Unhooking robot self-correction regulation and control method and device combined with follow-up analysis

Abstract

The invention discloses an unhooking robot self-rectification regulation and control method and device combined with follow-up analysis, and relates to the technical field of machine regulation and control, and the method comprises the steps: obtaining the position data, angle data, force feedback data and environment data of an unhooking robot in the operation process, and constructing a robot operation dynamic model; the operation deviation of the unhooking robot is analyzed in real time, the position deviation and the force feedback deviation of the robot in different operation steps are determined, and a deviation data set is generated; performing fuzzification processing on the deviation data set according to a preset deviation correction rule, matching the preset deviation correction rule and performing fuzzy reasoning calculation to generate an initial deviation correction scheme; performing correction strategy optimization based on the initial correction scheme to generate an optimized correction scheme; and controlling the unhooking robot to perform self-correction regulation and control based on the optimized correction scheme. The technical problem that in the prior art, the regulation and control precision is low when unhooking robot self-deviation-correction regulation and control are carried out is solved, and the technical effect of improving the unhooking robot self-deviation-correction regulation and control precision is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of machine control, and in particular to a self-correction control method and device for a hook-removing robot combined with follow-up analysis. Background Art

[0002] In the field of modern industrial automation, hook-removing robots are widely used in a variety of complex operation scenarios. When performing tasks, these robots need to operate in a high-speed, high-precision environment, and often face changing environmental factors and precise operation requirements. In these operations, the precise control of the position, angle, force feedback and other parameters of the hook-removing robot directly affects the success rate of the task and the safety of the operation.

[0003] However, in actual applications, when performing complex operations, the unhooking robot is often disturbed by external factors, resulting in reduced operation accuracy or operation failure. In this case, the robot needs to have the ability to self-correct, that is, to be able to detect and correct its operation deviation in real time during the operation to ensure the smooth completion of the task. Traditional correction methods mainly rely on pre-set rules and control algorithms, but since these methods are usually difficult to adapt to the complex and changeable actual operating environment, the correction effect is limited, resulting in the problem of low control accuracy when performing self-correction control of the unhooking robot. Summary of the invention

[0004] The present application provides a method and device for self-correction control of a hook removing robot combined with follow-up analysis, which is used to solve the technical problem of low control accuracy in the prior art when performing self-correction control of a hook removing robot.

[0005] In view of the above problems, the present application provides a self-correction control method and device for a hook-removing robot combined with follow-up analysis.

[0006] The first aspect of the present application provides a self-correction control method for a hook-removing robot combined with follow-up analysis, the method comprising:

[0007] The position data, angle data, force feedback data and environmental data of the unhooking robot during operation are acquired in real time to construct a dynamic model of the robot operation, wherein the dynamic model includes operation parameters and environmental status information; based on the dynamic model of the robot operation, the operation deviation of the unhooking robot is analyzed in real time to determine the position deviation and force feedback deviation of the robot in different operation steps, and generate a deviation data set; according to the preset correction rules, the deviation data set is fuzzy processed, the preset correction rules are matched and fuzzy reasoning calculation is performed to generate an initial correction plan; based on the initial correction plan, the correction strategy is optimized to generate an optimized correction plan; based on the optimized correction plan, the unhooking robot is controlled to perform self-correction regulation.

[0008] The second aspect of the present application provides a self-correction control device for a hook-removing robot combined with follow-up analysis, the device comprising:

[0009] A dynamic model building module, the dynamic model building module acquires the position data, angle data, force feedback data and environmental data of the unhooking robot in real time during the operation process, and builds a dynamic model of the robot operation, wherein the dynamic model includes operation parameters and environmental status information; a deviation data acquisition module, the deviation data acquisition module analyzes the operation deviation of the unhooking robot in real time based on the robot operation dynamic model, determines the position deviation and force feedback deviation of the robot in different operation steps, and generates a deviation data set; a correction scheme acquisition module, the correction scheme acquisition module fuzzifies the deviation data set according to preset correction rules, matches the preset correction rules and performs fuzzy reasoning calculation to generate an initial correction scheme; a correction strategy optimization module, the correction strategy optimization module optimizes the correction strategy based on the initial correction scheme to generate an optimized correction scheme; a self-correction control module, the self-correction control module controls the unhooking robot to perform self-correction control based on the optimized correction scheme.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The present application obtains the position data, angle data, force feedback data and environmental data of the unhooking robot in the operation process in real time, and constructs a dynamic model of the robot operation, wherein the dynamic model includes operation parameters and environmental status information; based on the dynamic model of the robot operation, the operation deviation of the unhooking robot is analyzed in real time, the position deviation and force feedback deviation of the robot in different operation steps are determined, and a deviation data set is generated; according to the preset correction rule, the deviation data set is fuzzy processed, the preset correction rule is matched and fuzzy reasoning calculation is performed to generate an initial correction scheme; based on the initial correction scheme, the correction strategy is optimized to generate an optimized correction scheme; based on the optimized correction scheme, the unhooking robot is controlled to perform self-correction regulation. The present invention solves the technical problem of low control accuracy in the prior art when performing self-correction regulation of the unhooking robot, by real-time acquisition and analysis of the position, angle, force feedback and environmental data of the unhooking robot, constructing a dynamic model, identifying operation deviations, generating an initial scheme through fuzzy processing and correction rules, and then performing strategy optimization, and finally performing self-correction regulation, so as to achieve the technical effect of improving the self-correction regulation accuracy of the unhooking robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic flow chart of a method for self-correction and control of a hook-removing robot combined with follow-up analysis provided in an embodiment of the present application;

[0014] Figure 2 Schematic diagram of the structure of the self-correcting and control device of the hook-removing robot combined with follow-up analysis provided in an embodiment of the present application.

