Mechanical arm and control method thereof

Abstract

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm and a control method thereof. The method comprises the following steps that real-time load information of all joints of the mechanical arm in the operation process is obtained, the acceleration and rotational inertia of the mechanical arm are monitored in real time, and real-time inertia force data are generated; the torque compensation amount needed by the mechanical arm is calculated according to the real-time inertia force data, and torque compensation control data are generated; performing dynamic analysis on the torque compensation control data, and performing real-time adjustment on the output torque of the servo motor based on the current load and inertia change condition to generate inertia compensation data; and the motion path and the motion speed of the mechanical arm are dynamically optimized according to the inertia compensation data, and path optimization data are obtained. Through real-time monitoring, dynamic adjustment and intelligent optimization, the operation precision, the movement efficiency, the stability and the self-adaptive capacity of the mechanical arm in the complex working environment are remarkably improved.

Description

Technical Field

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

[0002] The current robotic arms on the market are usually unable to efficiently deal with the load changes that occur during the work process. Load changes will directly affect the inertial characteristics of the robotic arm, which will lead to dynamic changes in torque requirements. If the robotic arm cannot compensate for the inertial force generated by the load change in time, the system may experience problems such as overload, insufficient power, or delayed action. These problems make it impossible for the robotic arm to maintain high-precision work efficiency when dealing with complex tasks, especially in scenarios where the load is unstable or changes frequently. When the robotic arm moves at high speed or changes direction rapidly, the inertial force is an important factor affecting its working stability. At present, most robotic arm control systems on the market rely on pre-set models, and the compensation strategy for inertial force is usually fixed and cannot be dynamically adjusted according to the real-time working status. This fixed compensation method is prone to insufficient or over-compensation of torque compensation, thereby affecting the motion accuracy and energy efficiency of the robotic arm. At the same time, insufficient inertial force compensation may also cause increased joint wear, thereby affecting the service life of the robotic arm. The robotic arm is usually composed of multiple joints, and the motion coordination between each joint directly determines the working efficiency and accuracy of the overall system. However, most robotic arms on the market lack precise coordination control mechanisms between their joints, and the movements between joints are often out of sync, especially when operating under high load or high speed. The asynchrony between joints can lead to path deviation, reduced work efficiency, and even damage to system hardware. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a robotic arm and a control method thereof to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above object, a control method of a robotic arm comprises the following steps:

[0005] Step S1: obtaining the real-time load information of each joint of the robot arm during operation, and performing real-time monitoring of the acceleration and rotational inertia of the robot arm to generate real-time inertial force data; calculating the torque compensation required by the robot arm according to the real-time inertial force data to generate torque compensation control data;

[0006] Step S2: dynamically analyzing the torque compensation control data, and adjusting the output torque of the servo motor in real time based on the current load and inertia changes to generate inertia compensation data; dynamically optimizing the motion path and motion speed of the robot arm according to the inertia compensation data to obtain path optimization data;

[0007] Step S3: predicting the inertia force and torque requirements of the robot arm under various load conditions, and generating a torque compensation optimization instruction; based on the path optimization data, adjusting the output torque of the servo motor using the torque compensation optimization instruction, and obtaining torque optimization adjustment data;

[0008] Step S4: Based on the path optimization data and the torque optimization adjustment data, the torque compensation of each joint of the robot arm is synchronously optimized to generate multi-joint collaborative control data; the collaborative motion state between the joints is adjusted according to the multi-joint collaborative control data to obtain joint synchronous control data;

[0009] Step S5: dynamically tune the inertia force compensation and torque compensation based on the joint synchronization control data and the real-time inertia force data, and generate a tuning control instruction; comprehensively optimize the torque compensation and inertia compensation of the robot arm according to the tuning control instruction, and obtain torque and inertia comprehensive compensation data;

[0010] Step S6: remotely monitor the operation status of the robot arm based on the cloud platform, and generate a long-term operation optimization model using the torque and inertia comprehensive compensation data; dynamically optimize the torque compensation and inertia compensation algorithms of the robot arm using the long-term operation optimization model to generate operation mode optimization data;

[0011] Step S7: Optimize the data according to the operation mode and generate an intelligent optimization plan for the robot arm using the cloud platform; coordinately control the servo motor and sensor system of the robot arm based on the intelligent optimization plan to obtain multi-system synchronous control data.

[0012] The present invention also provides a mechanical arm, comprising a joint and a support structure, a servo motor, an end effector and a control system, wherein the servo motor is deployed on the joint and the support structure, the end effector is connected to the joint and the support structure, the control system is installed in the servo motor, and the control system is used to execute a control method for the mechanical arm, and the control system comprises:

[0013] The real-time load and inertia monitoring module is used to obtain the real-time load information of each joint of the robot arm during operation, and to monitor the acceleration and rotational inertia of the robot arm in real time to generate real-time inertial force data; the required torque compensation amount of the robot arm is calculated based on the real-time inertial force data to generate torque compensation control data;

[0014] The torque compensation and path optimization module is used to dynamically analyze the torque compensation control data, and adjust the output torque of the servo motor in real time based on the current load and inertia changes to generate inertia compensation data; dynamically optimize the motion path and motion speed of the robot arm according to the inertia compensation data to obtain path optimization data;

[0015] The load prediction and torque optimization module is used to predict the inertia force and torque requirements of the robot under various load conditions and generate torque compensation optimization instructions; based on the path optimization data, the output torque of the servo motor is adjusted using the torque compensation optimization instructions to obtain torque optimization adjustment data;

[0016] The multi-joint collaborative optimization module is used to synchronously optimize the torque compensation of each joint of the robot arm based on the path optimization data and the torque optimization adjustment data, and generate multi-joint collaborative control data; according to the multi-joint collaborative control data, the collaborative motion state between each joint is adjusted to obtain joint synchronous control data;

[0017] The dynamic tuning and comprehensive compensation module is used to dynamically tune the inertia force compensation and torque compensation based on the joint synchronization control data and real-time inertia force data, and generate tuning control instructions; according to the tuning control instructions, the torque compensation and inertia compensation of the robot arm are comprehensively optimized to obtain the torque and inertia comprehensive compensation data;

[0018] The remote monitoring and long-term optimization module is used to remotely monitor the operation status of the robot arm based on the cloud platform, and generate a long-term operation optimization model using the torque and inertia comprehensive compensation data; the long-term operation optimization model is used to dynamically optimize the torque compensation and inertia compensation algorithms of the robot arm to generate operation mode optimization data;

[0019] The intelligent optimization and system collaborative control module is used to optimize data according to the operating mode and generate an intelligent optimization plan for the robotic arm using the cloud platform; based on the intelligent optimization plan, the servo motor and sensor system of the robotic arm are collaboratively controlled to obtain multi-system synchronous control data.

[0020] The present invention monitors the acceleration, rotational inertia and real-time load of each joint of the robot arm during operation and generates real-time inertial force data, thereby ensuring real-time control of the operation state of the robot arm and effectively improving the response speed and control accuracy of the system. The torque compensation required by the robot arm is calculated based on the inertial force data, and the output torque of the servo motor is dynamically adjusted through the torque compensation control data to ensure that the robot arm maintains stable and efficient operation in complex tasks, especially when the load changes, it can respond and adapt quickly. The motion path and speed of the robot arm are optimized through the inertial compensation data, which improves the work efficiency and accuracy of the robot arm, avoids the problems of path deviation and low efficiency, and enables it to better adapt to complex working environments. The torque demand prediction function under various load conditions is introduced to improve the ability to predict the motion state of the robot arm in advance, ensure the accuracy and real-time performance of the torque compensation, and reduce the situation of sudden torque shortage or redundant compensation of the robot arm when performing tasks. The collaborative working state between the joints is optimized through multi-joint collaborative control data, which effectively solves the problem of asynchronous movement between the multi-joints of the robot arm and improves the overall motion coordination and system stability of the robot arm. The dynamic tuning mechanism ensures the coordination and consistency of torque and inertia compensation, further improves the overall working stability of the robot arm, reduces joint wear, and helps to extend the service life of the robot arm. Combined with the remote monitoring function of the cloud platform, the robot arm can analyze its own operating status in real time, generate a long-term optimization model, and dynamically adjust the control algorithm. This function enables the system to perform intelligent optimization based on the long-term operating data of the robot arm, thereby improving work efficiency and effectively responding to potential faults. Based on the operation mode optimization data, the robot arm can generate an intelligent optimization plan and perform multi-system synchronous control, realize the coordinated work of systems such as servo motors and sensors, further improve the intelligence level and control effect of the robot arm, and make it have higher stability and flexibility in complex operating environments. Through real-time monitoring, dynamic adjustment and intelligent optimization, the present invention effectively improves the operating accuracy, motion efficiency, stability and adaptive ability of the robot arm, and solves the problems of insufficient accuracy, multi-joint incoordination, slow response speed, etc. of the existing robot arm in a complex working environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0022] Figure 1 It is a schematic diagram of the steps of the present invention based on the robot arm and the control method thereof;

