundary value and a right boundary value.

[0009] Further, judging whether to adjust the PID gain of the robot arm according to the comparison result 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 adjustment of the robot arm is not performed.

[0010] Furthermore, 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, including: 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 combined load is less than the left boundary of the standard load range, a second load difference is obtained, and the proportional gain of PID is increased according to the second load difference, where the second load difference is the difference between the left boundary and the combined load.

[0011] Furthermore, 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 include a historical characteristic vector, an adjustment coefficient is determined according to the first load difference or the second load difference to adjust the PID gain coefficient.

[0012] Further, when the adjustment coefficient is determined according to the historical adjustment coefficient corresponding to the historical feature 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.

[0013] Further, 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, 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: ; Where T represents the adjustment coefficient, Indicates the second load difference.

[0014] Furthermore, 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.

[0015] Compared with the prior art, the beneficial effects of the present invention are: by collecting the mass of the clamped object, static load and dynamic load of the robot arm, the comprehensive load is calculated in real time and compared 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 operation speed of the robot arm according to the load change. The PID gain adjustment is optimized by the fuzzy algorithm to ensure that the robot arm can automatically adjust the movement speed when facing different load conditions, improve the accuracy and stability of the automated crimping process, and reduce the vibration and unstable movement problems caused by load changes. By comparing historical data with real-time load characteristic vectors, the optimal PID gain can be selected according to different load conditions, which improves the accuracy of automatic feeding and assembly, improves production efficiency, and reduces equipment failure rate and defective product rate. This solution improves the adaptability and intelligence level of the robot arm and improves the performance of the copper column automatic crimping machine.

[0016] On the other hand, the present application also provides a speed control system for a robotic arm, which is used to apply the above speed control method for a robotic arm, including: 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.

[0017] It can be understood that the above-mentioned speed control system and control method for the robotic arm have the same beneficial effects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flow chart of a speed control method for a robotic arm provided by an embodiment of the present invention; Figure 2 A structural block diagram of a speed control system for a robotic arm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] In some embodiments of the present application, see Figure 1 As shown, a speed control method for a robotic arm comprises: S100: 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.

[0021] S200: Collect historical operation data of the robot arm, select data with the same mass as the clamped object and without PID gain adjustment, and obtain a standard load range.

[0022] S300: Compare the comprehensive load with the standard load range, and determine whether to adjust the PID gain of the robot arm according to the comparison result.

[0023] S400: When it is determined to adjust the PID gain of 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.

[0024] S500: 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 historical feature vectors in historical operation data, and the adjustment coefficient is determined to adjust the PID gain coefficient to obtain the adjusted operating speed.

[0025] S600: The robot arm is operated at the adjusted operating speed, and the final position of the clamped object is collected, and the final position is compared with the expected position, and it is determined whether to issue an early warning according to the comparison result.

[0026] Specifically, in S100, the mass of the object clamped by the robot arm is collected through sensors. According to the mass of the clamped object, the appropriate operating speed is matched for the first time. When the robot arm performs a task, the mass of the clamped object directly affects the force and motion inertia required. The static load and dynamic load information of the robot arm are collected. The static load refers to the load applied to the robot arm when it is in a stationary state, and the dynamic load takes into account the inertia and external force generated during the movement of the robot arm. Through the load data, the comprehensive load is obtained. In S200, the historical operation data of the robot arm is collected, especially the records of the same clamped object mass and no PID gain adjustment. Based on the historical data, a standard load range is established, that is, the normal working range of the robot arm under similar load conditions. In S300, after obtaining the comprehensive load and the standard load range, the current comprehensive load is compared with the standard load range. If the current comprehensive load exceeds the standard load range, it means that the load exceeds the standard or decreases, and it is determined whether the PID gain needs to be adjusted based on the comparison result. The goal of PID gain adjustment is to ensure that the robot arm can maintain stable motion control when facing 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, and then a solution is provided for the PID gain adjustment. In S500, after determining the PID gain adjustment solution, the mass of the clamped object and the comprehensive load information are used as feature vectors and compared 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 to obtain an optimal operating speed. Through data-driven feature vector comparison, the operating speed can be adaptively adjusted according to different load conditions. In S600, the robot arm performs the task at the adjusted operating speed and collects the final position of the clamped object through the sensor. This position is compared with the expected position. If the actual position of the clamped object differs greatly from the expected position, an early warning is issued based on the deviation result to remind the operator or the system to make further adjustments to ensure the final assembly accuracy.

