Method and System for Controlling a Robot Arm Monitored by a Sensor Made of Barium Titanate

Abstract

The present invention discloses a method and system for controlling a robotic arm based on monitoring by a barium titanate-based sensor, which relates to the technical field of intelligent control. The method includes installing a barium titanate intelligent sensor on the robotic arm to collect the operating data of the robotic arm and obtaining the information of the item grasped by the robotic arm; guiding the grasping of the item through acoustic positioning and obtaining local point cloud data, and adjusting the grasping force of the robotic arm according to the local point cloud data and the item information; remotely monitoring the faults of the robotic arm according to the operating data of the robotic arm and performing maintenance on the robotic arm; and recording the operating state of the robotic arm and the grasping process in real time for display and storage. The present invention calculates the target grasping force of the robotic arm on the item by obtaining the local point cloud data, and optimizes the grasping force of the robotic arm in real time through an algorithm to grasp the item, effectively improving the stability and accuracy of the robotic arm grasping. At the same time, by constructing a support vector machine model for monitoring and maintaining the operating faults of the robotic arm, the safety of the robotic arm operation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and particularly to a control method and system for a robotic arm monitored by a sensor made of barium titanate. Background Art

[0002] With the continuous development of industrial automation technology, the robotic arm, as a core device in automated production, plays an indispensable role in various fields such as manufacturing, logistics, and healthcare. Traditional robotic arm systems usually rely on standard sensors and control algorithms to complete precise operation tasks. However, with the continuous evolution of technology, higher requirements are put forward for the control accuracy, stability, and real-time performance of robotic arms. For this reason, intelligent sensor technology has gradually been applied to the monitoring and control of robotic arms, especially intelligent sensors that can provide real-time feedback on environmental and operation information, such as pressure, position, speed, and strain sensors. However, there are still defects in the existing technology. Traditional robotic arm systems lack sufficient intelligence and adaptability, and it is difficult to achieve efficient control in real-time adjustment of grasping force. This limitation is likely to lead to grasping failures or damage to items due to excessive grasping force. Summary of the Invention

[0003] In view of the problems existing in the above-mentioned existing control method and system for a robotic arm monitored by a sensor made of barium titanate, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that traditional robotic arm systems lack sufficient intelligence and adaptability, and it is difficult to achieve efficient control in real-time adjustment of grasping force. This limitation is likely to lead to grasping failures or damage to items due to excessive grasping force.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: A control method for a robotic arm monitored by a sensor made of barium titanate, which includes installing a barium titanate intelligent sensor on the robotic arm to collect the operation data of the robotic arm and obtain the information of the item grasped by the robotic arm; guiding the grasping of the item through acoustic positioning and obtaining local point cloud data, and adjusting the grasping force of the robotic arm according to the local point cloud data and the item information; remotely monitoring the faults of the robotic arm based on the operation data of the robotic arm and performing maintenance on the robotic arm; and recording the operation state of the robotic arm and the grasping process in real-time for display and storage.

[0006] As a preferred solution of the robotic arm control method based on barium titanate sensor monitoring described in the present invention, wherein: the barium titanate intelligent sensor is installed on the robotic arm to collect the robotic arm operation data and obtain the information of the robotic arm grasping the object, which means deploying barium titanate intelligent sensors at the joints and end fingers of the robotic arm to collect the robotic arm operation data and the grasping force data of the end fingers, connecting all barium titanate intelligent sensors to form a Zigbee sensor network to the intelligent control end and transmitting the robotic arm operation data and grasping force data in real time, and the intelligent control end obtains the basic information of the grasped object through pre-input.

[0007] As a preferred solution of the robotic arm control method based on sensor monitoring made of barium titanate described in the present invention, the method of positioning and grasping objects through acoustic guidance and obtaining local point cloud data refers to installing an ultrasonic sensor on the robotic arm to determine the distance d between the robotic arm and the grasped object through ultrasound, setting the distance d as the local point cloud capture area range of the robotic arm, using laser radar to generate three-dimensional point cloud data of the grasped object, and screening the three-dimensional point cloud data according to the local point cloud capture area range to extract local point cloud data.