[0023] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0024] Figure 3 for Figure 1 Detailed step flow diagram of step S2;

[0025] Figure 4 for Figure 1 Detailed step flow chart of step S3 in FIG. DETAILED DESCRIPTION

[0026] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0028] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0029] To achieve this, please refer to Figures 1 to 4 The present invention provides a method for controlling a robot arm, the method comprising the following steps:

[0030] Step S1: obtaining the real-time load information of each joint of the robot arm during operation, and performing real-time monitoring of the acceleration and rotational inertia of the robot arm to generate real-time inertial force data; calculating the torque compensation required by the robot arm according to the real-time inertial force data to generate torque compensation control data;

[0031] Step S2: dynamically analyzing the torque compensation control data, and adjusting the output torque of the servo motor in real time based on the current load and inertia changes to generate inertia compensation data; dynamically optimizing the motion path and motion speed of the robot arm according to the inertia compensation data to obtain path optimization data;

[0032] Step S3: predicting the inertia force and torque requirements of the robot arm under various load conditions, and generating a torque compensation optimization instruction; based on the path optimization data, adjusting the output torque of the servo motor using the torque compensation optimization instruction, and obtaining torque optimization adjustment data;

[0033] Step S4: Based on the path optimization data and the torque optimization adjustment data, the torque compensation of each joint of the robot arm is synchronously optimized to generate multi-joint collaborative control data; the collaborative motion state between the joints is adjusted according to the multi-joint collaborative control data to obtain joint synchronous control data;

[0034] Step S5: dynamically tune the inertia force compensation and torque compensation based on the joint synchronization control data and the real-time inertia force data, and generate a tuning control instruction; comprehensively optimize the torque compensation and inertia compensation of the robot arm according to the tuning control instruction, and obtain torque and inertia comprehensive compensation data;

[0035] Step S6: remotely monitor the operation status of the robot arm based on the cloud platform, and generate a long-term operation optimization model using the torque and inertia comprehensive compensation data; dynamically optimize the torque compensation and inertia compensation algorithms of the robot arm using the long-term operation optimization model to generate operation mode optimization data;

[0036] Step S7: Optimize the data according to the operation mode and generate an intelligent optimization plan for the robot arm using the cloud platform; coordinately control the servo motor and sensor system of the robot arm based on the intelligent optimization plan to obtain multi-system synchronous control data.

[0037] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of controlling a robotic arm of the present invention. In this example, the control method of the robotic arm includes the following steps:

[0038] Step S1: obtaining the real-time load information of each joint of the robot arm during operation, and performing real-time monitoring of the acceleration and rotational inertia of the robot arm to generate real-time inertial force data; calculating the torque compensation required by the robot arm according to the real-time inertial force data to generate torque compensation control data;

[0039] The embodiment of the present invention is equipped with a plurality of load sensors, and a load sensor is installed at each joint position to monitor the load borne by the joint in real time. These sensors can sense and record real-time force and torque information. Through the embedded control system, the data of these sensors are regularly collected and transmitted to the main control unit to form a real-time load information set. Accelerometers and gyroscopes are installed at each joint of the mechanical arm to monitor the acceleration and rotation state of the mechanical arm in real time. These devices can measure the linear acceleration and angular velocity of the joint, and then calculate the rotational inertia of the mechanical arm. By collecting and processing these data, the dynamic state of the mechanical arm during operation can be calculated and the motion characteristics of each joint can be identified. Using the acquired real-time load information, acceleration and rotational inertia data, a dynamic model is used to analyze the current state of the mechanical arm to generate real-time inertial force data. Based on the real-time inertial force data, a formula is used to calculate the torque compensation required for each joint of the mechanical arm. This calculation process can dynamically adapt to various operating conditions and load changes, and provide accurate torque compensation control data for subsequent steps. The calculated torque compensation is converted into a control signal and stored in a digital form in the control system. These signals will be used to adjust the output torque of the servo motor in the subsequent steps. At this stage, the system will also update the data in real time. All collected data is transmitted to the cloud platform or local control unit through the internal network or data bus for subsequent data analysis and processing.

[0040] Step S2: dynamically analyzing the torque compensation control data, and adjusting the output torque of the servo motor in real time based on the current load and inertia changes to generate inertia compensation data; dynamically optimizing the motion path and motion speed of the robot arm according to the inertia compensation data to obtain path optimization data;

[0041] In the embodiment of the present invention, the torque compensation control data is transmitted to the control system. The processing unit of the control system receives these data and parses them to obtain the torque compensation information required for each joint. The control system compares these control data with the current real-time load information to identify potential differences and adjustment requirements. The system monitors the operating status of the robot arm in real time through load sensors and inertial sensors. These data are used to determine the current working status and motion of the robot arm. The control algorithm dynamically analyzes the output torque of the servo motor based on the data collected in real time, and calculates the required adjustment amount to ensure the stability and accuracy of the robot arm. According to the dynamic analysis results, the control system generates adjustment instructions for the servo motor. These instructions send control signals to each servo motor to adjust its output torque in real time. The adjusted output torque depends on the current load, inertia data and preset control strategy to ensure that the robot arm can adapt to changing working conditions. After adjusting the output torque of the servo motor in real time, the system records and generates new inertia compensation data. These data are used for path optimization and motion speed adjustment in subsequent steps. The motion path will be optimized based on the current inertia and load data to ensure that the robot is more efficient and consumes less energy during movement. The movement speed will also be adjusted according to the real-time status. Based on the dynamic optimization of the motion path and speed, path optimization data is generated. The system will feed back all collected data and generated optimization results to the cloud platform or local control interface in real time. Through the visual interface, the operator can view the operating status of the robot at any time and make adjustments as needed.

[0042] Step S3: predicting the inertia force and torque requirements of the robot arm under various load conditions, and generating a torque compensation optimization instruction; based on the path optimization data, adjusting the output torque of the servo motor using the torque compensation optimization instruction, and obtaining torque optimization adjustment data;

[0043] The embodiment of the present invention transmits the inertia compensation data and the path optimization data to the control system. Based on the collected historical data and real-time data, the control system uses algorithms such as machine learning or model predictive control (MPC) to establish a prediction model for the inertia force and torque demand of the robot under different load conditions. The model uses past operating data (such as load changes, speed changes, inertia changes, etc.) to predict the inertia force and torque requirements under different working conditions. Considering the dynamic characteristics of the robot, the model can be updated in time to adapt to the new operating environment. Using the output of the prediction model, the control system generates corresponding torque compensation optimization instructions. The optimization instructions specifically include the torque compensation value to be applied to each joint, and the acceleration or deceleration strategy that may be required. The control system combines the path optimization data to comprehensively adjust the generated torque compensation optimization instructions. With the assistance of the path optimization data, the system will optimize the motion strategy to meet the requirements of torque and inertia compensation. Based on the torque compensation optimization instructions generated above, the control system will adjust the output torque of the servo motor. The system generates corresponding torque optimization adjustment data, which will be used for multi-joint collaborative control in subsequent steps to further optimize the motion performance of the robot. The generated torque optimization adjustment data will be fed back to the cloud platform and control interface in real time for operators to monitor and manage. In actual operation, the system regularly verifies the prediction model and optimizes the accuracy of the model by comparing the expected results with the actual output. If there is a significant difference between the predicted results and the actual performance, the system will adjust the prediction algorithm based on the new data to improve the subsequent prediction accuracy and control capabilities.