[0015] Explanation of the accompanying drawings: dynamic model building module 11, deviation data acquisition module 12, correction plan acquisition module 13, correction strategy optimization module 14, self-correction control module 15. DETAILED DESCRIPTION

[0016] The present application provides a self-correction control method and device for a hook removing robot combined with follow-up analysis, aiming to solve the technical problem of low control accuracy in the prior art when performing self-correction control of the hook removing robot. By acquiring and analyzing the position, angle, force feedback and environmental data of the hook removing robot in real time, a dynamic model is constructed, and operational deviations are identified. An initial plan is generated through fuzzy processing and correction rules, and then strategy optimization is performed. Finally, self-correction control is performed, thereby achieving the technical effect of improving the self-correction control accuracy of the hook removing robot.

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0018] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0019] Embodiment 1, as Figure 1 As shown, the present application provides a self-correction control method for a hook-removing robot combined with follow-up analysis, the method comprising:

[0020] Step S100: acquiring the position data, angle data, force feedback data and environmental data of the unhooking robot in real time during operation, and constructing a dynamic model of the robot operation, wherein the dynamic model includes operation parameters and environmental status information.

[0021] In the embodiment of the present application, the position data is acquired by installing a displacement sensor on the robot, such as a laser rangefinder or an ultrasonic sensor, to monitor the position change of the robot in real time, and the position of the robot in the workspace is obtained through the data output of the sensor. Next, the angle data is acquired by installing an angle sensor, such as an encoder, a gyroscope or an accelerometer, on the joints or other rotating parts of the robot to obtain the angle data. At the same time, the force feedback data is obtained by installing a force sensor, such as a strain gauge sensor or a piezoelectric sensor, on the end effector or key joint of the robot. These data reflect the mechanical relationship between the robot and the work object, such as the size and change of the grasping or force. In addition, the environmental data is acquired by collecting environmental data around the robot, such as temperature and humidity sensors and infrared sensors, including temperature, humidity, light intensity, obstacle distance, etc., which can help the system understand the state of the current working environment. Subsequently, through data fusion and dynamic model construction technology, the position, angle, force feedback and environmental data are integrated together to build a real-time dynamic model of the operation.

[0022] Furthermore, in the method provided in the embodiment of the application, the position, angle, force feedback and environmental data of the unhooking robot during operation are obtained in real time, and a dynamic model of the robot operation is constructed, which also includes:

[0023] Based on the position data and in combination with the kinematic equations, a robot kinematic model is constructed; based on the angle data, the kinematic model is geometrically transformed to construct a robot posture channel; based on the force feedback data, a robot mechanical channel is constructed, and the robot mechanical channel is used to describe the mechanical relationship between the unhooking robot and the working object; based on the environmental data, the working scene is restored to construct a robot working environment channel; the robot posture channel, the robot mechanical channel, and the robot working environment channel are fused to construct the robot working dynamic model.

[0024] In an embodiment of the present application, a kinematic model is first constructed based on position data. Specifically, the position data obtained by the displacement sensor is used to calculate the position of each joint of the robot and its relationship with the final position in the workspace by applying kinematic equations, such as forward kinematics. Through these equations, the positional relationship of the various components of the robot in space is determined, and a preliminary kinematic model is constructed. Then, a geometric transformation is performed based on the angle data to construct a posture channel. First, the real-time angle data of each joint of the robot is obtained through an angle sensor. Then, using these angle data, a geometric transformation is performed on the previously constructed kinematic model. Specifically, the angle of the joint is converted into posture information in three-dimensional space, such as the direction and angle of the robot arm, through a rotation matrix or a homogeneous transformation matrix. This conversion expands the original position information into complete posture information to form a robot posture channel.

[0025] Then, a mechanical channel is constructed based on the force feedback data, and the force applied by the robot to the work object is monitored in real time through the end effector or installed force sensor of the robot. The mechanical model is constructed using these force feedback data. The model includes mechanical parameters such as contact force and friction force, and analyzes the distribution and influence of these forces through mechanical equations such as Newton's second law, thereby forming a robot mechanical channel. Next, the work scene is restored based on the environmental data, and the environmental channel is constructed. Through environmental sensors, key parameters in the working environment, such as temperature, humidity, light, obstacle position, etc., are obtained. Using environmental data, the current working scene is restored through image processing technology, and the physical environment of the robot is virtualized, so that the robot can accurately locate and operate in complex or changing environments, and an environmental channel is constructed.

[0026] Finally, the attitude channel, mechanical channel and environmental channel constructed in the above steps are fused using data fusion technology, such as Kalman filtering or Bayesian estimation, and the data of different channels are integrated and calibrated for consistency to ensure that all data can synchronously reflect the state of the robot in the workspace. Through the above steps, the fused robot operation dynamic model is obtained.