[0008] As a preferred solution of the robot arm control method based on barium titanate sensor monitoring of the present invention, wherein: the adjustment of the robot arm grasping force according to the local point cloud data and the object information includes:

[0009] Estimate the initial grasping force F based on the weight of the grasped object in ;

[0010] F in =m*g*α;

[0011] Where m is the weight of the grasped object, g is the acceleration of gravity, and α is the grasping safety factor;

[0012] For each point q in the local point cloud data i Perform K-nearest neighbor search to find the neighborhood point set, use the least squares method to fit the neighborhood plane of each point, and synthesize the normal vector N i ;

[0013] According to the initial gripping force F in and the normal vector N i Calculate point q i The normal force F n :

[0014] F n =F in *N i ;

[0015] Obtain the friction coefficient by grabbing the object information to calculate the friction force F of the normal force f , the friction force F fand the normal force F n Adding them together gives the total grasping force F i at point q t (q i );

[0016] Extract the surface points of the grasped object in the point cloud data to form the XOY plane and the XOZ plane, and extract the points in the XOY plane and the XOZ plane of the local point cloud data to form point sets respectively. Calculate the force balance of the XOY plane and the XOZ plane respectively:

[0017]

[0018] where F XOY is the balancing force of the XOY plane, F XOZ is the balancing force of the XOZ plane, M is the point set of the XOY plane, q j is the j-th point in the point set M, m is the total number of points in the point set M, P is the point set of the XOZ plane, q k is the k-th point in the point set P, and p is the total number of points in the point set P;

[0019] Compare the balancing forces of the XOY plane and the XOZ plane, and select the minimum value as the target grasping force F to ;

[0020] Guide the robotic arm to perform object positioning and grasping through an ultrasonic sensor, and obtain the grasping force F i at the end finger of the robotic arm in real time. Define the residual r i as the difference between the target grasping force F to and the grasping force F i . Define the optimization objective f(x) as:

[0021]

[0022] where n is the number of end fingers of the robotic arm;

[0023] Calculate the adjustment value of the grasping force for the current iteration

[0024]

[0025] where J r is the Jacobian matrix, is the pseudo-inverse of the Jacobian matrix;

[0026] Based on the adjustment value of the grasping force Update the grasping force of the end finger of the robotic arm:

[0027]

[0028] where is the grasping force for the h-th iteration, and β is the step factor, is the grasping force updated iteratively;

[0029] The grasping force of the fingers at the end of the robotic arm is updated iteratively by the Gauss-Newton method until the function value of the optimization objective f(x) is less than the set iteration threshold, and then the iteration stops. The grasping force of the robotic arm is controlled and adjusted in real time according to the grasping force of each finger at the end of the robotic arm.

[0030] As a preferred solution of the robotic arm control method based on the sensor made of barium titanate according to the present invention, wherein: the remotely monitoring the faults of the robotic arm based on the operation data of the robotic arm means that after obtaining the operation data of the robotic arm, noise reduction processing is performed by a Kalman filter, and the operation data features are extracted through feature engineering;

[0031] A support vector machine model is constructed, and the support vector machine model is trained using a training set, and the model parameters are optimized;

[0032] The extracted operation data features are input into the support vector machine model to obtain the robotic arm fault monitoring result.

[0033] As a preferred solution of the robotic arm control method and system based on the sensor made of barium titanate according to the present invention, wherein: the performing the maintenance of the robotic arm means that after obtaining the robotic arm fault monitoring result, the robotic arm is maintained. If the fault monitoring result is normal operation, the operation monitoring of the robotic arm is maintained, and regular maintenance of the robotic arm is arranged. If the fault monitoring result is a fault, the operation of the robotic arm is stopped, and the operation data of the robotic arm collected in real time is sent to the staff to judge the cause and location of the fault, and the fault repair content is formulated to repair the robotic arm.

[0034] As a preferred solution of the robotic arm control method based on the sensor made of barium titanate according to the present invention, wherein: the real-time recording of the operation state and the grasping process of the robotic arm for display and storage means that the operation state and the grasping process of the robotic arm are recorded in real time to generate an operation log, and the robotic arm maintenance and repair content are synchronously generated into a robotic arm maintenance log and stored in a database. The database regularly performs security detection on the operation log and the maintenance log, and uploads them to the cloud for backup.