[0044] Step S4: Based on the path optimization data and the torque optimization adjustment data, the torque compensation of each joint of the robot arm is synchronously optimized to generate multi-joint collaborative control data; the collaborative motion state between the joints is adjusted according to the multi-joint collaborative control data to obtain joint synchronous control data;

[0045] The embodiment of the present invention transmits the torque optimization adjustment data and the path optimization data to the control system, and at the same time, the real-time monitoring system collects the real-time state data of each joint. The control system first comprehensively analyzes the torque compensation requirements of each joint, and calculates the specific torque compensation value required for each joint in the current state in combination with the path optimization data. This calculation process takes into account the coupling effect between the joints to ensure that no negative impact is caused when compensation is performed. According to the calculated torque compensation value, the control system generates multi-joint collaborative control data, specifically including the torque compensation amount, expected motion trajectory and speed to be applied to each joint. This data also needs to ensure the coordination of the movements between the joints to avoid the instability of the overall movement caused by the adjustment of individual joints. Using the multi-joint collaborative control data, the system adjusts the motion state between the joints to ensure that the joints can move in coordination when performing complex tasks, and the control algorithm will adjust the motion parameters according to real-time feedback. In the process of multi-joint collaborative control, the system continuously monitors the real-time motion state and feedback information of each joint. If it is found that the movement of a joint deviates from the predetermined trajectory, the system will immediately adjust the torque compensation amount of the joint to restore normal movement. Based on the multi-joint collaborative control data generated by the above steps, the control system will output joint synchronization control data. These data will guide the motion parameters of each joint, such as acceleration, speed and position. In actual applications, the control system continuously optimizes the control strategy, learns historical data and real-time feedback based on the coordinated control effect of each joint.

[0046] Step S5: dynamically tune the inertia force compensation and torque compensation based on the joint synchronization control data and the real-time inertia force data, and generate a tuning control instruction; comprehensively optimize the torque compensation and inertia compensation of the robot arm according to the tuning control instruction, and obtain torque and inertia comprehensive compensation data;

[0047] The control system of the embodiment of the present invention collects real-time inertial force data and joint synchronization control data from the sensors of each joint. The control system first analyzes the real-time collected data to identify the inertial force compensation and torque compensation requirements of the current joint. Combined with the joint synchronization control data, the system will generate a tuning control instruction to indicate how to adjust the torque and inertia compensation of each joint. According to the tuning control instruction, the system will adjust the torque compensation and inertia compensation of each joint in real time. The adjustment process is based on the current load condition and motion state to ensure effective compensation between each joint and avoid the overall motion out of control due to the deviation of individual joints. The system will comprehensively optimize the torque compensation and inertia compensation of the manipulator through real-time feedback and tuning control instructions, and generate torque and inertia comprehensive compensation data. During the entire tuning process, the system continuously monitors the motion state of each joint and collects new real-time data to evaluate the adjustment effect. If it is found that the performance of a joint does not meet expectations after tuning, the system will automatically correct the tuning strategy and continue to optimize the compensation amount. Once the tuning process is completed, the system will evaluate the adjusted performance and compare the data differences before and after optimization to ensure that the newly generated torque and inertia comprehensive compensation data can effectively improve the motion accuracy and stability of the robot arm. The tuning control instructions and torque and inertia comprehensive compensation data are stored on the cloud platform, which will also use this data to update the operation mode optimization model.

[0048] Step S6: remotely monitor the operation status of the robot arm based on the cloud platform, and generate a long-term operation optimization model using the torque and inertia comprehensive compensation data; dynamically optimize the torque compensation and inertia compensation algorithms of the robot arm using the long-term operation optimization model to generate operation mode optimization data;

[0049] The embodiment of the present invention continuously receives real-time data from each joint of the robotic arm through the cloud platform, and uses powerful data processing capabilities to analyze the collected real-time data, identify the performance and potential problems of the robotic arm under different operating conditions, and the focus of the analysis includes the effectiveness of inertia force compensation and torque compensation. Based on the analysis results of the real-time data, the system establishes a long-term operation optimization model. The system dynamically optimizes the torque compensation and inertia compensation algorithms of the robotic arm according to the generated optimization model. By adjusting the control algorithm parameters, the adaptability of the robotic arm to load changes and environmental changes is enhanced. To ensure the optimization effect, the cloud platform sets a feedback mechanism to evaluate the optimized operation mode in real time. By comparing the performance indicators before and after optimization, the system can identify the improvement effect and make necessary adjustments. The system converts the results of dynamic optimization into operation mode optimization data. The optimized operation mode data is stored on the cloud platform for subsequent query and analysis. At the same time, these data can be shared with other robotic arms to promote the intelligent development of the overall system.

[0050] Step S7: Optimize the data according to the operation mode and generate an intelligent optimization plan for the robot arm using the cloud platform; coordinately control the servo motor and sensor system of the robot arm based on the intelligent optimization plan to obtain multi-system synchronous control data.

[0051] The embodiment of the present invention collects and organizes operation mode optimization data, uses machine learning and optimization algorithms, systematically analyzes the collected data, and generates an intelligent optimization solution. The intelligent optimization solution is applied to the servo motor and sensor system of the robot arm. This integration is achieved through a communication protocol, so that the output torque and motion path of the servo motor can be dynamically adjusted according to the optimization solution. The control system monitors the motion feedback of each joint in real time and adjusts the control instructions in time. During operation, the system maintains a real-time connection with the cloud platform and continuously receives sensor data and motion state feedback. If a deviation is found, the system will immediately adjust the control parameters. The system evaluates the operating status after implementation and analyzes the efficiency and accuracy of the robot arm when performing tasks. According to the evaluation results, the intelligent optimization solution is adjusted. The intelligent optimization solution will be updated regularly, combined with new data and changes in the operating environment to ensure that the system can adapt to changing working conditions.

[0052] The present invention can obtain the load information and inertia data of each joint in real time, ensuring rapid response to various dynamic changes during operation. This real-time monitoring significantly improves the adaptability and reliability of the system. The robot arm can dynamically tune the torque and inertia compensation according to the real-time data. This precise control optimizes the performance of the robot arm under different load conditions and reduces the error caused by inertia changes. The motion path and speed of the robot arm are dynamically optimized, ensuring the efficiency and accuracy when performing complex tasks and improving work efficiency. The coordinated motion ability between the joints is enhanced, so that the robot arm can smoothly and efficiently complete multi-joint collaborative tasks, reducing mechanical wear and failure rate. Through remote monitoring of the cloud platform, the long-term performance stability of the robot arm during operation is guaranteed, and the control algorithm can be adjusted at any time to cope with different working environments and task requirements. Based on the generation of intelligent optimization schemes, the robot arm can perform more complex tasks, adapt to changing industrial needs, and provide more intelligent decision support. Not only does it achieve current task optimization, but it also has the ability to make continuous improvements based on historical data, further improving the flexibility and intelligence level of the system.

[0053] Preferably, step S1 comprises the following steps:

[0054] Step S11: monitoring the joint load information of the robot arm during operation in real time through load sensors installed at each joint, and generating real-time load data of each joint;

[0055] Step S12: Based on the acceleration sensors and inertial measurement units installed at each joint, the acceleration, angular velocity and rotational inertia of the robot arm are monitored in real time to generate inertial measurement data;

[0056] Step S13: Comprehensively analyzing the real-time load data and the inertial measurement data, and calculating the inertial force of each joint based on the load condition of the robotic arm to generate real-time inertial force data;

[0057] Step S14: Calculate the torque compensation required for each joint of the robot arm using a dynamics model according to the real-time inertial force data and the real-time load data to generate real-time torque demand data;

[0058] Step S15: Control the output torque of the servo motor in real time according to the real-time torque demand data to obtain torque compensation control data.

[0059] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0060] Step S11: monitoring the joint load information of the robot arm during operation in real time through load sensors installed at each joint, and generating real-time load data of each joint;

[0061] In the embodiment of the present invention, a high-precision load sensor is installed at each joint of the mechanical arm. The sensor should have sufficient sensitivity and response speed to accurately monitor the changes in the joint load during operation. A real-time data acquisition system is designed and deployed to ensure that the load sensor can continuously transmit data to the central control unit. The system needs to have data processing and analysis capabilities to achieve real-time feedback. The load sensor transmits the monitored joint load information to the central control system by wired or wireless means. The received joint load data is formatted for subsequent analysis and application. The system should have sufficient storage capacity to record load data and support historical data retrospective analysis. Through the data processing algorithm, real-time load data of each joint is generated. A data visualization interface is implemented to allow the operator to monitor the load status of each joint in real time. The interface should be able to be dynamically updated to display the real-time load changes of each joint to help the operator make adjustments in time. A safety monitoring mechanism is integrated in the load monitoring system. When the load of a joint exceeds the preset safety threshold, the system can automatically issue an alarm and record abnormal data to prevent potential equipment damage or safety accidents.