[0027] Step S200: Based on the robot operation dynamic model, the operation deviation of the unhooking robot is analyzed in real time to determine the position deviation and force feedback deviation of the robot in different operation steps, and generate a deviation data set.

[0028] In an embodiment of the present application, the position and posture of the robot during the operation are first monitored in real time through the robot operation dynamic model, and the actual operation is compared with the preset ideal trajectory. The determination of the position deviation depends on the position data in the dynamic model. By calculating the difference between the current position coordinates of the robot and the ideal position, the degree of displacement of the robot in space is accurately identified. At the same time, the mechanical channel in the robot operation dynamic model provides real-time force feedback data. Using these data, compared with the preset ideal force value in the model, the force feedback deviation is determined, that is, the difference between the actual applied force and the expected force. Among them, the preset ideal force value and ideal position in the robot operation dynamic model are pre-set by technical experts based on the working requirements of the unhooking robot.

[0029] Through the above process, the position deviation and force feedback deviation of the robot in different working steps are determined, and a deviation data set is generated.

[0030] Furthermore, in the method provided in the embodiment of the application, generating a deviation data set also includes:

[0031] Perform real-time trajectory comparison analysis, calculate the deviation from the preset ideal trajectory, and generate position deviation data; perform dynamic correction analysis on angle data, calculate the angle difference between the real-time operation angle and the preset target angle, and generate angle deviation data; perform abnormality detection on force feedback data, monitor the fluctuation amplitude and frequency of force feedback during operation, and compare them with the preset force feedback reference value to generate force feedback deviation data; perform real-time monitoring of environmental data, compare current environmental parameters with preset environmental standard values, and generate environmental deviation data; summarize the position deviation data, the angle deviation data, the force feedback deviation data, and the environmental deviation data to generate the deviation data set.

[0032] In an embodiment of the present application, the actual motion trajectory of the robot is first recorded in a dynamic model through the position data obtained in real time. Then, this actual trajectory is compared with the preset ideal trajectory, and a path planning algorithm, such as the Dijkstra algorithm, is used to calculate the distance deviation between the two, thereby generating position deviation data. Next, the angle data is obtained through the dynamic model, and these real-time angle data are geometrically transformed. By comparing these converted data with the preset target angle, the angle difference is corrected using geometric tools such as rotation matrices or quaternions, and finally the difference between the real-time operating angle and the target angle is calculated to generate angle deviation data. Here, the dynamic model provides real-time angle information, and the preset ideal angle is set independently for comparative analysis.

[0033] Anomaly detection is then performed based on the force feedback data in the dynamic model. The force sensors record the mechanical interaction between the robot and the work object in real time, and this data is input into the dynamic model. These real-time force feedback data are then compared with the preset ideal force values, and signal processing algorithms such as fast Fourier transform or wavelet transform are used to identify abnormal fluctuations, thereby generating force feedback deviation data. This process also relies on real-time data from the dynamic model and compares it with an independently set ideal force value.

[0034] In addition, the working environment is monitored in real time through the environmental data channel in the dynamic model. Environmental sensors collect current environmental parameters such as temperature, humidity, light intensity, etc., record these data in the dynamic model, and compare them with the preset ideal environmental standards. Through environmental modeling technology, the deviation of environmental parameters is identified and environmental deviation data is generated. These data reflect the difference between the actual environment and the ideal conditions.

[0035] Finally, the actual deviation information obtained through the robot operation dynamic model is compared with the independently set ideal data, and the position deviation data, angle deviation data, force feedback deviation data and environmental deviation data are summarized to generate a deviation data set.

[0036] Step S300: According to the preset correction rules, the deviation data set is fuzzified, the preset correction rules are matched and fuzzy reasoning calculation is performed to generate an initial correction plan.

[0037] In an embodiment of the present application, the generated deviation data set is processed according to the preset correction rules to formulate an initial correction plan. Specifically, the deviation data set is first fuzzified. Then the fuzzified deviation data is matched with the preset correction rules. These rules are set by technical experts based on historical data and describe the correction measures to be taken in different deviation situations. After the rule matching is completed, fuzzy reasoning calculations are performed to generate an initial correction plan, which includes how the robot should adjust operating parameters such as position, angle, and strength.

[0038] Furthermore, in the method provided in the embodiment of the application, according to the preset correction rule, the deviation data set is fuzzified, the preset correction rule is matched and fuzzy reasoning calculation is performed to generate an initial correction scheme, and the method also includes:

[0039] Perform multi-level fuzzification processing on the deviation data set, map the initial fuzzy membership, and generate a multidimensional membership matrix; based on the multidimensional membership matrix, perform hierarchical rule screening to generate a primary correction rule set, perform correlation authentication on the primary correction rule set, and determine an optimized correction rule group; perform cross-activation processing on the optimized correction rule group, assign activation weights and set execution priorities, and output a priority correction rule matrix; based on the priority correction rule matrix, perform multiple defuzzification processing, extract preliminary operation instruction values, and generate a first correction plan; dynamically compare the first correction plan with real-time data to generate the initial correction plan.