[0027] It is understandable that by integrating load monitoring, historical data comparison, fuzzy control and PID gain dynamic adjustment technology, adaptive control of the robot arm under different load conditions is achieved. By collecting the mass and load information of the clamped object in real time, and combining historical data with the support of fuzzy algorithm, it is determined whether the PID gain needs to be adjusted and the running speed of the robot arm is optimized. Compared with the traditional fixed PID gain adjustment method, it effectively responds to load changes, reduces vibration and improves motion stability, and improves the accuracy, stability and production efficiency of the automated crimping process. At the same time, it reduces the equipment failure rate and defective product rate.

[0028] In some embodiments of the present application, when collecting the static load and dynamic load of the robot arm and obtaining the comprehensive load of the robot arm, it includes: installing force sensors at the joints of the robot arm, collecting the force data of each force sensor, and obtaining the static load based on the force data. Installing an acceleration sensor at the end effector of the robot arm, collecting the acceleration data of the acceleration sensor, and obtaining the dynamic load based on the acceleration data. The comprehensive load is obtained based on 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.

[0029] It is understandable that by collecting static load and dynamic load data respectively, the load changes of the robot arm in static and moving states are captured. Compared with the traditional method that only focuses on static load, this embodiment is more comprehensive and improves the accuracy of load monitoring. The weight coefficients a1 and a2 of static and dynamic loads are adjusted according to the different requirements of the task to ensure that the system can respond flexibly under different load conditions. For example, under different clamping mass 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 standard load data corresponding to the clamping mass. Collect data in which the historical clamping mass is the same as the clamping mass and the PID gain adjustment is not performed, and extract the corresponding non-abnormal historical load range from the data. Compare the non-abnormal historical load range with the standard load data, and generate a standard load range based on the numerical value relationship, wherein the standard load range includes a left boundary value and a right boundary value.

[0030] In some embodiments of the present application, 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 that the PID gain of the robot arm is adjusted. 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 robot arm.

[0031] Specifically, when the load changes, it will directly affect the robot's motion inertia and acceleration. If the load exceeds the normal range, the robot's response will vibrate or become unstable, resulting in increased motion errors and affecting work accuracy. By comparing the load with the standard load range in real time, the load abnormality can be determined, and the PID gain can be dynamically adjusted to match the robot's motion speed with the load, thereby ensuring the robot's stability and control accuracy.

[0032] It is understandable that by analyzing historical data, a standard load range is generated, and the PID gain needs to be adjusted based on the comparison results between the comprehensive load and the standard load range. The load changes of the robot arm are detected in real time, and the PID gain is automatically adjusted according to the load exceeding the standard range, thereby avoiding vibration and instability caused by load changes and ensuring the precise control of the robot arm under different working conditions. Compared with the traditional fixed PID gain method, it can adapt to different load conditions, improve the operating stability and efficiency of the robot arm, and reduce unnecessary adjustments. Through dynamic PID gain adjustment, it can effectively adapt to load changes, ensure the optimal performance of the robot arm in different operating environments, and avoid accuracy errors and production stagnation caused by load fluctuations.

[0033] In some embodiments of the present application, a fuzzy algorithm is used to obtain a fuzzy processing result, and when a PID gain adjustment scheme is determined according to the fuzzy processing result, it includes: when the comprehensive load is greater than the right boundary of the standard load range, a first load difference is obtained, and the proportional gain of the PID is reduced according to the first load difference, and 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, a second load difference is obtained, and the proportional gain of the PID is increased according to the second load difference, and the second load difference is the difference between the left boundary and the comprehensive load.