[0062] Step S12: Based on the acceleration sensors and inertial measurement units installed at each joint, the acceleration, angular velocity and rotational inertia of the robot arm are monitored in real time to generate inertial measurement data;

[0063] The embodiment of the present invention installs high-precision acceleration sensors and inertial measurement units (IMUs) at each joint of the robotic arm to ensure that they can capture the acceleration, angular velocity, and rotational inertia data of the joints in real time. A real-time data acquisition system is configured to transmit the data output by the sensor to a central control unit. The sensor data is processed in real time through a software algorithm to calculate the acceleration, angular velocity, and rotational inertia of each joint, and the collected data is formatted. The real-time acceleration and rotation data are displayed on the monitoring interface, allowing the operator to quickly understand the motion status of the robotic arm.

[0064] Step S13: Comprehensively analyzing the real-time load data and the inertial measurement data, and calculating the inertial force of each joint based on the load condition of the robotic arm to generate real-time inertial force data;

[0065] The embodiment of the present invention integrates the real-time load data and inertial measurement data through a central processing unit to form a comprehensive data set. An algorithm is used to analyze the joint load data and the inertial measurement data to calculate the inertial force of each joint under the current operating conditions. The motion state and environmental factors of the robot arm are considered during the analysis. Physical models and dynamic equations are applied to calculate the inertial force of each joint based on real-time load and acceleration data. The generated real-time inertial force data is formatted to support the calculation of torque compensation. The calculation results are displayed in real time on the operation interface, allowing the operator to monitor the status of the robot arm and manually intervene or adjust as needed.

[0066] Step S14: Calculate the torque compensation required for each joint of the robot arm using a dynamics model according to the real-time inertial force data and the real-time load data to generate real-time torque demand data;

[0067] The embodiment of the present invention constructs a dynamic model based on the geometric parameters and kinematic characteristics of the robot arm, including the motion state, load conditions and inertial characteristics of each joint. Using the dynamic model, the torque required for each joint under the current load and inertial force is calculated. Considering the movement direction and angle of the joint, the required compensation torque is calculated using a formula. The calculated real-time torque demand data is formatted and stored. The torque demand results are displayed in real time on the operation interface, allowing the operator to monitor and evaluate the performance and responsiveness of the robot arm.

[0068] Step S15: Control the output torque of the servo motor in real time according to the real-time torque demand data to obtain torque compensation control data.

[0069] The embodiment of the present invention adopts a closed-loop control algorithm to compare the actual output torque with the required torque in real time. Use algorithms such as PID controllers to reduce errors and respond quickly. According to the adjustment value calculated by the control algorithm, the output torque of the servo motor is adjusted in real time to meet the real-time torque demand. Monitor the operating status of the servo motor and detect the output torque in real time through the feedback system. If an abnormality is found, the system will immediately alarm and adjust the control strategy. The data generated during the control process is recorded in the system for subsequent analysis and optimization of the performance of the robotic arm to ensure long-term stability.

[0070] By installing load sensors and acceleration sensors at each joint, the present invention enables the robot arm to monitor load and inertia data in real time, ensuring a rapid response to operating conditions. By comprehensively analyzing the real-time load and inertia measurement data, the system can accurately calculate the torque compensation required for each joint, ensuring the stability and safety of the robot arm under various working conditions. Using dynamic models and real-time control algorithms, the servo motor can be adjusted instantly according to the real-time torque demand data, thereby optimizing the movement accuracy and efficiency of the robot arm. By controlling torque compensation in real time, the robot arm can maintain high precision in complex operations, reduce errors caused by load changes, and improve overall work efficiency. The feedback mechanism of the entire control system ensures that the servo motor can be adjusted in time under any abnormal circumstances, thereby improving the safety and reliability of the system.

[0071] Preferably, step S2 comprises the following steps:

[0072] Step S21: Based on the torque compensation control data, a dynamic analysis algorithm is used to evaluate the torque demand of each joint to obtain dynamic torque analysis data;

[0073] Step S22: monitor the output torque and load condition of the servo motor in real time through the torque sensor on the servo motor to generate servo motor output monitoring data;

[0074] Step S23: performing inertia change analysis on the torque demand influencing factor based on the dynamic torque analysis data and the servo motor output monitoring data to generate inertia compensation data;

[0075] Step S24: dynamically adjust the motion path and speed of the robot arm using the inertia compensation data to generate path optimization data.

[0076] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0077] Step S21: Based on the torque compensation control data, a dynamic analysis algorithm is used to evaluate the torque demand of each joint to obtain dynamic torque analysis data;

[0078] The embodiment of the present invention uses a dynamic analysis algorithm to process the collected data and evaluate the torque requirements of each joint under the current operating conditions. Based on the results of the dynamic analysis, dynamic torque analysis data are generated. These data contain the torque compensation required for each joint and reflect the real-time changes under different load and motion conditions. The dynamic torque analysis data is used to guide the control decision of the servo motor to ensure the stability and accuracy of the robot arm under various operating conditions.

[0079] Step S22: monitor the output torque and load condition of the servo motor in real time through the torque sensor on the servo motor to generate servo motor output monitoring data;

[0080] In an embodiment of the present invention, a torque sensor is installed on the servo motor to monitor the output torque and load condition of the motor in real time. During the operation of the robotic arm, the output torque data and motor load status of the servo motor are collected in real time. The data acquisition frequency needs to be high enough to capture rapidly changing torque information. The collected torque monitoring data is preprocessed, including noise filtering, data smoothing, etc. Based on the processed data, the servo motor output monitoring data is generated, including the actual output torque of the motor, load changes and related timestamp information. The monitoring data is fed back to the control system for dynamic torque demand evaluation and inertia change analysis, so as to adjust the control strategy in time.

[0081] Step S23: performing inertia change analysis on the torque demand influencing factor based on the dynamic torque analysis data and the servo motor output monitoring data to generate inertia compensation data;

[0082] The embodiment of the present invention collects dynamic torque analysis data and servo motor output monitoring data. Identify and define key factors that affect torque requirements, including load changes of the robotic arm, changes in motion path, external environmental influences (such as wind, vibration, etc.), and acceleration and deceleration during operation. Use a dynamic analysis algorithm to analyze the integrated data and evaluate how these influencing factors lead to changes in inertial force. Calculate the inertia changes under different states through mathematical models (such as dynamic models). Generate inertia compensation data based on the results of the inertia change analysis. These data provide compensation strategies for different operating conditions, which are used to dynamically adjust the control parameters of the robotic arm. Feed the generated inertia compensation data back to the control system.

[0083] Step S24: dynamically adjust the motion path and speed of the robot arm using the inertia compensation data to generate path optimization data.

[0084] The embodiment of the present invention uses inertia compensation data to calculate the optimal motion path and speed of the robot under the current operating conditions. Dynamic adjustments are made using optimization algorithms based on real-time load and inertia conditions. Dynamic models and control algorithms are applied to consider factors such as acceleration, deceleration, coordinated motion between joints, and adaptability to environmental changes. These algorithms will comprehensively consider the dynamic characteristics of the robot to generate the optimal path. The adjusted path and speed parameters are converted into path optimization data, including specific motion trajectories, time allocations, and motion instructions for each joint. The path optimization data is fed back to the control system of the robot, and the control parameters are further fine-tuned by monitoring the responses of each joint in real time.

[0085] The system of the present invention can monitor and evaluate the torque requirements of each joint in real time, ensuring that the robot arm maintains optimal performance under different loads and operating conditions. Using dynamic analysis algorithms and real-time monitoring data, the motion path and speed of the robot arm can be dynamically adjusted, thereby improving the accuracy and response speed of the motion. Through the analysis of inertia changes, the system can effectively compensate for the dynamic instability caused by load changes, and enhance the reliability of the robot arm in complex operating environments. The generation of path optimization data enables the robot arm to reduce unnecessary movement when performing tasks, reduce energy consumption, and improve work efficiency. Real-time monitoring and dynamic adjustment mechanisms ensure the safety of the robot arm when performing operations, avoiding potential risks caused by overload or loss of motion control.

[0086] Preferably, step S3 comprises the following steps:

[0087] Step S31: monitoring the inertial force data of the robot arm under various load conditions in real time through a sensor system to obtain inertial force data;

[0088] Step S32: Based on the motion characteristics of the robot arm, a prediction model of the robot arm is constructed using the inertial force data, and the prediction model is used to predict the torque demand under each load condition to generate torque demand prediction data;

[0089] Step S33: Optimizing and analyzing the torque demand prediction data to generate torque compensation optimization instructions;

[0090] Step S34: Integrate the path optimization data with the torque compensation optimization instruction, identify the optimal output torque of the servo motor, and generate torque optimization adjustment data.