[0035] Another object of the present invention is to provide a robotic arm control system based on the sensor made of barium titanate, which includes a data acquisition module for acquiring the operation data of the robotic arm and the information of the grasped object, and acquiring the grasping force data of the fingers at the end of the robotic arm during grasping;

[0036] A grasping control module for calculating the initial grasping force, acquiring local point cloud data, and adjusting the grasping force of the robotic arm according to the local point cloud data and the object information;

[0037] An operation monitoring module, which is used to analyze and monitor the operation status of the robotic arm according to the collected operation data of the robotic arm, and perform maintenance on the robotic arm;

[0038] A log recording module, which is used to generate an operation log and a maintenance log and store them in a database.

[0039] A computer device, comprising: a memory and a processor; the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned robotic arm control method based on sensor monitoring made of barium titanate are implemented.

[0040] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned robotic arm control method based on sensor monitoring made of barium titanate are implemented.

[0041] The beneficial effects of the present invention are as follows: by positioning and grasping an item, obtaining local point cloud data to calculate the target grasping force of the robotic arm on the item, and optimizing the grasping force of the robotic arm in real time through an algorithm for item grasping, the stability and accuracy of the robotic arm grasping are effectively improved. At the same time, by constructing a support vector machine model for robotic arm operation fault monitoring and maintenance, the safety of the robotic arm operation is improved. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 It is a schematic flowchart of a robotic arm control method based on sensor monitoring made of barium titanate.

[0044] Figure 2 It is a schematic structural diagram of a robotic arm control system based on sensor monitoring made of barium titanate. Detailed Embodiments

[0045] In order to make the above-mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed embodiments of the present invention in detail with reference to the drawings of the specification.

[0046] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0047] Second, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or selectively exclusive embodiments from other embodiments.

[0048] Embodiment 1

[0049] Referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a method for controlling a robotic arm based on the monitoring of a sensor made of barium titanate. The method for controlling a robotic arm based on the monitoring of a sensor made of barium titanate includes

[0050] S1. Install a barium titanate intelligent sensor on the robotic arm to collect the operation data of the robotic arm and obtain the information of the item grasped by the robotic arm;

[0051] Specifically, installing a barium titanate intelligent sensor on the robotic arm to collect the operation data of the robotic arm and obtain the information of the item grasped by the robotic arm means deploying barium titanate intelligent sensors at the joints and the end fingers of the robotic arm to collect the operation data of the robotic arm and the grasping force data of the end fingers. The barium titanate intelligent sensor is made by 3D printing. The piezoelectric property of barium titanate enables it to generate electrical signals under mechanical pressure, thereby monitoring the motion state of the robotic arm in real time. All barium titanate intelligent sensors form a Zigbee sensor network and are connected to the intelligent control terminal to transmit the operation data and grasping force data of the robotic arm in real time. The intelligent control terminal obtains the basic information of the grasped item through pre-input, including size, weight, material, shape, etc.

[0052] By installing barium titanate smart sensors on the joints and end fingers of the robotic arm, the piezoelectric effect of the sensors enables them to sense and monitor mechanical pressure and strain in real time during the operation of the robotic arm. This real-time monitoring can not only capture the motion state of the robotic arm but also accurately track the grasping force exerted by the robotic arm's fingers on the object surface. Through these high-precision data, the system can achieve precise control of the robotic arm's operation, avoiding object damage or grasping failure caused by improper grasping force. The Zigbee network features low power consumption and high stability, making it particularly suitable for sensor systems that require long-term continuous operation. Through the Zigbee protocol, all barium titanate smart sensors on the robotic arm are interconnected to form a complete wireless sensor network, ensuring that the data collected by each sensor can be transmitted to the intelligent control terminal in real time. The low-latency characteristic of the Zigbee network in data transmission guarantees the real-time nature of the robotic arm's operation data and grasping force data, providing a solid foundation for the efficient operation of the robotic arm and ensuring that it can make precise adjustments and operations in a short time. The intelligent control terminal is the "brain" of the entire system. By receiving the data transmitted by the sensor network, this control terminal can monitor the operation state of the robotic arm in real time and dynamically adjust the operation strategy of the robotic arm based on the preset item information (such as size, weight, material, and shape). The acquisition and input of item information enable the system to formulate adaptive grasping plans for different types of objects. For example, for fragile items, the intelligent control terminal can reduce the grasping force; while for heavier items, it can improve the grasping stability.