[0034] Specifically, the fuzzy algorithm defines fuzzy sets of inputs and outputs, and uses fuzzy rules to map the system input (such as load difference) to the corresponding fuzzy output (such as the adjustment trend of PID gain). First, according to the size of the load difference, these data are converted into fuzzy language variables (such as "high load" or "low load"), and then reasoning is performed through empirical rules (such as "when the load difference is large, reduce PID gain") to obtain corresponding PID gain adjustment suggestions to ensure the efficient and stable operation of the robot arm under complex working conditions.

[0035] Specifically, when the combined load exceeds the right boundary of the standard load range, the load is too high, resulting in an increase in the inertia of the robot 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 robot arm is reduced, and the impact of 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 too small and the response of the robot arm becomes slow. In order to increase the response speed, the second load difference, that is, the difference between the left boundary and the combined load, is calculated, and the proportional gain of the PID is increased according to this difference. By increasing the proportional gain, the acceleration of the robot arm is increased, so that the robot arm can respond to control instructions faster and reduce the lag of movement.

[0036] It is understandable that when the load is too large, reducing the proportional gain effectively avoids over-acceleration and vibration, ensuring the smooth movement of the robot arm; when the load is too small, increasing the proportional gain can improve the response speed of the robot arm and avoid control lag. The dynamic PID gain adjustment based on the load difference improves the adaptability of the robot arm and avoids errors or instability caused by load fluctuations. With the support of fuzzy algorithms, the adjustment of PID gain is more flexible and intelligent, which can ensure that the robot arm is always in the best operating state in a changing working environment, improving work efficiency and operation accuracy.

[0037] In some embodiments of the present application, 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 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.

[0038] In some embodiments of the present application, when the adjustment coefficient is determined according to the historical adjustment coefficient corresponding to the historical feature vector to adjust the PID gain coefficient, it includes: ;

[0039] 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.

[0040] In some embodiments of the present application, 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 combined load is greater than the right boundary of the standard load range, the proportional gain of the PID is adjusted by determining the adjustment coefficient according to the first load difference. The adjustment coefficient is calculated by the following formula: ;

[0041] T represents the adjustment coefficient, Indicates the first load difference.

[0042] When the combined load is less than the left boundary of the standard load range, the proportional gain of the PID is adjusted by determining the adjustment coefficient according to the second load difference. The adjustment coefficient is calculated by the following formula: ; Where T represents the adjustment coefficient, Indicates the second load difference.

[0043] Specifically, historical feature vectors are compared with current feature vectors through cluster analysis, and cluster analysis helps to classify similar feature vectors into the same cluster. If the current feature vector is similar to the historical feature vector, they will be in the same cluster. When the current feature vector is classified into the same cluster as some historical feature vectors, the adjustment coefficient is determined by finding the historical adjustment coefficient corresponding to the historical feature vector in the cluster. These historical adjustment coefficients represent the adjustment experience of PID gain under similar load conditions in the past, and can provide a reference for the current PID adjustment. It avoids the extreme situation of a single historical data affecting the adjustment of 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 based on 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 larger the load difference, the greater the adjustment of the PID gain.

[0044] It is understandable that the PID gain is adjusted dynamically and accurately based on the actual adjustment experience of historical data, combined with cluster analysis and load difference. When the current operating conditions are similar to historical data, the historical adjustment coefficient is automatically extracted to avoid repeated calculations and misadjustments, ensuring a 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 load changes, thereby avoiding the lag or overshoot problems of traditional PID control in the face of complex load changes. The stability and accuracy of the robot arm control are improved, the vibration and error of the robot arm in dynamic load changes are effectively reduced, and the intelligence level and automation efficiency of the system are improved.

[0045] In some embodiments of the present application, 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 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 not to issue an early warning.

[0046] It is understandable that the position accuracy of the robot arm is monitored, and the comparison between the position deviation value and the deviation threshold is used to determine whether there is a control accuracy problem. If the position deviation exceeds the set threshold, the early warning mechanism is triggered to prevent mission failure or component damage due to excessive errors. By setting the early warning 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 suspending operations. This effectively improves the reliability of the automation system, ensures the accuracy and stability of the robot arm when performing tasks, and avoids unnecessary losses or operation interruptions due to abnormal deviations.