[0091] As an embodiment of the present invention, refer to Figure 4 As shown, Figure 1 Detailed step flow diagram of step S3 in the embodiment of the present invention, step S3 includes the following steps:

[0092] Step S31: monitoring the inertial force data of the robot arm under various load conditions in real time through a sensor system to obtain inertial force data;

[0093] The embodiment of the present invention installs a variety of sensors at the key joints and connection parts of the robot arm, and the system monitors the inertial force data of the robot arm under various load conditions in real time. These sensors regularly collect the load information, acceleration and rotational angular velocity of the joints to generate real-time inertial force data. These data are transmitted to the central processing unit through the data acquisition module for preliminary processing and filtering, and the system forms a comprehensive inertial force data set.

[0094] Step S32: Based on the motion characteristics of the robot arm, a prediction model of the robot arm is constructed using the inertial force data, and the prediction model is used to predict the torque demand under each load condition to generate torque demand prediction data;

[0095] The inertial force data of the embodiment of the present invention is combined with the motion characteristics of the robotic arm to construct a set of prediction models. The model is based on machine learning or dynamic analysis and can simulate the dynamic behavior of the robotic arm under different load conditions. By inputting real-time inertial force data, the model predicts the torque demand under each load condition and generates corresponding torque demand prediction data. These prediction data provide a basis for subsequent torque compensation optimization, ensuring that the robotic arm can adjust the output torque in time according to the actual load conditions when performing tasks, thereby improving the operation accuracy and stability.

[0096] Step S33: Optimizing and analyzing the torque demand prediction data to generate torque compensation optimization instructions;

[0097] The embodiments of the present invention utilize optimization algorithms, such as genetic algorithms or gradient descent methods, to evaluate the predicted data to identify and eliminate potential errors or uncertainties. In this process, the optimization objectives may include minimizing energy consumption, improving response speed, or enhancing stability, taking into account the dynamic characteristics and load changes of the robotic arm. Torque compensation optimization instructions are generated based on the optimization results. These instructions will guide the servo motor to adjust the output torque under different operating conditions to ensure efficient operation and precise control of the robotic arm under various loads.

[0098] Step S34: Integrate the path optimization data with the torque compensation optimization instruction, identify the optimal output torque of the servo motor, and generate torque optimization adjustment data.

[0099] The embodiment of the present invention analyzes the output torque required for each path by comparing the path optimization data under different load conditions. A multivariable optimization algorithm is used to ensure that the optimal output torque of the servo motor is identified while meeting the motion requirements of the robot arm. The generated torque optimization adjustment data will provide accurate output instructions for the servo motor to dynamically adjust the torque, thereby optimizing the performance and efficiency of the robot arm under various working conditions.

[0100] The present invention significantly improves the operating accuracy and efficiency of the robot arm under various load conditions through real-time monitoring and dynamic analysis. It ensures a comprehensive assessment of the inertial force and torque requirements, allowing the robot arm to adapt to complex working environments. By building a prediction model and optimizing instructions, the motion path and output torque of the robot arm are intelligently adjusted, thereby achieving higher response speed and stability. This dynamic optimization method improves the overall performance of the robot arm, making it more adaptable and reliable in automation tasks.

[0101] Preferably, step S4 comprises the following steps:

[0102] Step S41: performing demand analysis on the torque demand and motion state of each joint based on the path optimization data and the torque optimization adjustment data to obtain joint torque demand data;

[0103] The embodiment of the present invention collects the torque demand data of each joint under different working conditions, and analyzes it in combination with the current motion state of the robot arm. A dynamic analysis algorithm is applied to calculate the difference between the actual load and the expected load of each joint, and the joint torque demand data is generated.

[0104] Step S42: synchronously optimizing the torque compensation of each joint of the robot arm according to the joint torque demand data to generate multi-joint collaborative control data;

[0105] The embodiment of the present invention analyzes the torque requirements of each joint and identifies the joints that need to be compensated. The optimization algorithm is applied to calculate the torque compensation required for each joint taking into account the current load, motion state and torque requirements. Multi-joint collaborative control data is generated to ensure that the torque compensation of all joints is coordinated with each other to avoid overload or underload. The optimized torque compensation is applied to servo motor control to achieve precise motion adjustment and ensure the stability and efficiency of the robot arm in complex tasks.

[0106] Step S43: adjusting the coordinated motion state between the joints in real time according to the multi-joint coordinated control data, and recording the adjusted joint motion state to generate joint synchronous control data.

[0107] The embodiment of the present invention obtains the current motion state data of each joint, compares it with the multi-joint coordinated control data, and identifies the existing deviations. A dynamic adjustment algorithm is applied to fine-tune the torque compensation and motion parameters of each joint to optimize the overall motion coordination. The response of each joint is monitored in real time to ensure that the adjustment effect meets expectations. The adjusted joint motion state is recorded, and joint synchronization control data is generated to provide reference and basis for subsequent motion.

[0108] The present invention can achieve synchronous optimization of multiple joints by analyzing the torque requirements and motion states of each joint, making the robot arm more coordinated when performing complex tasks and reducing motion impact. The system can monitor and adjust the motion state of each joint in real time to ensure that the robot arm can still maintain efficient and stable operation under load changes or environmental interference. Through the generated joint synchronization control data, the accuracy of the robot arm during operation is significantly improved, and it can better meet the needs of complex tasks. In the synchronous optimization of torque compensation, unnecessary energy consumption is reduced, the working efficiency of the servo motor is improved, and the service life of the robot arm is extended.

[0109] Preferably, step S5 comprises the following steps:

[0110] Step S51: Analyze the real-time data, identify the inertia force and torque compensation requirements of each joint, and obtain the expected torque compensation data;

[0111] The embodiment of the present invention collects data of the robot arm in real time during operation through the sensor system installed at each joint, including information such as joint angle, speed, load and acceleration. Using these real-time data, a dynamic analysis algorithm is used to calculate the inertia force and torque compensation requirements of each joint. The system will model the motion state of each joint and identify the torque compensation required for each joint under the current load and motion conditions. Generate the desired torque compensation data to provide a basis for subsequent tuning control instructions.

[0112] Step S52: analyzing the expected torque compensation data according to the joint synchronization control data to generate a tuning control instruction;

[0113] An embodiment of the present invention receives desired torque compensation data and joint synchronization control data. The two sets of data are comprehensively analyzed by an algorithm to evaluate the gap between the current joint state and the desired compensation. Using the model prediction and optimization algorithm, tuning control instructions are generated to indicate how to adjust the torque compensation of each joint. This process may include weight allocation and priority setting to ensure that the coordinated optimization of each joint can meet the overall performance requirements under different loads and motion states. The tuning control instructions will provide specific adjustment strategies for the next step of torque and inertia comprehensive optimization.

[0114] Step S53: According to the tuning control instruction, the torque compensation and inertia compensation of the robot arm are comprehensively optimized, and the motion state and performance of each joint are monitored in real time, the compensation effect of the optimized torque and inertia is evaluated, and the torque and inertia comprehensive compensation data is obtained.

[0115] The system of the embodiment of the present invention receives tuning control instructions. These instructions describe in detail the adjustments that need to be made to the torque compensation and inertia compensation of each joint. According to these instructions, the system adjusts the torque and inertia compensation values ​​of each joint of the robotic arm in real time. The system will use sensors to monitor the motion state and performance indicators of each joint in real time, including angular velocity, position, load, etc. Through the feedback mechanism, the system continuously evaluates the effect of the compensation adjustment and identifies whether the expected torque and inertia compensation levels have been achieved. Based on real-time monitoring data, the system generates torque and inertia comprehensive compensation data to reflect the performance of each joint under the new settings.

[0116] The present invention significantly improves the flexibility and response speed of the system. Through real-time data analysis, the system can accurately identify the inertia force and torque compensation requirements of each joint, thereby generating the desired torque compensation data. The generation of tuning control instructions ensures that the compensation scheme of each joint can be dynamically adjusted based on the real-time situation. During the comprehensive optimization process, the motion state and performance of each joint are monitored in real time, so that the compensation effect can be evaluated in a timely manner. The torque and inertia comprehensive compensation data finally generated not only improves the operating accuracy of the robot arm, but also enhances its adaptability under different load conditions. This method makes the application of the robot arm in complex environments more reliable and efficient, thereby improving the overall operating efficiency.

