Robot-positioner PLC cooperative communication control method and system

Abstract

The invention relates to the technical field of industrial robot control, and discloses a robot-positioner PLC cooperative communication control method and system.The method comprises the steps that an initial cooperative trajectory entropy model is built by receiving a robot tail end pose sequence and a positioner target angle sequence, and the actual pose, servo feedback signals and welding energy flux density parameters are collected in real time; generating a time-varying weight matrix and compensation intensity, performing dynamic optimization and fault-tolerant correction on the trajectory based on the initial collaborative trajectory entropy model, and outputting a collaborative control instruction; the problems of communication delay, limited positioning precision, poor system compatibility, weak fault-tolerant capability and the like in a traditional control method are effectively solved, high-precision synchronous control over the welding robot system and the positioner is achieved, and the production efficiency and the welding quality of the flexible welding workstation are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robot control, and more specifically, to a robot-positioner PLC collaborative communication control method and system. Background Art

[0002] In the field of industrial robot automation control technology, with the continuous increase in the requirements of the manufacturing industry for production precision and efficiency, the collaborative control of robots and positioners in welding operations has become a key link. Especially in the field of automotive industry welding, a robot flexible welding workstation requires the positioner to accurately position the workpiece at multiple angles to achieve the operation of complex welding paths and meet the high-quality welding requirements. However, traditional control methods have exposed many problems that cannot be ignored in practical applications, restricting the improvement of the quality and efficiency of welding production.

[0003] In the existing related technologies, the Chinese patent with the authorization announcement number CN103513612B proposed a system and method for controlling the coordinated movement of an industrial robot and a positioner, and realized the coordinated movement of the robot and the positioner through a series of steps such as establishing a link coordinate system, obtaining coordinate transformation relations, and performing spline interpolation. Although this method solves the problem of the coordinated movement of an industrial robot and a positioner along a complex space trajectory, it does not fully consider the dynamic factors in actual production, such as communication delay, mechanical vibration during equipment operation, and the influence of environmental factors on movement accuracy. In an actual industrial environment, the communication delay between the robot and the positioner will cause the actions of the two to be inconsistent in coordination, and this existing technology lacks an effective response mechanism for this problem.

[0004] The Chinese patent application with the publication number CN109015652A discloses a control method for the coordinated movement of a robot and a positioner, which coordinates the movement by establishing a coordinate system transformation relationship through robot manual teaching, meets the general requirements of the robot to a certain extent, simplifies the process of repositioning and setting the coordinate system transformation relationship, and improves production efficiency. However, the communication protocols of robots and PLCs of different brands vary greatly, and this existing technology does not involve how to solve the problem of inconsistent communication protocols. When actually integrating systems, if devices of different brands are used, customized development interfaces still need to be carried out, increasing the complexity and cost of system integration. In addition, this existing technology lacks an effective fault tolerance mechanism in the face of abnormal working conditions, such as communication interruption or workpiece offset.

[0005] The existing robot-positioner collaborative control technology cannot meet the diverse working conditions requirements of modern flexible welding workstations in terms of communication delay processing, positioning accuracy improvement, system compatibility optimization, and fault tolerance ability enhancement. Summary of the Invention

[0006] The present invention is applicable to robotic flexible welding workstations in fields such as automobile manufacturing and aerospace, especially for multi-angle continuous welding scenarios of complex curved workpieces (such as vehicle body side panels and aircraft skins). Through PLC collaborative communication control, millisecond-level synchronization between the rotating / tipping axes of the positioner and the welding torch at the end of the robot is achieved, ensuring the accuracy of the weld seam trajectory and the consistency of the penetration depth. For example, in the welding of new energy vehicle battery trays, the positioner needs to flip the workpiece with high precision, while the welding torch at the end of the robot needs to maintain a constant welding speed along a three-dimensional curved path. The present invention can avoid welding defects such as false welding or burn-through caused by collaborative errors through dynamic compensation of thermal deformation and mechanical errors.