[0053] S2. Grasp the item through acoustic guidance positioning, obtain local point cloud data, and adjust the grasping force of the robotic arm according to the local point cloud data and item information;

[0054] Specifically, grasping the item through acoustic guidance positioning and obtaining local point cloud data means installing ultrasonic sensors on the robotic arm to determine the distance d between the robotic arm and the grasped item through ultrasonic waves, setting the distance d as the range of the local point cloud capture area of the robotic arm, using a lidar to generate three-dimensional point cloud data of the grasped object, and screening the three-dimensional point cloud data according to the local point cloud capture area range to extract the local point cloud data.

[0055] Ultrasonic sensors can provide fast and accurate distance information and are not affected by changes in ambient light. They are suitable for grasping operations in complex industrial scenarios. By accurately determining the relative position of an object through ultrasonic waves, a more precise grasping strategy can be set for the robotic arm, ensuring the correctness of the position and angle when grasping the object and reducing errors. Based on the distance obtained by the ultrasonic sensor, the system can dynamically set a local point cloud capture area, which represents the spatial range where the robotic arm's fingers may contact the object, ensuring that only the point cloud data related to the grasping operation is captured. By setting the local point cloud capture area, the processing volume of the large amount of point cloud data generated by the lidar can be effectively reduced, and only the data related to the grasping operation is retained. The setting of the local point cloud capture area makes the subsequent data processing more efficient, reduces the unnecessary computational burden, and ensures the high correlation and accuracy of the data during the grasping process. Compared with traditional two-dimensional images, three-dimensional point cloud data provides the spatial structure information of the object, enabling the robotic arm to more accurately identify the size, shape, and grasping points of the object. By combining the three-dimensional point cloud data, the robotic arm can identify the grasping methods of objects with complex shapes and dynamically adjust the grasping actions according to the geometric characteristics of the object. In addition, the high-precision measurement of the lidar enables the robotic arm to maintain a high grasping success rate in narrow spaces or environments with many obstacles. The screening of the local point cloud data greatly reduces the computational amount, enabling the system to process the grasping task faster and more accurately. The selected local point cloud data is highly concentrated in the operation area of the robotic arm, which ensures the accuracy of the subsequent grasping force calculation and path planning. By only processing the local point cloud data, the response speed of the system is improved, thereby enhancing the operation efficiency and real-time performance of the robotic arm. The optimization of the grasping strategy based on the point cloud data enables the system to handle diverse object grasping requirements. Whether it is an object with a regular shape or an irregular object, the system can formulate an accurate grasping strategy through a detailed analysis of the point cloud data, greatly enhancing the operation flexibility of the robotic arm. In addition, the dynamic adjustment of the grasping force avoids object damage caused by excessive grasping force or object dropping caused by insufficient grasping force.

[0056] Furthermore, adjusting the grasping force of the robotic arm according to the local point cloud data and item information includes

[0057] Estimating the initial grasping force F according to the weight of the grasped item in ;

[0058] F in = m * g * α;

[0059] where m is the weight of the grasped item, g is the acceleration due to gravity, and α is the grasping safety factor, which is used to prevent grasping failure and usually takes a value of 1.2 - 1.5;

[0060] For each point q in the local point cloud data iPerform K-nearest neighbor search to find the neighborhood point set, use the least squares method to fit the neighborhood plane of each point, and comprehensively obtain the normal vector N i ;

[0061] ax + by + c + d = 0;

[0062] where (x, y, z) are the point coordinates, a, b, and c are the normal vector components of the fitted neighborhood plane, and the normal vector N is comprehensively obtained through the normal vector components i ;

[0063] According to the preliminary grasping force F in and the normal vector N i calculate the normal force F i of point q n :

[0064] F n = F in * N i ;