[0117] Preferably, step S53 includes the following steps:

[0118] Step S531: performing comprehensive optimization on the torque and inertia compensation of each joint of the robot arm according to the tuning control instruction, and collecting the motion parameters of each joint in real time through the sensor system to obtain optimized motion parameter data;

[0119] In the embodiment of the present invention, the control system of the robotic arm comprehensively optimizes the torque and inertia compensation of each joint according to the previously generated tuning control instructions. The system adjusts the output torque of each joint according to the real-time data obtained by the sensor system to adapt to the current operating requirements and environmental changes. Through the sensors installed at each joint of the robotic arm, the motion parameters of each joint are collected in real time, including data such as position, speed, acceleration and output torque. These data will be transmitted to the central processing unit for processing and analysis. After calculation, optimized motion parameter data are obtained, which not only reflects the current state of the joint, but also provides a basis for subsequent comparative analysis.

[0120] Step S532: Compare and analyze the optimized motion parameter data with the expected torque compensation value to obtain expected deviation data;

[0121] The embodiment of the present invention obtains motion parameter data after comprehensive optimization, which includes information such as the current position, speed, acceleration and output torque of each joint. The system compares and analyzes these optimized motion parameter data with the preset expected torque compensation value. The comparative analysis process uses a difference calculation algorithm to calculate the deviation between the optimized motion parameters of each joint and the expected value. These deviations reflect the gap between the actual performance of the current joint torque and inertia compensation and the expected target.

[0122] Step S533: Based on the expected deviation data, the torque response of each joint when the load changes and the change of inertia force before and after compensation are evaluated and analyzed to obtain the torque and inertia comprehensive compensation data.

[0123] The embodiment of the present invention uses sensors to monitor the actual torque output and inertia force of each joint under different load conditions in real time, and records the data before and after compensation. The collected actual torque and inertia force data are compared with the preset expected values ​​to analyze the compensation effect. Through comprehensive analysis, the system evaluates the torque response ability and inertia compensation effect of each joint under load changes to determine whether the set performance indicators are met. If a large deviation is found, the compensation strategy can be further adjusted to improve the control accuracy and response speed of the robotic arm. The system generates a torque and inertia comprehensive compensation data report, which is provided to the operator or used for subsequent optimization.

[0124] Through comprehensive optimization, the torque and inertia compensation of the robot arm are effectively improved, ensuring that each joint can respond accurately under different load conditions. This precise control makes the robot arm more stable and reliable when performing complex tasks. The system monitors and analyzes the motion parameters of each joint in real time, can quickly adapt to different load changes, and improves the adaptability of the robot arm in a dynamic environment. This flexibility enables the robot arm to perform well in a variety of operating scenarios. By comparing and analyzing the optimized motion parameters and expected values, the system can quickly identify and correct deviations and provide real-time feedback on the compensation effect. This feedback mechanism helps to continuously improve the performance of the robot arm and ensure that it is always in the best working state. Through precise torque and inertia compensation, the robot arm can effectively reduce unnecessary energy consumption and improve energy efficiency during operation. This is especially important in long-term work or large-scale production. The implementation of this control method provides a basis for the intelligence of the robot arm, and combined with data analysis and feedback mechanism, the robot arm gradually develops in the direction of self-adaptation and autonomous decision-making.

[0125] Preferably, step S6 comprises the following steps:

[0126] Step S61: obtaining real-time operation status data of the robot arm through the cloud platform, and performing performance evaluation and analysis on the real-time operation status data, identifying abnormal situations, and obtaining performance abnormality data;

[0127] The embodiment of the present invention obtains the operating status data of the robotic arm in real time through a sensor system connected to a cloud platform. On the cloud platform, the real-time operating status data is evaluated using a data analysis algorithm. The analysis includes comparing with preset performance indicators to identify potential performance anomalies, such as joint wear, overload or control errors. Through the set thresholds and standards, the system automatically identifies data that deviates from the normal operating state and forms performance anomaly data. These abnormal data include the specific type of anomaly, the time of occurrence and its possible scope of impact. The identified performance anomaly data is summarized and a detailed anomaly report is generated for subsequent root cause analysis and optimization decision-making. When a major anomaly is identified, the system will send a real-time notification to the operator through the cloud platform so that timely measures can be taken to prevent the fault from expanding.

[0128] Step S62: Analyze the root causes of the abnormal performance data and build a long-term operation optimization model;

[0129] The embodiment of the present invention collects the identified performance abnormality data, which may include abnormal torque output, joint temperature, operating speed and other information. Using data analysis tools and algorithms, the performance abnormality data is deeply analyzed to find the root cause of the abnormality. The analysis may include methods such as trend analysis, correlation analysis and multivariate regression to identify the relationship between various parameters. Based on the results of the root cause analysis, a long-term operation optimization model is constructed. The model will take into account the design characteristics, working environment and historical operation data of the robotic arm. By simulating the constructed optimization model, its effectiveness and reliability under different working conditions are verified to ensure that the model can adapt to actual operation requirements. After the model is successfully verified, the optimization model is updated regularly to reflect the actual operating status and potential changes of the robotic arm.

[0130] Step S63: dynamically optimizing the torque compensation and inertia compensation of the robot arm using the long-term operation optimization model to generate optimized compensation data;

[0131] The embodiment of the present invention takes the long-term operation optimization model as the basis and inputs the real-time operation status data of the robot arm, including parameters such as the torque demand, inertia force, and load condition of each joint. The dynamic optimization algorithm is applied to process the input data. These algorithms can evaluate the effect of torque and inertia compensation based on real-time data and find the best compensation strategy. Through model calculation, the optimized compensation data for the current operation state is generated, and the calculation will cover the torque compensation and inertia compensation of all joints. The generated optimized compensation data is simulated and verified to evaluate its effectiveness and stability in actual operation. During the verification process, the simulation environment can be used to observe the response and performance changes of the robot arm. A feedback mechanism is established to monitor the effect of the optimized compensation data in the operation of the robot arm in real time. If new abnormalities or performance degradation are found during operation, the model and optimization strategy are updated in time.

[0132] Step S64: Push the optimization compensation data to the robot control system through the cloud platform to generate operation mode optimization data.

[0133] The embodiment of the present invention organizes the optimization compensation data, including the torque compensation and inertia compensation values ​​of each joint. Through the API interface of the cloud platform, a secure communication channel with the robotic arm control system is established to ensure the security and reliability of data transmission. The optimization compensation data is packaged and pushed to the robotic arm control system in real time through the cloud platform. During the push process, a receipt mechanism can be set to confirm that the data is successfully received. In the robotic arm control system, the optimization compensation data is received and unpacked, and a preliminary verification is performed to ensure the integrity and accuracy of the data. According to the received optimization compensation data, the operation mode of the robotic arm is updated. Specifically, it includes adjusting the compensation parameters in the control algorithm to improve motion accuracy and stability. After implementing the new operation mode, the operating status of the robotic arm is continuously monitored and feedback data is collected to evaluate the optimization effect. If problems are found, adjustments or re-optimizations are made in a timely manner.

[0134] The present invention obtains real-time operation status data through a cloud platform, promptly identifies performance anomalies of the robotic arm, and ensures that the system can quickly respond to potential problems. An in-depth analysis of abnormal data is performed to form a long-term operation optimization model, which helps the system understand and predict the cause of the failure, thereby improving reliability. The optimization model is used to dynamically adjust the torque and inertia compensation, which improves the adaptability of the robotic arm under different loads and working conditions and enhances its overall performance. The optimization data is pushed to the control system through the cloud platform to achieve data-driven optimization control and ensure that the robotic arm can operate in the optimal mode. Real-time monitoring and feedback of the implementation effect promote continuous optimization of the system, improve the work efficiency and accuracy of the robotic arm, and reduce the failure rate. Through intelligent data processing and dynamic optimization, efficient and stable operation of the robotic arm is achieved, and its ability to adapt to complex environments is improved.

[0135] Preferably, step S7 comprises the following steps:

[0136] Step S71: using the cloud platform to pre-process and integrate the real-time load information, path optimization data, torque and inertia comprehensive compensation data, and operation mode optimization data to obtain optimized control data of the robotic arm;

[0137] The embodiment of the present invention collects the load information, path optimization data, torque and inertia comprehensive compensation data and operation mode optimization data of the robot arm in real time through the cloud platform. These data can be automatically collected by the sensor system and uploaded to the cloud platform. Invalid or erroneous data points are removed, and data from different sources are converted into a unified format for subsequent analysis. The pre-processed data is integrated to form a comprehensive data set. Use a suitable data structure for storage for subsequent processing. By analyzing the integrated data, optimized control data suitable for robot arm control is generated. These data contain optimization suggestions for the current working state to provide a basis for subsequent intelligent optimization solutions.