[0007] To overcome the above-mentioned defects of the prior art, the present invention provides a robot-positioner PLC collaborative communication control method and system. By constructing an initial collaborative trajectory entropy value model, collecting multi-source data to generate a time-varying weight matrix and compensation intensity, and dynamically optimizing and fault-tolerantly correcting the trajectory, problems such as communication delay, limited positioning accuracy, poor system compatibility, and weak fault tolerance in traditional control methods are effectively solved. The invention realizes high-precision synchronous control of the welding robot system and the positioner, significantly improves the production efficiency of the flexible welding workstation, reduces welding defects, improves welding quality, and brings a more reliable and efficient control solution for industrial welding production.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A robot-positioner PLC collaborative communication control method, comprising:

[0010] Receiving the end-effector pose sequence P(t) of the robot and the target angle sequence A(t) of the positioner, and constructing an initial collaborative trajectory entropy value model H0 according to P(t) and A(t); real-time collecting the actual end-effector pose sequence P'(t) of the robot, the servo feedback signal F(t) of the positioner, and the welding energy flux density parameter Q(t); extracting the actual angle sequence A'(t) of the positioner according to the servo feedback signal F(t) of the positioner; generating a time-varying weight matrix W(t) according to P(t), A(t), P'(t), Q(t) and A'(t), and calculating the compensation intensity λ(t);

[0011] According to the initial collaborative trajectory entropy value model H0, the time-varying weight matrix W(t) and the compensation intensity λ(t), performing trajectory optimization and fault-tolerant correction on P'(t) and A'(t) to obtain the end-effector pose fault-tolerant optimization sequence P f (t) and the positioner angle fault-tolerant optimization sequence A f (t); using P f (t) and A f (t) as the collaborative control instructions for the robot and the positioner.

[0012] Further, the expression of the end - pose sequence P(t) of the robot is P(t) = [x(t), y(t), z(t), α(t), β(t), γ(t)], and the expression of the target - angle sequence A(t) of the positioner is A(t) = [α1(t), β1(t)], where t is the time variable, x(t), y(t), and z(t) respectively represent the position coordinates of the robot end in the base coordinate system; α(t), β(t), and γ(t) respectively represent the attitude angles of the robot end around the x, y, and z axes of the base coordinate system; α1(t) represents the target angle of the rotating axis of the positioner, and β1(t) represents the target angle of the tilting axis of the positioner.

[0013] Further, generating the time - varying weight matrix W(t) according to P(t), A(t), P'(t), Q(t) and A'(t), and calculating the compensation intensity λ(t) includes:

[0014] Calculating the robot pose deviation ΔP(t) from P(t) and P'(t);

[0015] The expression of the actual - angle sequence A'(t) of the positioner is A'(t) = [α'1(t), β'1(t)]; obtaining the positioner angle deviation ΔA(t)=[Δα1(t), Δβ1(t)] from A'(t) and A(t); where α'1(t) represents the actual angle of the rotating axis of the positioner, β'1(t) represents the actual angle of the tilting axis of the positioner, Δα1(t) represents the deviation between the actual angle and the ideal angle of the rotating axis of the positioner, and Δβ1(t) represents the deviation between the actual angle and the ideal angle of the tilting axis of the positioner;

[0016] Constructing the calculation formula of the spatio - temporal phase coupling factor K(t) according to ΔP(t) and ΔA(t), and generating the time - varying weight matrix W(t) according to the calculation formula of the spatio - temporal phase coupling factor K(t);

[0017] Obtaining the environmental temperature - gradient data ΔT(t), fusing ΔA(t), Q(t) and ΔT(t), and calculating the compensation intensity λ(t).

[0018] Further, generating the time - varying weight matrix W(t) according to the calculation formula of the spatio - temporal phase coupling factor K(t) includes:

[0019] Substituting P(t), A(t), P'(t) and ΔA(t) into the calculation formula of the spatio - temporal phase coupling factor K(t), and calculating the spatio - temporal phase coupling factor value K i at each sampling time t i =K(t i ), forming the discrete sequence of the spatio - temporal phase coupling factor [K1, K2, …, K n1 , where t iDenote the \(i\)-th sampling moment as \(K\). i Denote the sampling moment as \(t\). i The spatio-temporal phase coupling factor value at \(t\), \(n_1\) is the total number of sampling points, \(i\) is the index of the sampling moment, and \(1\leq i\leq n_1\).

[0020] For each element \(K\) in the discrete sequence of spatio-temporal phase coupling factors \([K_1, K_2, \ldots, K]\). n1 i Perform normalization processing to obtain the normalized weight coefficient \(w\). ij (t), where \(w\). ij (t) represents the spatio-temporal coupling weight of the \(i\)-th sampling moment relative to other sampling moments.