[0040] In an embodiment of the present application, the deviation data generated by the unhooking robot during the operation process is first subjected to multi-level fuzzification processing. This process uses fuzzy logic technology to convert precise numerical deviations into fuzzy memberships, so that the system can better handle uncertainty and complexity. Specifically, a membership function, such as a triangular or trapezoidal membership function, is used to map data such as position deviation, angle deviation, force feedback deviation, etc. into a fuzzy set. For example, the position deviation is fuzzified into "slight deviation" or "moderate deviation" to generate an initial fuzzy membership. Next, based on these initial fuzzy memberships, a multidimensional membership matrix is ​​constructed. Each matrix dimension represents a deviation type, such as position, angle, and force feedback, and each element represents the membership value of the corresponding deviation in different fuzzy sets. This matrix integrates the membership data of different deviation types and provides a comprehensive view for subsequent rule matching and screening.

[0041] Then, based on the multidimensional membership matrix, hierarchical rule screening is performed. Through the preset correction rule library, the rule matching algorithm is used to screen out the correction rules most relevant to the current deviation layer by layer to form a primary correction rule set. The preset correction rule library is set by technical experts based on historical data. Each rule provides specific correction measures for different operation scenarios and deviation conditions. For example, "If the position deviation is 'moderate' and the angle deviation is 'slight', then increase the robot arm angle adjustment by 3 degrees." Through rule matching algorithms, such as conditional reasoning or rule-based reasoning, the data in the multidimensional membership matrix are compared to screen out the rules most relevant to the current deviation situation layer by layer. The screening process is divided into multiple levels. First, a preliminary screening is performed based on the maximum membership of a single dimension, and then the membership of other dimensions is gradually added for cross-screening. For example, if the matrix shows that the membership of the position deviation is high, the rules related to the position adjustment are selected first, and then the membership of the angle and force feedback are considered, and finally the primary correction rule set suitable for the current operation situation is screened out. Then, the primary correction rule set is authenticated for relevance, and a rule association matrix is ​​constructed through association rule mining algorithms, such as the Apriori algorithm, to identify dependencies or potential conflicts between rules. Logical reasoning analysis is used to identify conflicts, and conflict resolution algorithms, such as priority sorting, are used to handle these conflicts, and finally the optimized correction rule group is determined. Specifically, the Apriori algorithm is first used to mine association rules, and by analyzing historical operation data, it is identified which rules are often triggered together in past operations or have dependencies. For example, if rule A and rule B are applied simultaneously in multiple operation scenarios, they are regarded as rules with strong association. Through this process, a rule association matrix is ​​constructed. Next, logical reasoning analysis is used to identify dependencies or potential conflicts between rules. At this stage, by analyzing the preconditions and execution results of each rule, it is determined whether there is a logical conflict between them. For example, if rule A requires increasing the gripping force of the robot arm, and rule B requires reducing the gripping force under the same conditions, the conflict relationship between these rules is identified through logical reasoning analysis. Further use the rule relationship displayed in the association matrix to confirm which rules may conflict in actual operations and mark these rules as conflicting pairs. After potential conflicts are identified, they are handled using a prioritization algorithm. Prioritization determines the order in which rules are executed based on their importance, historical success rate, and the needs of the current operating environment. When dealing with conflicts, high-priority rules are executed first, while low-priority rules are adjusted or suppressed to avoid conflicts. Ultimately, these steps generate an optimized set of correction rules.

[0042] After determining the optimized deviation correction rule group, cross-activation processing is performed to calculate the activation strength of each rule, and activation weights are assigned according to the needs and importance of the current operation, and execution priorities are set to generate a priority deviation correction rule matrix. Based on the priority deviation correction rule matrix, multiple defuzzification processes are performed, and defuzzification techniques such as the centroid method or the maximum membership method are used to convert fuzzy membership into specific operation instruction values, thereby generating the first deviation correction plan.

[0043] Finally, the first correction scheme is dynamically compared with the real-time operation data to ensure that the scheme is completely consistent with the actual situation of the current operating environment. In this process, the consistency of the current operating environment with the preset conditions is verified by comparing the real-time data with the assumed conditions of the first correction scheme. If the comparison result shows that there is no difference between the real-time data and the assumed data in the scheme, the first correction scheme is directly determined as the initial correction scheme.

[0044] Furthermore, in the method provided in the embodiment of the application, the optimized deviation correction rule group is cross-activated, activation weights are assigned and execution priorities are set, and a priority deviation correction rule matrix is ​​output, which also includes:

[0045] Based on the multiple memberships of the optimized correction rule group, multiple initial activation strengths of the optimized correction rule group are calculated to generate an initial activation matrix; internal relationship identification is performed on the optimized correction rule group, and the initial activation matrix is ​​adjusted according to the identification results to generate an adjusted activation matrix; based on the adjusted activation matrix, activation weights are assigned to the optimized correction rule group to generate a weight assignment matrix; based on the weight assignment matrix, the execution priority of the optimized correction rule group is set to generate a priority matrix; rules are sorted based on the priority matrix to generate the priority correction rule matrix.

[0046] In an embodiment of the present application, the activation strength of each rule is first calculated based on the current environmental data and the membership of the optimized deviation correction rule group. Specifically, the activation strength of each rule is calculated using the membership and rule importance factor, activation strength = membership × rule importance factor. The rule importance factor is a weight value pre-set by a technical expert. Finally, the calculated activation strength values ​​are summarized to form an initial activation matrix, where each row in the matrix represents a rule and each column represents a different environmental variable.