[0138] Step S72: Analyze the optimization control data using a genetic algorithm to generate an intelligent optimization solution for the robot arm;

[0139] The embodiment of the present invention sets a fitness function to evaluate the performance of each solution, which usually includes indicators such as energy consumption, motion efficiency and response time. The population size, crossover rate and mutation rate of the genetic algorithm are set to ensure that the algorithm can effectively search for the optimal solution. The optimization control data is converted into a coding format suitable for genetic algorithm processing to perform genetic operations. The suitable individuals are determined to enter the next generation through selection operations. The selected individuals are crossover operated to generate new individuals to explore more solution spaces. Mutation operations are performed on some individuals to increase the diversity of the population and avoid falling into local optimal solutions. Fitness evaluation is performed on the newly generated individuals, and the performance indicators of each individual are recorded. The process of selection, crossover and mutation is repeated until the set termination condition (such as the maximum number of iterations or fitness threshold) is reached. The individual with the best fitness is selected from the final population to generate an intelligent optimization solution for the robotic arm. This solution includes an optimization control strategy for load, path and compensation requirements.

[0140] Step S73: formulating a coordinated control strategy for the servo motor and the sensor system of the robot arm according to the intelligent optimization scheme to obtain an optimized coordinated control strategy;

[0141] The embodiment of the present invention analyzes each control strategy in the intelligent optimization scheme and identifies its specific impact on the servo motor and sensor system. Determine the key parameters that affect the performance of the robotic arm, such as position, speed, acceleration, torque demand and sensor feedback. Apply control theory to design a control algorithm suitable for the servo motor and sensor system. Combine real-time load information, path optimization data and torque and inertia comprehensive compensation data to form a comprehensive control strategy. Determine the hierarchical structure of the control framework, including low-level motor control and high-level motion planning. Ensure effective communication between the servo motor and the sensor, and adopt a suitable communication protocol. Run the optimized collaborative control strategy in a simulation environment to verify its performance and stability. According to the simulation results, adjust the control parameters to optimize the system response. Integrate the above analysis and verification results to form a final optimized collaborative control strategy to support the precise control and efficient operation of the robotic arm.

[0142] Step S74: synchronously control the robot arm servo motor and sensor system according to the optimized collaborative control strategy, and monitor the operating status of each system in real time to obtain multi-system synchronous control data.

[0143] The embodiment of the present invention uploads the optimized collaborative control strategy to the control system of the manipulator through the cloud platform. The optimized collaborative control strategy is loaded in the manipulator control system to ensure that all parameters and algorithms are configured correctly. The control system is initialized, including starting the self-test program to ensure that each component is working properly. According to the optimization strategy, the servo motor is started to achieve the predetermined motion trajectory and speed. The data fed back by the sensor, such as position, speed, torque and load information, is monitored in real time. The operating status of each system is continuously monitored through the cloud platform, including the motor operation status and the validity of the sensor data. According to the real-time monitoring data, the control strategy is dynamically adjusted as necessary to ensure the stability and accuracy of the manipulator under different loads and working conditions. All data in the synchronous control process, including the response time, load change and sensor feedback of the servo motor, are recorded for subsequent analysis. The operation data is regularly analyzed to evaluate the effectiveness of the control strategy and identify potential problems or optimization space. The real-time operation data and the control strategy execution results are integrated to generate multi-system synchronous control data to provide a basis for subsequent system optimization and fault diagnosis. A feedback mechanism is established to ensure that the cloud platform can update and optimize the collaborative control strategy according to the operation status data to adapt to future tasks and environmental changes.

[0144] The present invention integrates real-time load information, path optimization data and other compensation data through a cloud platform, so that the robot control system can obtain comprehensive optimization control data and improve decision-making efficiency. Genetic algorithms are used to analyze and optimize control data to generate highly adaptable intelligent optimization schemes, thereby making the control strategy more accurate and efficient. The collaborative control strategy formulated according to the intelligent optimization scheme ensures that the servo motor and sensor system can achieve effective coordination and improve the working ability of the robot in a complex environment. By monitoring the operating status of each system in real time, potential problems can be identified and solved in a timely manner to ensure the stability and reliability of the robot under dynamic loads and different task conditions. The generated multi-system synchronous control data provides strong support for subsequent optimization and improvement, and further improves the overall performance of the robot. The intelligence level and operating accuracy of the robot are significantly improved, and its application capabilities in industrial automation are enhanced.

[0145] Preferably, the present invention further provides a robotic arm for executing the robotic arm control method as described above, the robotic arm comprising:

[0146] The real-time load and inertia monitoring module is used to obtain the real-time load information of each joint of the robot arm during operation, and to monitor the acceleration and rotational inertia of the robot arm in real time to generate real-time inertial force data; the required torque compensation amount of the robot arm is calculated based on the real-time inertial force data to generate torque compensation control data;

[0147] The torque compensation and path optimization module is used to dynamically analyze the torque compensation control data, and adjust the output torque of the servo motor in real time based on the current load and inertia changes to generate inertia compensation data; dynamically optimize the motion path and motion speed of the robot arm according to the inertia compensation data to obtain path optimization data;

[0148] The load prediction and torque optimization module is used to predict the inertia force and torque requirements of the robot under various load conditions and generate torque compensation optimization instructions; based on the path optimization data, the output torque of the servo motor is adjusted using the torque compensation optimization instructions to obtain torque optimization adjustment data;

[0149] The multi-joint collaborative optimization module is used to synchronously optimize the torque compensation of each joint of the robot arm based on the path optimization data and the torque optimization adjustment data, and generate multi-joint collaborative control data; according to the multi-joint collaborative control data, the collaborative motion state between each joint is adjusted to obtain joint synchronous control data;

[0150] The dynamic tuning and comprehensive compensation module is used to dynamically tune the inertia force compensation and torque compensation based on the joint synchronization control data and real-time inertia force data, and generate tuning control instructions; according to the tuning control instructions, the torque compensation and inertia compensation of the robot arm are comprehensively optimized to obtain the torque and inertia comprehensive compensation data;

[0151] The remote monitoring and long-term optimization module is used to remotely monitor the operation status of the robot arm based on the cloud platform, and generate a long-term operation optimization model using the torque and inertia comprehensive compensation data; the long-term operation optimization model is used to dynamically optimize the torque compensation and inertia compensation algorithms of the robot arm to generate operation mode optimization data;

[0152] The intelligent optimization and system collaborative control module is used to optimize data according to the operating mode and generate an intelligent optimization plan for the robotic arm using the cloud platform; based on the intelligent optimization plan, the servo motor and sensor system of the robotic arm are collaboratively controlled to obtain multi-system synchronous control data.

[0153] The present invention effectively obtains the real-time load and inertia data of each joint of the robotic arm, improves the accuracy of the torque compensation demand, and ensures the stability during operation. The output torque of the servo motor is dynamically adjusted, the motion path and speed are optimized, and the adaptability and efficiency of the robotic arm under different load and inertia conditions are improved. By accurately predicting the torque demand under various load conditions, the generated optimization instructions ensure the rationality of the servo motor output and enhance the operational flexibility of the robotic arm. The synchronous optimization of the torque of each joint is achieved, the coordination of the robotic arm is improved, and the efficiency and stability in complex operations are ensured. The torque and inertia compensation are dynamically tuned by real-time data, the adaptive ability of the system is enhanced, and the reliability of the overall performance is improved. Remote monitoring of the robotic arm is realized, and a long-term optimization model is generated by using a cloud platform, which improves the predictability and long-term stability of the system operation. The generated intelligent optimization scheme ensures the coordinated work of the servo motor and the sensor system, and improves the overall coordination ability and operating efficiency of the robotic arm. The intelligence, flexibility and reliability of the robotic arm are significantly improved, and its application ability in complex environments is enhanced.