[0021] Fill \(w\). ij (t) into an \(n_1\times n_1\) matrix to form a time-varying weight matrix \(W(t)\).

[0022] Furthermore, the fusion of \(\Delta A(t)\), \(Q(t)\) and \(\Delta T(t)\) to calculate the compensation intensity \(\lambda(t)\) includes:[[]]

[0023] Fuse \(\Delta A(t)\), \(Q(t)\) and \(\Delta T(t)\) to construct a multi-dimensional compensation vector \(V(t)=[\Delta A(t), Q(t), \Delta T(t)]\).

[0024] Extract features from \(V(t)\) and calculate the mean \(\mu\). i' , variance and skewness \(S\). i' , to obtain a statistical feature vector where \(m\) is the number of dimensions of the multi-dimensional compensation vector \(V(t)\), \(m = 3\); \(\mu\). i' is the mean of the \(i'\)-th dimension, is the variance of the \(i'\)-th dimension, \(S\). i' is the skewness of the \(i'\)-th dimension, \(1\leq i'\leq m\); \(V\). s There are a total of \(3m\) statistical features in it.

[0025] According to the statistical feature vector \(V\). s , calculate the compensation intensity \(\lambda(t)\).

[0026] Furthermore, the trajectory optimization and fault tolerance correction of \(P'(t)\) and \(A'(t)\) include:[[]]

[0027] According to the initial cooperative trajectory entropy value model \(H_0\), the time-varying weight matrix \(W(t)\) and the compensation intensity \(\lambda(t)\), perform trajectory optimization on \(P'(t)\) and \(A'(t)\) to obtain the optimized robot end pose sequence \(P\). o (t) and the optimized positioner angle sequence \(A\). o (t);

[0028] For \(P\). o ​(t) and A o Perform fault tolerance correction on (t) to obtain the robot end - pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f (t).

[0029] Furthermore, the obtained optimized robot end - pose sequence P o (t) and the optimized positioner angle sequence A o (t) include:

[0030] Input the initial collaborative trajectory entropy value model H0 into the Kalman - Fourier hybrid filter, and perform frequency - domain decomposition on P'(t) and A'(t) in combination with W(t) to obtain the spectral matrix D(f);

[0031] Perform band - pass filtering on D(f), extract the low - frequency components with frequency value f < f1 in D(f) to generate the low - frequency sub - spectral matrix D L ; Extract the high - frequency components with frequency value f > f2 in D(f) to generate the high - frequency sub - spectral matrix D H , where f1 is the preset low - frequency threshold and f2 is the preset high - frequency threshold;

[0032] Process D L and D H to generate the optimized robot end - pose sequence P o (t) and the optimized positioner angle sequence A o (t).

[0033] Furthermore, the process of processing D L and D H to generate the optimized robot end - pose sequence P o (t) and the optimized positioner angle sequence A o (t) includes:

[0034] Apply the entropy value suppression algorithm to D L to obtain the optimized spectrum D' L ;

[0035] Apply the phase - lead compensation algorithm to D H to design the feed - forward controller G(s). Based on the feed - forward controller G(s), perform phase - lead processing on the phase of the high - and medium - frequency components in D H according to the designed phase - lead amount Δφ(f), and output the compensated spectrum D' H ;

[0036] Perform inverse Fourier transform on D' L and D' H and superimpose them to generate the optimized robot end - pose sequence P o(t) and the optimized angle sequence A of the positioner o (t).

[0037] Furthermore, the obtained optimized spectrum D' L includes:

[0038] For D L Adopt the entropy value suppression algorithm, introduce λ(t) as the iteration step size, construct the trajectory entropy stability objective function J, and minimize the trajectory entropy stability objective function J by the gradient descent method. After convergence, output the optimized spectrum D'. L .