[0047] In the above embodiment, by collecting the mass of the clamped object, static load and dynamic load of the robot arm, the comprehensive load is calculated in real time and compared 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 operation speed of the robot arm according to the load change. The PID gain adjustment is optimized by the fuzzy algorithm to ensure that the robot arm can automatically adjust the movement speed when facing different load conditions, improve the accuracy and stability of the automated crimping process, and reduce the vibration and unstable movement problems caused by load changes. By comparing historical data with real-time load characteristic vectors, the optimal PID gain can be selected according to different load conditions, which improves the accuracy of automatic feeding and assembly, improves production efficiency, and reduces equipment failure rate and defective product rate. This solution improves the adaptability and intelligence level of the robot arm and improves the performance of the copper column automatic crimping machine.

[0048] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a speed control system for a robotic arm, which is used to apply the above speed control method for a robotic arm, including: 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 picked up by the robot arm, match the running speed according to the mass of the object picked up, and acquire the static load and dynamic load of the robot arm to obtain the comprehensive load of the robot arm; acquire the historical running data of the robot arm, filter the data with the same mass as the object picked up and without PID gain adjustment, and obtain the standard load range; 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 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 plan is determined according to the fuzzy processing result; The adjustment unit is configured to, after determining the PID gain adjustment scheme, use the mass of the clamped object and the comprehensive load as a feature vector, compare the feature vector with a historical feature vector in the historical operation data, determine an adjustment coefficient, adjust the PID gain coefficient, and obtain an adjusted operation speed; The early warning unit is configured to run the robotic arm at the adjusted running 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.

[0049] It can be understood that by collecting the mass of the clamped object, static load and dynamic load of the robot arm, 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 operation speed of the robot arm according to the load change. The PID gain adjustment is optimized by fuzzy algorithm to ensure that the robot arm can automatically adjust the movement speed when facing different load conditions, improve the accuracy and stability of the automated crimping process, and reduce the vibration and unstable movement caused by load changes. By comparing historical data with real-time load feature vectors, the optimal PID gain can be selected according to different load conditions, which improves the accuracy of automatic feeding and assembly, improves production efficiency, and reduces equipment failure rate and defective product rate. This solution improves the adaptability and intelligence level of the robot arm and improves the performance of the copper column automatic crimping machine.

[0050] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may 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.) containing computer-usable program codes.

[0051] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0052] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0053] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection 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; The robot arm is operated at the adjusted operating speed, and the final position of the clamped object is collected, the final position is compared with the expected position, and it is determined whether to issue an early warning based on the comparison result.

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 obtaining the standard load range, it includes: Acquiring standard load data corresponding to the mass of the clamped object; Collect data of historical clamped object mass that is the same as the clamped object mass and has not been subjected to PID gain adjustment, and extract the corresponding non-abnormal historical load range from the data; The non-abnormal historical load range is compared with the standard load data, and a standard load range is generated according to the numerical value relationship, wherein the standard load range includes a left boundary value and a right boundary value.

4. The speed control method for a robot arm according to claim 3, characterized in that: 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 adjustment of the robot arm is not performed.

5. The speed control method for a robot arm according to claim 4, characterized in that: 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 combined load is less than the left boundary of the standard load range, a second load difference is obtained, and the proportional gain of PID is increased according to the second load difference, where the second load difference is the difference between the left boundary and the combined load.

6. The speed control method for a robot arm according to claim 5, characterized in that: 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 include a historical characteristic vector, an adjustment coefficient is determined according to the first load difference or the second load difference to adjust the PID gain coefficient.

7. The speed control method for a robot arm according to claim 6, characterized in that: 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.

8. The speed control method for a robot arm according to claim 7, 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, 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: ； Where T represents the adjustment coefficient, Indicates the second load difference.

9. 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.

10. 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 9, 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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