[0047] After generating the initial activation matrix, further identify the internal relationships between the rules to ensure that they can work in harmony. First, use logical analysis and dependency analysis techniques to evaluate the antecedents and consequences between the rules. For example, if rule A requires the robot to slow down, and rule B requires the robot to adjust its angle after the speed is reduced, identify the dependency relationship between rules A and B. In addition, identify rules that may conflict, such as one rule requiring the robot to increase its gripping force, while another rule requires it to be reduced. After identifying these relationships, adjust the initial activation matrix. For rules with dependencies, reduce the activation strength of the dependent rules to ensure that the predecessor rules are executed first. For conflicting rules, reduce the activation strength of the conflicting rules, or suppress one of the rules completely to avoid conflicts in actual operations. The adjusted activation matrix is ​​called the adjusted activation matrix, which reflects the modified activation strength of each rule after considering the internal relationships of the rules.

[0048] After the adjustment activation matrix is ​​generated, weights are assigned to each rule in order to determine their execution priority in actual operation. Specifically, activation weights are first assigned based on the adjusted activation strength. Weight assignment takes into account multiple factors such as the urgency of the task, the stability of the equipment status, the complexity of the operating environment, and safety requirements. For example, priority is given to rules involving safe operations, while rules related to efficiency improvement are assigned lower weights. These weight values ​​reflect the importance of each rule in the current operating environment and the priority of the execution order, and are finally summarized to form a weight assignment matrix. Next, the information in the weight assignment matrix is ​​converted into specific execution priorities through a priority sorting algorithm to ensure that rules with high weights are executed first. The priority matrix is ​​generated through the above process.

[0049] Finally, all rules are sorted according to the priority matrix. The higher the priority, the higher the position of the rule in the sorting. Once the rules are sorted, the execution sequence is generated according to this order. Finally, based on the generated execution sequence, a priority correction rule matrix is ​​generated. Each row of the matrix corresponds to an operation step, that is, a rule, and each column corresponds to a different operation condition or key parameter in the step.

[0050] Step S400: Based on the initial correction plan, the correction strategy is optimized to generate an optimized correction plan.

[0051] Furthermore, in the method provided in the embodiment of the application, based on the initial correction scheme, the correction strategy is optimized to generate an optimized correction scheme, and the method further includes:

[0052] Based on the initial correction scheme, random combinations are performed to generate an initial correction strategy set; based on the robot operation dynamic model, simulated operations are performed according to the initial correction strategy set, and fitness calculations are performed to obtain the initial correction strategy fitness set; based on a preset fitness threshold, the initial correction strategy fitness set is selected to generate a second-generation correction strategy set; cross operations are performed on the second-generation correction strategy set to exchange adjustment parameters of position, angle and force feedback in different strategies to generate a third-generation correction strategy set; mutation operations are performed on the three-generation correction strategy set, and fitness evaluation is performed to select the strategy with the highest fitness as the optimized correction scheme.

[0053] In the embodiment of the present application, firstly, a random combination algorithm is used to generate multiple possible correction strategies based on the generated initial correction scheme. The random combination algorithm generates a series of strategies by combining different operating parameters, such as the position, angle, and force feedback of the robot arm. These strategies have diversity in parameter configuration to ensure that the system can explore various possible operation paths. These strategies are combined together to form an initial correction strategy set.

[0054] Next, based on the robot operation dynamic model, the initial correction strategy set is simulated. The system inputs each strategy in the initial correction strategy set into the operation dynamic model for simulation. During the simulation, the response behavior of the robot arm is tracked in real time, including position changes, angle adjustment accuracy, force feedback stability, etc. The performance of each strategy is then calculated through the fitness function, which performs a comprehensive evaluation based on key performance indicators such as operation accuracy, stability, and energy consumption. Finally, these fitness values ​​are summarized to form the initial correction strategy fitness set.

[0055] On this basis, through the fitness screening algorithm, based on the preset fitness threshold, the strategies with better performance are selected from the initial correction strategy fitness set to generate the second-generation correction strategy set. The fitness screening algorithm eliminates those strategies with fitness below the threshold according to the preset fitness threshold, and gathers the strategies that pass the screening into the second-generation correction strategy set. These strategies have shown good results in preliminary simulations.

[0056] In order to increase the diversity of strategies and explore new possible solutions, the second-generation correction strategy set is cross-operated, and new strategy combinations are generated by exchanging parameters in different strategies. Then, two or more strategies in the second-generation correction strategy set are randomly selected as parents, and cross-exchanges are performed on different parameters of these strategies, such as position parameters, angle parameters, and force feedback parameters, to generate new third-generation correction strategies. These strategies are obtained through cross-operation and may combine the advantages of the parent strategies. Through this process, a three-generation correction strategy set is obtained.

[0057] Then, the three-generation correction strategy set is mutated to prevent the strategy from converging to the local optimal solution too early. The mutation operation introduces random changes. Common mutation methods include parameter perturbation, random increase and decrease, etc. A part of the strategies in the three-generation correction strategy set is randomly selected to make fine or large adjustments to the parameters. Then, the fitness of the mutated strategies is evaluated again, and the performance of these strategies in the simulation operation is recalculated using the previously set fitness function. Through the fitness evaluation, the strategy with the highest fitness is finally selected as the optimized correction solution.