[0039] The robot-positioner PLC collaborative communication control system is used to implement the above-mentioned robot-positioner PLC collaborative communication control method. The system includes:

[0040] Model construction module: used to receive the robot end pose sequence P(t) and the positioner target angle sequence A(t), and construct the initial collaborative trajectory entropy value model H0 according to P(t) and A(t);

[0041] Calculation module: used to collect the actual pose sequence P'(t) of the robot end, the positioner servo feedback signal F(t) and the welding energy flux density parameter Q(t) in real time; extract the actual angle sequence A'(t) of the positioner according to the positioner servo feedback signal F(t); generate the time-varying weight matrix W(t) according to P(t), A(t), P'(t), Q(t) and A'(t), and calculate the compensation intensity λ(t);

[0042] Trajectory optimization module: according to the initial collaborative trajectory entropy value model H0, the time-varying weight matrix W(t) and the compensation intensity λ(t), perform trajectory optimization and fault tolerance correction on P'(t) and A'(t) to obtain the robot end pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f (t);

[0043] Instruction output module: Take P f (t) and A f (t) as the collaborative control instructions for the robot and the positioner.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] The present invention realizes high-precision closed-loop collaborative control of the robot-positioner system by constructing a collaborative trajectory entropy value model and a dynamic compensation mechanism. First, based on the time-varying weight matrix generated by the spatio-temporal phase coupling factor, the control weights of the robot pose and the positioner angle can be dynamically allocated, effectively suppressing the trajectory deviation caused by communication delay and mechanical transmission error, and synchronously improving the accuracy. Second, by fusing the welding energy flux density parameter and the environmental temperature gradient data, the compensation intensity is adaptively adjusted to solve the influence of thermal deformation on collaborative positioning and ensure the consistency of the welding penetration. In addition, a multi-dimensional fault tolerance correction algorithm is adopted to automatically switch to the optimized fault tolerance trajectory when the communication is interrupted or the servo is abnormal, avoiding equipment collision and maintaining continuous operation. This solution supports multi-protocol compatibility of cross-brand devices, the system repeat positioning accuracy reaches ±0.05mm, improves the welding speed while reducing the scrap rate, and significantly improves the quality and efficiency of complex curved surface welding. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is the principle flow chart of the robot-positioner PLC collaborative communication control method in the present invention;

[0048] Figure 2 It is the principle flow chart of optimizing the trajectories of P'(t) and A'(t) in the robot-positioner PLC collaborative communication control method of the present invention;

[0049] Figure 3 It is about P o (t) and A o (t) in the robot-positioner PLC collaborative communication control method of the present invention for fault tolerance correction;

[0050] Figure 4 It is the functional module diagram of the robot-positioner PLC collaborative communication control system in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0052] Example 1

[0053] Please refer to Figure 1 as shown in the figure. This embodiment provides a robot-positioner PLC collaborative communication control method, including:

[0054] Step S1000: Receive the robot end pose sequence P(t) and the positioner target angle sequence A(t). According to P(t) and A(t), construct the initial collaborative trajectory entropy value model H0; collect the actual robot end pose sequence P'(t), the positioner servo feedback signal F(t), and the welding energy flux density parameter Q(t) in real time; extract the actual positioner angle sequence A'(t) according to the positioner servo feedback signal F(t); generate the time-varying weight matrix W(t) according to P(t), A(t), P'(t), Q(t), and A'(t), and calculate the compensation intensity λ(t);

[0055] Furthermore, step S1000 includes:

[0056] Step S1100: Receive the robot end pose sequence P(t) = [x(t), y(t), z(t), α(t), β(t), γ(t)] and the positioner target angle sequence A(t) = [α1(t), β1(t)], where t is the time variable, x(t), y(t), and z(t) respectively represent the position coordinates of the robot end in the base coordinate system; α(t), β(t), and γ(t) respectively represent the attitude angles of the robot end around the x, y, and z axes of the base coordinate system; α1(t) represents the target angle of the positioner rotation axis, and β1(t) represents the target angle of the positioner tilting axis; construct the initial collaborative trajectory entropy value model H0 according to P(t) and A(t);

[0057] Specifically, in this step, the robot terminal pose sequence P(t) = [x(t), y(t), z(t), α(t), β(t), γ(t)] and the positioner target angle sequence A(t) = [α1(t), β1(t)] are received, where t represents the time variable, which is used to identify the motion state of the robot and the positioner at different times. In the robot terminal pose sequence, x(t), y(t), and z(t) respectively represent the position coordinates of the robot terminal in the base coordinate system. These three coordinates determine the specific position of the robot terminal in three-dimensional space. For example, in a welding scene, the spatial position of the welding head can be accurately represented; α(t), β(t), and γ(t) respectively represent the attitude angles of the robot terminal around the x, y, and z axes of the base coordinate system. These attitude angles describe the direction of the robot terminal, such as the tilt angle of the welding head. In the target angle sequence of the positioner, α1(t) represents the target angle of the positioner's rotation axis, and β1(t) represents the target angle of the positioner's flip axis. They determine the angle state that the positioner should reach at different times to coordinate the movement of the robot and achieve process requirements such as welding and assembly.