[0065] The preliminary grasping force F in is a vector with direction and magnitude. The normal vector N i describes the geometric direction feature of a certain point on the object surface. In order to obtain the normal force applied at a certain point, the component of the force in the direction of the normal vector is obtained through vector motor operation, that is, the normal force;

[0066] Obtain the frictional force F f of the normal force by calculating the friction coefficient through the grasped item information, add the frictional force F f and the normal force F n to get the total grasping force F i of point q t (q i );

[0067] Extract the points on the grasped item surface in the point cloud data to form the XOY plane and the XOZ plane, and extract the points of the XOY plane and the XOZ plane in the local point cloud data to form point sets respectively, and calculate the force balance of the XOY plane and the XOZ plane respectively:

[0068]

[0069]

[0070] where F XOY is the balancing force of the XOY plane, F XOZ is the balancing force of the XOZ plane, M is the point set of the XOY plane, q j is the j-th point in the point set M, m is the total number of points in the point set M, P is the point set of the XOZ plane, q k is the k-th point in the point set P, and p is the total number of points in the point set P;

[0071] Compare the balancing forces in the XOY plane and the XOZ plane, and select the minimum value as the target grasping force F to ;

[0072] Use the ultrasonic sensor to guide the robotic arm to locate and grasp the object, and obtain the grasping force F of the fingers at the end of the robotic arm in real time i , define the residual r i as the target grasping force F to and the grasping force F i The difference between them, define the optimization objective f(x) as:

[0073]

[0074] where n is the number of fingers at the end of the robotic arm;

[0075] Calculate the adjustment value of the grasping force for the current iteration

[0076]

[0077] where J r is the Jacobian matrix, and the Jacobian matrix represents the first-order derivative of the residual r i with respect to the grasping force F i , indicating the relationship between the adjustment of the grasping force and the change of the residual, is the pseudoinverse of the Jacobian matrix;

[0078] Based on the adjustment value of the grasping force Update the grasping force of the fingers at the end of the robotic arm:

[0079]

[0080] where is the grasping force at the h-th iteration, β is the step factor, is the grasping force updated by iteration;

[0081] Iteratively update the grasping force of the fingers at the end of the robotic arm by the Gauss-Newton method until the function value of the optimization objective f(x) is less than the set iteration threshold, and then stop the iteration. Control and adjust the grasping force of the robotic arm in real time according to the grasping force of each finger at the end of the robotic arm

[0082] By integrating item information, the system can dynamically adjust the initial grasping force to ensure that even in the case of uneven object mass, the robotic arm can provide sufficient grasping force. At the same time, the setting of the safety factor adds fault tolerance to the grasping process, effectively avoiding grasping failures or object damage. By performing K-nearest neighbor search on each point, the system can more accurately describe the local geometric features of the object surface. The calculation of the normal vector provides a reference for the direction of the grasping force, enabling the grasping force to be applied along the normal direction of the object surface to ensure that the object surface will not be damaged due to excessive grasping force. Through the calculation of the normal force and friction force, the system can accurately adjust the magnitude and direction of the grasping force according to the geometric and physical properties of the object surface to ensure that the grasping operation is both stable and will not damage the object. The precise control of the grasping force improves the success rate of the robotic arm when grasping items of various shapes and materials. Through force balance calculation, the system ensures that the grasping force of the robotic arm remains stable on different planes, preventing grasping failures caused by uneven force distribution. Selecting the minimum value as the target grasping force can minimize the excessive force exerted by the robotic arm during the grasping process, further enhancing the safety and accuracy of grasping. Through the iterative optimization of the Gauss-Newton method, the system can dynamically adjust the grasping force to ensure that the actual grasping force is closer to the target grasping force after each iteration. This process reduces the need for human intervention through automated force adjustment, improving the automation level and precision of the robotic arm operation.

[0083] S3. Remotely monitor the faults of the robotic arm based on the operation data of the robotic arm and perform maintenance on the robotic arm;

[0084] Specifically, remotely monitoring the faults of the robotic arm based on the operation data of the robotic arm means that after obtaining the operation data of the robotic arm, noise reduction processing is performed through a Kalman filter, and the operation data features are extracted through feature engineering;

[0085] Construct a support vector machine model, use the training set to train the support vector machine model, and optimize the model parameters;

[0086] Input the extracted operation data features into the support vector machine model to obtain the robotic arm fault monitoring results.