[0058] Furthermore, in the method provided in the embodiment of the application, based on the robot operation dynamic model, simulated operation is performed according to the initial correction strategy set, and fitness calculation is performed to obtain the initial correction strategy fitness set, and further includes:

[0059] The initial correction strategy set is input into the robot operation dynamic model for simulation operation to obtain the simulation operation results of the robot when executing each correction strategy, including the accuracy, operation stability and energy consumption after correction; according to the simulation operation results, a preset fitness function is used for evaluation to generate an initial correction strategy fitness set.

[0060] In the embodiment of the present application, the generated initial deviation correction strategy set is first input into the robot operation dynamic model for simulation operation, simulating the actual operation performance of the robot arm under different strategies. In the simulation operation, each operation step of the robot arm is tracked and recorded in detail to obtain the operation accuracy, operation stability and energy consumption after deviation correction.

[0061] After the simulation operation is completed, the simulation results of each strategy are evaluated using the preset fitness function. The fitness function is a mathematical formula used to comprehensively evaluate the pros and cons of a strategy, including the weighted sum of multiple indicators, such as operation accuracy, stability, and energy consumption.

[0062] Finally, the fitness values ​​of all strategies are summarized to generate the initial correction strategy fitness set.

[0063] Among them, the preset fitness function is:

[0064]

[0065] Among them, F represents the fitness value. The higher the fitness value, the better the performance of the strategy in the current simulation environment; E p Indicates the operation accuracy error. This is the deviation value between the robot arm and the target operation after performing the correction operation; S represents the operation stability, which is the sensitivity of the robot arm to the external environment during the entire operation process. The higher the S value, the more stable the robot arm is during the operation; C represents energy consumption, which is the amount of energy consumed to complete the correction task; w 1、w 2 、w 3 The weight factor is used to indicate the relative importance of each indicator in the fitness calculation. It is pre-set, w 1 =0.5, w 2 =0.3, w 3 =0.2.

[0066] To calculate E p , calculate the difference between the actual operation result of the robot arm and the expected result. The accuracy error includes position deviation, angle deviation, etc. The calculation formula is the square root of the sum of the squares of each deviation, that is, the Euclidean distance to measure the overall error.

[0067] To obtain operational stability S, it is expressed by the variance or standard deviation of the vibration sensor data.

[0068] To calculate the energy consumption C, record the energy consumption of the robot during the entire operation, that is, the power required for the movement of the robot.

[0069] Step S500: Based on the optimized deviation correction scheme, the unhooking robot is controlled to perform self-correction regulation.

[0070] In an embodiment of the present application, an optimized deviation correction scheme is input into the control system of the unhooking robot. This scheme includes specific operating parameters, such as the position of the robotic arm, angle adjustment, force feedback settings, etc. The real-time control algorithm is used to ensure that the robot accurately executes each step in the scheme. For example, if the scheme requires the gripping operation to be performed after adjusting the angle of the robotic arm, these operating instructions are executed in sequence in the established order. Through this process, the self-correction control of the unhooking robot is completed.

[0071] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0072] The present application obtains the position data, angle data, force feedback data and environmental data of the unhooking robot in the operation process in real time, and constructs a dynamic model of the robot operation, wherein the dynamic model includes operation parameters and environmental status information; based on the dynamic model of the robot operation, the operation deviation of the unhooking robot is analyzed in real time, the position deviation and force feedback deviation of the robot in different operation steps are determined, and a deviation data set is generated; according to the preset correction rule, the deviation data set is fuzzy processed, the preset correction rule is matched and fuzzy reasoning calculation is performed to generate an initial correction scheme; based on the initial correction scheme, the correction strategy is optimized to generate an optimized correction scheme; based on the optimized correction scheme, the unhooking robot is controlled to perform self-correction regulation. The present invention solves the technical problem of low control accuracy in the prior art when performing self-correction regulation of the unhooking robot, by real-time acquisition and analysis of the position, angle, force feedback and environmental data of the unhooking robot, constructing a dynamic model, identifying operation deviations, generating an initial scheme through fuzzy processing and correction rules, and then performing strategy optimization, and finally performing self-correction regulation, so as to achieve the technical effect of improving the self-correction regulation accuracy of the unhooking robot.

[0073] Embodiment 2 is based on the same inventive concept as the method for self-correction control of the unhooking robot combined with follow-up analysis in the previous embodiment. Figure 2 As shown, the present application provides a self-correction control device for a hook-removing robot combined with follow-up analysis, and the device and method embodiments in the present application are based on the same inventive concept. The device includes:

[0074] A dynamic model building module 11, the dynamic model building module 11 acquires the position data, angle data, force feedback data and environmental data of the unhooking robot in real time during the operation process, and builds a dynamic model of the robot operation, wherein the dynamic model includes operation parameters and environmental status information; a deviation data acquisition module 12, the deviation data acquisition module 12 analyzes the operation deviation of the unhooking robot in real time based on the robot operation dynamic model, determines the position deviation and force feedback deviation of the robot in different operation steps, and generates a deviation data set; a correction scheme acquisition module 13, the correction scheme acquisition module 13 performs fuzzy processing on the deviation data set according to preset correction rules, matches the preset correction rules and performs fuzzy reasoning calculation to generate an initial correction scheme; a correction strategy optimization module 14, the correction strategy optimization module 14 optimizes the correction strategy based on the initial correction scheme to generate an optimized correction scheme; a self-correction control module 15, the self-correction control module 15 controls the unhooking robot to perform self-correction control based on the optimized correction scheme.