[0058] Based on the above received sequence, the initial collaborative trajectory entropy model H0 is constructed. This model is used to measure the performance indicators of the collaborative trajectory of the robot and the positioner, such as smoothness, accuracy, and stability. In practical applications, such as in the welding process of automotive parts, if the collaborative trajectory of the robot and the positioner is not stable, it may lead to welding position deviation, welding quality degradation and other problems. The initial collaborative trajectory entropy model H0 can quantitatively evaluate the performance of this collaborative trajectory, providing an important basis for subsequent optimization and adjustment. Its calculation method is usually based on information entropy theory, comprehensively considering the changes in the robot terminal posture sequence and the positioner target angle sequence in the time dimension. By analyzing the changes in posture and angle at different times, a value that can reflect the overall performance of the collaborative trajectory is obtained. For example, if the posture of the robot terminal changes too much in a short period of time, and the positioner angle adjustment cannot keep up, the entropy value calculated by the model will be large, indicating that the stability of the collaborative trajectory is poor. This model provides basic data for trajectory optimization and fault-tolerant correction in subsequent steps, enabling the entire control method to be optimized according to the actual collaborative trajectory performance, effectively improving the accuracy and reliability of the collaborative work between the robot and the positioner, thereby improving the quality and efficiency of the production process.

[0059] Step S1200, collect the actual pose sequence P'(t) of the robot end, the servo feedback signal F(t) of the positioner, the welding energy flux density parameter Q(t), and the communication interruption status signal in real time; calculate the robot pose deviation ΔP(t) from P(t) and P'(t); according to the servo feedback signal F(t) of the positioner, extract the actual angle sequence A'(t) = [α'1(t), β'1(t)] of the positioner; obtain the positioner angle deviation ΔA(t) = [Δα1(t), Δβ1(t)] from A'(t) and A(t); where, α'1(t) represents the actual angle of the rotation axis of the positioner, β'1(t) represents the actual angle of the tilting axis of the positioner, Δα1(t) represents the deviation between the actual angle and the ideal angle of the rotation axis of the positioner, and Δβ1(t) represents the deviation between the actual angle and the ideal angle of the tilting axis of the positioner.

[0060] Specifically, first, the actual pose sequence P'(t) of the robot end, the servo feedback signal F(t) of the positioner, the welding energy flux density parameter Q(t), and the communication interruption status signal are collected in real time through a multi-protocol compatible interface. The multi-protocol compatible interface is a compatible interface that can adapt to multiple communication protocols to ensure stable and accurate acquisition of various data transmitted by different devices. In an industrial production environment, devices such as robots and positioners may use different communication protocols, and the multi-protocol compatible interface can solve the compatibility problem of data transmission and ensure the integrity and accuracy of data collection. After collecting the actual pose sequence P'(t) of the robot end, the robot pose deviation ΔP(t) is calculated from P(t) and P'(t). Among them, the robot end pose sequence P(t) is the ideal trajectory, which is the pre-set motion path that the robot should follow, while P'(t) is the pose sequence collected during the actual motion process, and P'(t) = [x'(t), y'(t), z'(t), α'(t), β'(t), γ'(t)], where x'(t), y'(t), z'(t) respectively represent the actual position coordinates of the robot end in the base coordinate system; α'(t), β'(t), γ'(t) respectively represent the actual attitude angles of the robot end around the x, y, z axes of the base coordinate system. For example, during high-precision part assembly operations, if there is a deviation between the actual grasping position of the robot end and the ideal grasping position, this deviation is ΔP(t), and ΔP(t) = [Δx(t), Δy(t), Δz(t), Δα(t), Δβ(t), Δγ(t)], where Δx(t), Δy(t), Δz(t) respectively represent the deviations between the actual position and the ideal position of the robot end in the x, y, z axis directions in the base coordinate system at time t; Δα(t), Δβ(t), Δγ(t) respectively represent the deviation angles between the actual attitude and the ideal attitude of the robot end around the x, y, z axes of the base coordinate system at time t. The way to calculate this deviation is usually to subtract the corresponding coordinate values and attitude angles respectively. For example, in terms of position coordinates, Δx(t) = x'(t) - x(t), Δy(t) = y'(t) - y(t), Δz(t) = z'(t) - z(t); in terms of attitude angles, Δα(t) = α'(t) - α(t), Δβ(t) = β'(t) - β(t), Δγ(t) = γ'(t) - γ(t), and the complete robot pose deviation ΔP(t) is obtained through these calculations.