[0087] Through Kalman filtering, the system can remove random noise in the operating data of the robotic arm, making the sensor data more reliable and stable. This provides high-quality data input for subsequent feature extraction and fault monitoring, ensuring the accuracy of the fault monitoring results. Through feature engineering, the system can accurately extract important information during the operation of the robotic arm and provide high-quality input features for the fault monitoring model. Feature engineering can not only improve the prediction accuracy of the model, but also reduce the redundancy of the original data, lower the computational cost, and enhance the real-time monitoring ability of the system. Support vector machines can construct the optimal classification hyperplane in a high-dimensional space, ensuring the accuracy and robustness of the robotic arm fault monitoring results. Through parameter optimization, the system can achieve higher classification accuracy between the fault and non-fault states and reduce the risk of misclassification. This makes the system more efficient in detecting potential faults of the robotic arm at an early stage and avoiding economic losses caused by equipment damage or downtime.

[0088] Furthermore, robotic arm maintenance refers to performing robotic arm maintenance after obtaining the robotic arm fault monitoring results. If the fault monitoring result is normal operation, continue to monitor the operation of the robotic arm and schedule regular robotic arm maintenance. If the fault monitoring result indicates a fault, stop the robotic arm, send the real-time collected robotic arm operation data to the staff to determine the cause and location of the fault, and formulate the fault repair content for robotic arm repair.

[0089] By incorporating regular maintenance during normal operation into a systematic management system, it is possible to effectively prevent unexpected failures of the robotic arm due to excessive wear or fatigue. Regular maintenance can promptly detect and resolve potential problems, avoiding small issues from gradually accumulating into major failures. At the same time, it enhances the service life and stability of the robotic arm. The combination of automated monitoring and regular maintenance reduces the reliance on manual inspections, improving the operating efficiency and reliability of the equipment. When a fault is detected, shutting down the machine in a timely manner not only protects the robotic arm from further damage but also prevents other chain failures or accidents from occurring on the automated production line. This protection mechanism significantly enhances the safety of the system, reducing downtime and repair time caused by equipment failures. The shutdown protection function also ensures that maintenance personnel can safely access the equipment for fault analysis and repair. Real-time data collection enables technicians to quickly grasp the detailed situation at the time of the fault, thus more accurately determining the cause and location of the fault. With the support of real-time data, the diagnostic time is significantly shortened, and technicians can rapidly formulate repair plans, reducing equipment downtime. In addition, detailed operation data can help technicians better prevent the occurrence of similar future faults, providing data support for continuously optimizing the maintenance strategy. Through a systematic repair plan, maintenance work can be more efficient and targeted. The repair plan is formulated based on real-time collected data and fault diagnosis results, ensuring that each repair can precisely solve the problem and avoid repeated failures. The re-monitoring function after repair completion guarantees the stability of the system after repair, enabling the robotic arm to safely and stably resume operation, enhancing the reliability and sustainability of the system.

[0090] S4. Real-time record the operating status of the robotic arm and the grasping process for display and storage;

[0091] Specifically, real-time recording the operating status of the robotic arm and the grasping process for display and storage means generating an operation log by real-time recording the operating status and grasping process of the robotic arm, and synchronously generating a robotic arm maintenance log for the maintenance and repair content of the robotic arm and storing it in the database. The database regularly conducts security inspections on the operation log and maintenance log and uploads them to the cloud for backup.