[0075] Furthermore, the device is also used to achieve the following functions:

[0076] Based on the position data and in combination with the kinematic equations, a robot kinematic model is constructed; based on the angle data, the kinematic model is geometrically transformed to construct a robot posture channel; based on the force feedback data, a robot mechanical channel is constructed, and the robot mechanical channel is used to describe the mechanical relationship between the unhooking robot and the working object; based on the environmental data, the working scene is restored to construct a robot working environment channel; the robot posture channel, the robot mechanical channel, and the robot working environment channel are fused to construct the robot working dynamic model.

[0077] Furthermore, the device is also used to achieve the following functions:

[0078] Perform real-time trajectory comparison analysis, calculate the deviation from the preset ideal trajectory, and generate position deviation data; perform dynamic correction analysis on angle data, calculate the angle difference between the real-time operation angle and the preset target angle, and generate angle deviation data; perform abnormality detection on force feedback data, monitor the fluctuation amplitude and frequency of force feedback during operation, and compare them with the preset force feedback reference value to generate force feedback deviation data; perform real-time monitoring of environmental data, compare current environmental parameters with preset environmental standard values, and generate environmental deviation data; summarize the position deviation data, the angle deviation data, the force feedback deviation data, and the environmental deviation data to generate the deviation data set.

[0079] Furthermore, the device is also used to achieve the following functions:

[0080] Perform multi-level fuzzification processing on the deviation data set, map the initial fuzzy membership, and generate a multidimensional membership matrix; based on the multidimensional membership matrix, perform hierarchical rule screening to generate a primary correction rule set, perform correlation authentication on the primary correction rule set, and determine an optimized correction rule group; perform cross-activation processing on the optimized correction rule group, assign activation weights and set execution priorities, and output a priority correction rule matrix; based on the priority correction rule matrix, perform multiple defuzzification processing, extract preliminary operation instruction values, and generate a first correction plan; dynamically compare the first correction plan with real-time data to generate the initial correction plan.

[0081] Furthermore, the device is also used to achieve the following functions:

[0082] Based on the multiple memberships of the optimized correction rule group, multiple initial activation strengths of the optimized correction rule group are calculated to generate an initial activation matrix; internal relationship identification is performed on the optimized correction rule group, and the initial activation matrix is ​​adjusted according to the identification results to generate an adjusted activation matrix; based on the adjusted activation matrix, activation weights are assigned to the optimized correction rule group to generate a weight assignment matrix; based on the weight assignment matrix, the execution priority of the optimized correction rule group is set to generate a priority matrix; rules are sorted based on the priority matrix to generate the priority correction rule matrix.

[0083] Furthermore, the device is also used to achieve the following functions:

[0084] Based on the initial correction scheme, random combinations are performed to generate an initial correction strategy set; based on the robot operation dynamic model, simulated operations are performed according to the initial correction strategy set, and fitness calculations are performed to obtain the initial correction strategy fitness set; based on a preset fitness threshold, the initial correction strategy fitness set is selected to generate a second-generation correction strategy set; cross operations are performed on the second-generation correction strategy set to exchange adjustment parameters of position, angle and force feedback in different strategies to generate a third-generation correction strategy set; mutation operations are performed on the three-generation correction strategy set, and fitness evaluation is performed to select the strategy with the highest fitness as the optimized correction scheme.

[0085] Furthermore, the device is also used to achieve the following functions:

[0086] The initial correction strategy set is input into the robot operation dynamic model for simulation operation to obtain the simulation operation results of the robot when executing each correction strategy, including the accuracy, operation stability and energy consumption after correction; according to the simulation operation results, a preset fitness function is used for evaluation to generate an initial correction strategy fitness set.

[0087] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0088] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0089] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A self-correction control method for a hook-removing robot combined with follow-up analysis, characterized in that: The method comprises: Acquire the position data, angle data, force feedback data and environmental data of the unhooking robot in real time during operation, and build a dynamic model of the robot operation, wherein the dynamic model includes operation parameters and environmental status information; Based on the robot operation dynamic model, the operation deviation of the unhooking robot is analyzed in real time to determine the position deviation and force feedback deviation of the robot in different operation steps, and generate a deviation data set; According to the preset correction rules, the deviation data set is fuzzified, the preset correction rules are matched and fuzzy reasoning calculation is performed to generate an initial correction plan; Based on the initial correction plan, optimizing the correction strategy and generating an optimized correction plan; Based on the optimized deviation correction scheme, the unhooking robot is controlled to perform self-correction regulation.

2. The method for self-correction and control of a hook-removing robot combined with follow-up analysis as claimed in claim 1 is characterized in that: The position, angle, force feedback and environmental data of the unhooking robot during operation are obtained in real time, and a dynamic model of the robot operation is constructed. The method includes: Based on the position data and in combination with kinematic equations, a robot kinematic model is constructed; Based on the angle data, geometrically transform the kinematic model to construct a robot posture channel; Based on the force feedback data, a robot mechanical channel is constructed, wherein the robot mechanical channel is used to describe the mechanical relationship between the unhooking robot and the working object; Performing operation scene restoration based on the environmental data to build a robot operation environment channel; The robot posture channel, the robot mechanics channel, and the robot working environment channel are fused to construct the robot working dynamic model.