[0154] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0155] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for controlling a robotic arm, characterized in that: The following steps are involved: Step S1: obtaining the real-time load information of each joint of the robot arm during operation, and performing real-time monitoring of the acceleration and rotational inertia of the robot arm to generate real-time inertial force data; calculating the torque compensation required by the robot arm according to the real-time inertial force data to generate torque compensation control data; Step S2: dynamically analyzing the torque compensation control data, and adjusting the output torque of the servo motor in real time based on the current load and inertia changes to generate inertia compensation data; dynamically optimizing the motion path and motion speed of the robot arm according to the inertia compensation data to obtain path optimization data; Step S3: predicting the inertial force and torque requirements of the robot arm under various load conditions, and generating torque compensation optimization instructions; Based on the path optimization data, the output torque of the servo motor is adjusted using the torque compensation optimization instruction to obtain the torque optimization adjustment data; Step S4: Based on the path optimization data and the torque optimization adjustment data, the torque compensation of each joint of the robot arm is synchronously optimized to generate multi-joint collaborative control data; the collaborative motion state between the joints is adjusted according to the multi-joint collaborative control data to obtain joint synchronous control data; Step S5: dynamically tune the inertia force compensation and torque compensation based on the joint synchronization control data and the real-time inertia force data, and generate a tuning control instruction; comprehensively optimize the torque compensation and inertia compensation of the robot arm according to the tuning control instruction, and obtain torque and inertia comprehensive compensation data; Step S6: remotely monitor the operation status of the robot arm based on the cloud platform, and generate a long-term operation optimization model using the torque and inertia comprehensive compensation data; dynamically optimize the torque compensation and inertia compensation algorithms of the robot arm using the long-term operation optimization model to generate operation mode optimization data; Step S7: Optimizing data according to the operation mode, and using the cloud platform to generate an intelligent optimization solution for the robotic arm; Based on the intelligent optimization scheme, the servo motor and sensor system of the robot arm are coordinated and controlled to obtain multi-system synchronous control data.

2. The control method of the robot arm according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: monitoring the joint load information of the robot arm during operation in real time through load sensors installed at each joint, and generating real-time load data of each joint; Step S12: Based on the acceleration sensors and inertial measurement units installed at each joint, the acceleration, angular velocity and rotational inertia of the robot arm are monitored in real time to generate inertial measurement data; Step S13: Comprehensively analyzing the real-time load data and the inertial measurement data, and calculating the inertial force of each joint based on the load condition of the robotic arm to generate real-time inertial force data; Step S14: Calculate the torque compensation required for each joint of the robot arm using a dynamics model according to the real-time inertial force data and the real-time load data to generate real-time torque demand data; Step S15: Control the output torque of the servo motor in real time according to the real-time torque demand data to obtain torque compensation control data.

3. The control method of the robot arm according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Based on the torque compensation control data, a dynamic analysis algorithm is used to evaluate the torque demand of each joint to obtain dynamic torque analysis data; Step S22: monitor the output torque and load condition of the servo motor in real time through the torque sensor on the servo motor to generate servo motor output monitoring data; Step S23: performing inertia change analysis on the torque demand influencing factor based on the dynamic torque analysis data and the servo motor output monitoring data to generate inertia compensation data; Step S24: dynamically adjust the motion path and speed of the robot arm using the inertia compensation data to generate path optimization data.

4. The control method of the robot arm according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: monitoring the inertial force data of the robot arm under various load conditions in real time through a sensor system to obtain inertial force data; Step S32: Based on the motion characteristics of the robot arm, a prediction model of the robot arm is constructed using the inertial force data, and the prediction model is used to predict the torque demand under each load condition to generate torque demand prediction data; Step S33: Optimizing and analyzing the torque demand prediction data to generate torque compensation optimization instructions; Step S34: Integrate the path optimization data with the torque compensation optimization instruction, identify the optimal output torque of the servo motor, and generate torque optimization adjustment data.

5. The control method of the robot arm according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing demand analysis on the torque demand and motion state of each joint based on the path optimization data and the torque optimization adjustment data to obtain joint torque demand data; Step S42: synchronously optimizing the torque compensation of each joint of the robot arm according to the joint torque demand data to generate multi-joint collaborative control data; Step S43: adjusting the coordinated motion state between the joints in real time according to the multi-joint coordinated control data, and recording the adjusted joint motion state to generate joint synchronous control data.

6. The control method of the robot arm according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: Analyze the real-time data, identify the inertia force and torque compensation requirements of each joint, and obtain the expected torque compensation data; Step S52: analyzing the expected torque compensation data according to the joint synchronization control data to generate a tuning control instruction; Step S53: According to the tuning control instruction, the torque compensation and inertia compensation of the robot arm are comprehensively optimized, and the motion state and performance of each joint are monitored in real time, the compensation effect of the optimized torque and inertia is evaluated, and the torque and inertia comprehensive compensation data is obtained.

7. The control method of the robot arm according to claim 6, characterized in that: Step S53 includes the following steps: Step S531: performing comprehensive optimization on the torque and inertia compensation of each joint of the robot arm according to the tuning control instruction, and collecting the motion parameters of each joint in real time through the sensor system to obtain optimized motion parameter data; Step S532: Compare and analyze the optimized motion parameter data with the expected torque compensation value to obtain expected deviation data; Step S533: Based on the expected deviation data, the torque response of each joint when the load changes and the change of inertia force before and after compensation are evaluated and analyzed to obtain the torque and inertia comprehensive compensation data.

8. The control method of the robot arm according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: obtaining real-time operation status data of the robot arm through the cloud platform, and performing performance evaluation and analysis on the real-time operation status data, identifying abnormal situations, and obtaining performance abnormality data; Step S62: Analyze the root causes of the abnormal performance data and build a long-term operation optimization model; Step S63: dynamically optimizing the torque compensation and inertia compensation of the robot arm using the long-term operation optimization model to generate optimized compensation data; Step S64: Push the optimization compensation data to the robot control system through the cloud platform to generate operation mode optimization data.

9. The control method of the robot arm according to claim 1, characterized in that: Step S7 includes the following steps: Step S71: using the cloud platform to pre-process and integrate the real-time load information, path optimization data, torque and inertia comprehensive compensation data, and operation mode optimization data to obtain optimized control data of the robotic arm; Step S72: Analyze the optimization control data using a genetic algorithm to generate an intelligent optimization solution for the robot arm; Step S73: formulating a coordinated control strategy for the servo motor and the sensor system of the robot arm according to the intelligent optimization scheme to obtain an optimized coordinated control strategy; Step S74: synchronously control the robot arm servo motor and sensor system according to the optimized collaborative control strategy, and monitor the operating status of each system in real time to obtain multi-system synchronous control data.

10. A robotic arm, characterized in that: The robot comprises a joint and a support structure, a servo motor, an end effector and a control system, wherein the servo motor is deployed on the joint and the support structure, the end effector is connected to the joint and the support structure, the control system is installed in the servo motor, and the control system is used to execute the control method of the robot arm according to claim 1, and the control system comprises: The real-time load and inertia monitoring module is used to obtain the real-time load information of each joint of the robot arm during operation, and to monitor the acceleration and rotational inertia of the robot arm in real time to generate real-time inertial force data; the required torque compensation amount of the robot arm is calculated based on the real-time inertial force data to generate torque compensation control data; The torque compensation and path optimization module is used to dynamically analyze the torque compensation control data, and adjust the output torque of the servo motor in real time based on the current load and inertia changes to generate inertia compensation data; dynamically optimize the motion path and motion speed of the robot arm according to the inertia compensation data to obtain path optimization data; The load prediction and torque optimization module is used to predict the inertia force and torque requirements of the robot under various load conditions and generate torque compensation optimization instructions; based on the path optimization data, the output torque of the servo motor is adjusted using the torque compensation optimization instructions to obtain torque optimization adjustment data; The multi-joint collaborative optimization module is used to synchronously optimize the torque compensation of each joint of the robot arm based on the path optimization data and the torque optimization adjustment data, and generate multi-joint collaborative control data; according to the multi-joint collaborative control data, the collaborative motion state between each joint is adjusted to obtain joint synchronous control data; The dynamic tuning and comprehensive compensation module is used to dynamically tune the inertia force compensation and torque compensation based on the joint synchronization control data and real-time inertia force data, and generate tuning control instructions; according to the tuning control instructions, the torque compensation and inertia compensation of the robot arm are comprehensively optimized to obtain the torque and inertia comprehensive compensation data; The remote monitoring and long-term optimization module is used to remotely monitor the operation status of the robot arm based on the cloud platform, and generate a long-term operation optimization model using the torque and inertia comprehensive compensation data; the long-term operation optimization model is used to dynamically optimize the torque compensation and inertia compensation algorithms of the robot arm to generate operation mode optimization data; The intelligent optimization and system collaborative control module is used to optimize data according to the operating mode and generate an intelligent optimization plan for the robotic arm using the cloud platform; based on the intelligent optimization plan, the servo motor and sensor system of the robotic arm are collaboratively controlled to obtain multi-system synchronous control data.

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