[0092] The operation data of the robotic arm recorded in real time can provide accurate basis for subsequent fault analysis, performance optimization, and improvement of grasping strategies. Through the operation logs accumulated over a long period, the system can identify the operation modes and potential problems of the robotic arm, thereby optimizing the work process, reducing the mistakes and faults of the robotic arm. At the same time, the detailed records during the grasping process provide accurate feedback for automated operations in industrial scenarios, helping to improve the grasping success rate and overall operation efficiency. By generating and storing maintenance logs, the system can better manage the maintenance cycle of the robotic arm, ensuring that each maintenance is accurately recorded and archived. The long-term preservation of the maintenance logs helps to identify which components are prone to problems and optimize the maintenance cycle, thereby reducing the maintenance cost and increasing the service life of the robotic arm. Further, these logs can also provide data support for predictive maintenance, helping the system to take preventive measures before a fault occurs and enhancing the reliability of the equipment. Through regular database security detection, the system can effectively prevent data loss and external intrusion, ensuring the security and integrity of key operation and maintenance data, which is crucial for ensuring the long-term reliable operation of the robotic arm. Especially in high-precision applications such as industry and medical care, the security of data directly affects the use and decision-making of the equipment. Cloud backup provides double protection for the operation data and maintenance data of the robotic arm, avoiding data loss caused by uncontrollable factors such as hardware failures and accidental disasters. By backing up the data to the cloud, enterprises can access these logs anytime and anywhere for remote management and analysis, which provides a convenient solution for cross-regional equipment management and large-scale equipment monitoring, and also improves the flexibility and security of data management.

[0093] Embodiment 2

[0094] Refer to Figure 2 , which is the second embodiment of the present invention. This embodiment is different from the previous one and provides a robotic arm control system based on the monitoring of sensors made of barium titanate, which includes,

[0095] A data acquisition module, which is used to acquire the operation data of the robotic arm and the information of the grasped object, and acquire the grasping force data of the fingers at the end of the robotic arm during grasping;

[0096] A grasping control module, which is used to calculate the initial grasping force, acquire local point cloud data, and adjust the grasping force of the robotic arm according to the local point cloud data and the object information;

[0097] An operation monitoring module, which is used to analyze and monitor the operation state of the robotic arm according to the collected operation data of the robotic arm and perform maintenance on the robotic arm;

[0098] A log recording module, which is used to generate operation logs and maintenance logs and store them in the database.

[0099] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0100] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0101] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), optical fiber devices, and portable compact disc read-only memories (CDROMs). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways when necessary, and then storing it in a computer memory.

[0102] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the technical solutions of the present invention.

Claims

1. A control method for a robotic arm monitored by a sensor made of barium titanate, characterized in that: including, installing a barium titanate intelligent sensor on the robotic arm to collect the operating data of the robotic arm and obtain the information of the item grasped by the robotic arm; locating and grasping the item through acoustic guidance and obtaining local point cloud data, and adjusting the grasping force of the robotic arm according to the local point cloud data and the item information; remotely monitoring the faults of the robotic arm according to the operating data of the robotic arm and performing maintenance on the robotic arm; recording the operating state of the robotic arm and the grasping process in real time for display and storage; Estimate the initial grasping force based on the weight of the grasped object , for each point in the local point cloud data Perform K-nearest neighbor search to find the neighborhood point set, use the least squares method to fit the neighborhood plane of each point, and comprehensively obtain the normal vector , calculate the frictional force of the normal force through the grasped object information to obtain the friction coefficient , add the frictional force and the normal force to get the total grasping force of point ; ; extracting the surface points of the grasped item in the point cloud data to form the XOY plane and the XOZ plane, respectively calculating the force balance of the XOY plane and the XOZ plane, comparing the balanced forces of the XOY plane and the XOZ plane, and selecting the minimum value as the target grasping force; The calculation of the balanced forces of the XOY plane and the XOZ plane is as follows: ； ； where is the balance force in the XOY plane, is the balance force in the XOZ plane, is the point set in the XOY plane, is the point set the j-th point in, m is the total number of points in the point set the total number of points in, is the point set in the XOZ plane, is the k-th point in the point set P, is the total number of points in the point set P; obtaining the grasping force of the fingers at the end of the robotic arm in real time, defining an optimization target, calculating the adjustment value of the grasping force to iteratively update the grasping force of the fingers at the end of the robotic arm, and controlling and adjusting the grasping force of the robotic arm in real time according to the grasping force of each finger at the end of the robotic arm, including: Use an ultrasonic sensor to guide the robotic arm to locate and grasp an object, and obtain the grasping force of the fingers at the end of the robotic arm in real time , define the residual as the target grasping force and the grasping force difference, define the optimization objective as: ； where n is the number of fingers at the end of the robotic arm; Calculate the grasping force adjustment value for the current iteration : ； wherein is the Jacobian matrix, is the pseudo-inverse of the Jacobian matrix; Based on the grasping force adjustment value Update the grasping force of the fingers at the end of the robotic arm: ； where is the grasping force of the h-th iteration, is the step factor, is the grasping force updated by iteration; Iteratively update the grasping force of the end fingers of the robotic arm through the Gauss-Newton method until the function value of the optimization objective is less than the set iteration threshold, and then stop the iteration. Control and adjust the grasping force of the robotic arm in real time according to the grasping force of each end finger of the robotic arm.