3. The self-correction control method of the unhooking robot combined with follow-up analysis as claimed in claim 1 is characterized in that: Generate a biased dataset, including: Perform real-time trajectory comparison and analysis, calculate the deviation from the preset ideal trajectory, and generate position deviation data; Perform dynamic correction analysis on the angle data, calculate the angle difference between the real-time operating angle and the preset target angle, and generate angle deviation data; Perform anomaly detection on force feedback data, and generate force feedback deviation data by monitoring the fluctuation amplitude and frequency of force feedback during operation and comparing them with the preset force feedback reference value; Monitor environmental data in real time, compare current environmental parameters with preset environmental standard values, and generate environmental deviation data; The position deviation data, the angle deviation data, the force feedback deviation data and the environment deviation data are aggregated to generate the deviation data set.

4. The self-correction control method of the hook-removing robot combined with follow-up analysis as claimed in claim 1 is characterized in that: According to the preset correction rule, the deviation data set is fuzzified, the preset correction rule is matched and fuzzy reasoning calculation is performed to generate an initial correction scheme, and the method includes: Performing multi-level fuzzification processing on the deviation data set, mapping the initial fuzzy membership, and generating a multi-dimensional membership matrix; Based on the multidimensional membership matrix, hierarchical rule screening is performed to generate a primary deviation correction rule set, and the primary deviation correction rule set is authenticated for relevance to determine an optimized deviation correction rule group; Performing cross-activation processing on the optimization correction rule group, allocating activation weights and setting execution priorities, and outputting a priority correction rule matrix; Based on the priority correction rule matrix, multiple defuzzification processes are performed to extract the preliminary operation instruction value and generate the first correction plan; The first deviation correction scheme is dynamically compared with the real-time data to generate the initial deviation correction scheme.

5. The self-correction control method of the unhooking robot combined with follow-up analysis as claimed in claim 1 is characterized in that: The optimized deviation correction rule group is cross-activated, activation weights are assigned, execution priorities are set, and a priority deviation correction rule matrix is ​​output. The method includes: Based on the multiple membership degrees of the optimized deviation correction rule group, multiple initial activation strengths of the optimized deviation correction rule group are calculated to generate an initial activation matrix; Identify the internal relationship of the optimization correction rule group, adjust the initial activation matrix according to the identification result, and generate an adjusted activation matrix; Based on the adjustment activation matrix, assigning activation weights to the optimization correction rule group to generate a weight assignment matrix; Based on the weight allocation matrix, setting the execution priority of the optimization correction rule group to generate a priority matrix; The rules are sorted based on the priority matrix to generate the priority correction rule matrix.

6. The method for self-correction and control of a hook-removing robot combined with follow-up analysis as claimed in claim 1, characterized in that: Based on the initial correction scheme, the correction strategy is optimized to generate an optimized correction scheme, the method comprising: Based on the initial deviation correction scheme, a random combination is performed to generate an initial deviation correction strategy set; Based on the robot operation dynamic model, simulate the operation according to the initial deviation correction strategy set, and perform fitness calculation to obtain the initial deviation correction strategy fitness set; Selecting the initial deviation correction strategy fitness set based on a preset fitness threshold to generate a second-generation deviation correction strategy set; Performing cross operations on the second-generation correction strategy set, exchanging adjustment parameters of position, angle and force feedback in different strategies, and generating a third-generation correction strategy set; The three generations of correction strategy sets are mutated and evaluated for fitness, and the strategy with the highest fitness is selected as the optimized correction solution.

7. The self-correction control method of the hook-removing robot combined with follow-up analysis as claimed in claim 6 is characterized in that: Based on the robot operation dynamic model, simulated operation is performed according to the initial correction strategy set, and fitness calculation is performed to obtain the initial correction strategy fitness set, the method comprising: Inputting the initial deviation correction strategy set into the robot operation dynamic model for simulation operation, and obtaining the simulation operation results of the robot when executing each deviation correction strategy, including the accuracy, operation stability and energy consumption after correction; According to the simulation operation results, a preset fitness function is used for evaluation to generate an initial deviation correction strategy fitness set.

8. The self-correction control device of the hook-removing robot combined with follow-up analysis is characterized in that: The device comprises: A dynamic model building module, wherein the dynamic model building module obtains the position data, angle data, force feedback data and environmental data of the unhooking robot in real time during operation, and builds a dynamic model of the robot operation, wherein the dynamic model includes operation parameters and environmental status information; A deviation data acquisition module, which performs real-time analysis on the operation deviation of the unhooking robot based on the robot operation dynamic model, determines the position deviation and force feedback deviation of the robot in different operation steps, and generates a deviation data set; A correction scheme acquisition module, wherein the correction scheme acquisition module performs fuzzy processing on the deviation data set according to a preset correction rule, matches the preset correction rule and performs fuzzy reasoning calculation to generate an initial correction scheme; A correction strategy optimization module, wherein the correction strategy optimization module optimizes the correction strategy based on the initial correction plan and generates an optimized correction plan; A self-correcting control module controls the unhooking robot to perform self-correcting control based on the optimized correction scheme.

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