2. The manipulator control method based on the sensor monitoring made of barium titanate as claimed in claim 1, wherein: The installation of the barium titanate intelligent sensor on the robotic arm to collect the operating data of the robotic arm and obtain the information of the item grasped by the robotic arm means deploying barium titanate intelligent sensors at the joints and fingers at the end of the robotic arm to collect the operating data of the robotic arm and the grasping force data of the fingers at the end, forming a Zigbee sensor network with all the barium titanate intelligent sensors and connecting it to the intelligent control terminal to transmit the operating data and grasping force data of the robotic arm in real time, and the intelligent control terminal obtains the basic information of the grasped item through pre-input.

3. The manipulator control method based on the sensor monitoring made of barium titanate according to claim 2, characterized in that: The locating and grasping the item through acoustic guidance and obtaining local point cloud data means installing an ultrasonic sensor on the robotic arm to determine the distance d between the robotic arm and the grasped item through ultrasonic waves, setting the distance d as the range of the local point cloud capture area of the robotic arm, generating three-dimensional point cloud data of the grasped object by using a lidar, and screening the three-dimensional point cloud data according to the range of the local point cloud capture area to extract the local point cloud data.

4. The manipulator control method based on the sensor monitoring made of barium titanate according to claim 3, wherein: The remote monitoring of the faults of the robotic arm according to the operating data of the robotic arm means denoising the operating data of the robotic arm through a Kalman filter after obtaining the operating data of the robotic arm, and extracting the characteristics of the operating data through feature engineering; constructing a support vector machine model, training the support vector machine model with a training set, and optimizing the model parameters; inputting the extracted operating data characteristics into the support vector machine model to obtain the fault monitoring result of the robotic arm.

5. The method for controlling a robotic arm monitored by a sensor made of barium titanate according to claim 4, characterized in that: The performance of maintenance on the robotic arm means performing maintenance on the robotic arm after obtaining the fault monitoring result of the robotic arm. If the fault monitoring result is normal operation, continue to monitor the operation of the robotic arm and arrange for regular maintenance of the robotic arm. If the fault monitoring result is a fault, stop the operation of the robotic arm, send the real-time collected operating data of the robotic arm to the staff to judge the cause and location of the fault, and formulate the content of fault repair for repairing the robotic arm.

6. The robotic arm control method based on the sensor monitoring made of barium titanate according to claim 5, characterized in that: The real-time recording of the operating state of the robotic arm and the grasping process is displayed and stored, which means generating an operation log by recording the operating state of the robotic arm and the grasping process in real time, and synchronously generating a robotic arm maintenance log for the robotic arm maintenance and repair content and storing it in the database. The database periodically performs security detection on the operation log and the maintenance log, and uploads them to the cloud for backup.

7. A robotic arm control system for a robotic arm control method based on monitoring by a sensor made of barium titanate as described in any one of claims 1-6, characterized in that: Including, a data acquisition module, configured to acquire the robotic arm operation data and the information of the grasped item, and acquire the grasping force data of the fingertips at the end of the robotic arm during grasping; a grasping control module, configured to calculate the initial grasping force, acquire local point cloud data, and adjust the grasping force of the robotic arm according to the local point cloud data and the item information; an operation monitoring module, configured to analyze and monitor the operating state of the robotic arm according to the collected robotic arm operation data, and perform robotic arm maintenance; a log recording module, configured to generate an operation log and a maintenance log and store them in the database.

8. A computer device, comprising: a memory and a processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the robotic arm control method based on the sensor monitoring made of barium titanate described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the robotic arm control method based on the sensor monitoring made of barium titanate described in any one of claims 1 to 6 are implemented.

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