[0061] According to the servo feedback signal F(t) of the positioner, the actual angle sequence A'(t) = [α'1(t), β'1(t)] of the positioner is extracted. The servo feedback signal F(t) of the positioner is the signal fed back by the positioner servo system, which contains the actual motion state information of the positioner. The actual angle α'1(t) of the rotating axis of the positioner and the actual angle β'1(t) of the tilting axis are extracted from it, so as to obtain the actual angle sequence of the positioner. Then, according to A'(t) and A(t), the angle deviation ΔA(t) = [Δα1(t), Δβ1(t)] of the positioner is obtained. Among them, Δα1(t) represents the deviation between the actual angle and the ideal angle of the rotating axis of the positioner, that is, Δα1(t) = α'1(t) - α1(t); Δβ1(t) represents the deviation between the actual angle and the ideal angle of the tilting axis of the positioner, that is, Δβ1(t) = β'1(t) - β1(t). For example, when welding a large workpiece, the positioner needs to rotate and tilt according to the preset angle to cooperate with the robot for welding. If there is a deviation between the actual angle and the target angle, it will affect the accuracy of the welding position. By calculating these angle deviations, the motion of the positioner can be monitored in real time to see if it meets the expectations.

[0062] The welding energy flux density parameter Q(t) is an alternative measurement index for welding current / voltage, which reflects the heat input amount during the welding process. During the welding process, the changes in welding current and voltage will directly affect the heat input of welding, and the heat input is closely related to the welding quality. Sensors dedicated to measuring the welding energy flux density parameter are equipped. These sensors are based on specific physical principles and can directly sense the energy distribution during the welding process and convert it into corresponding electrical signals or digital signals for output. For example, a heat flux sensor is used. Its working principle is based on the thermoelectric effect. When the heat during the welding process is transferred to the surface of the sensor, the sensor will generate a thermoelectric potential proportional to the heat flux. By measuring and calibrating the thermoelectric potential, the welding energy flux density parameter Q(t) can be directly obtained. By monitoring Q(t), the heat input situation during the welding process can be indirectly understood. For example, when welding thin plates, if the heat input is too large, the plate may be burned through; if the heat input is too small, the welding may be insecure. Therefore, the monitoring and analysis of Q(t) help to adjust the welding process parameters in time to ensure the welding quality. The communication interruption status signal is used to judge whether the communication between the robot and the positioner is normal. In industrial production, communication interruption may cause abnormal cooperation between the robot and the positioner, leading to production accidents. By monitoring the communication interruption status signal, once communication interruption is detected, corresponding measures can be taken in time, such as starting the safe trajectory cached locally, etc., to ensure the safety of the production process.

[0063] This step acquires the deviation information between the actual motion and the ideal motion of the robot and positioner, as well as the key parameters and communication status in the welding process, by collecting and analyzing these data in real time. This information provides important data support for the subsequent construction of spatiotemporal phase coupling factors, calculation of compensation strength, and trajectory optimization and fault-tolerance correction. By timely discovering and quantifying these deviations, the motion trajectory of the robot and positioner can be adjusted in a targeted manner, the accuracy and stability of collaborative control can be improved, the smooth progress of the production process can be ensured, and the decline in production quality and safety accidents caused by motion deviations and communication problems can be effectively avoided, thereby improving the reliability and production efficiency of the entire production system.

[0064] Step S1300, construct a calculation formula for the space-time phase coupling factor K(t) based on ΔP(t) and ΔA(t), and generate a time-varying weight matrix W(t) based on the calculation formula for the space-time phase coupling factor K(t).

[0065] Further, step S1300 includes:

[0066] Step S1310, constructing a calculation formula for the spatiotemporal phase coupling factor K(t) according to ΔP(t) and ΔA(t);

[0067] Specifically, the calculation formula of the space-time phase coupling factor K(t) is:

[0068]

[0069] in:

[0070] The L2 norm of the robot's posture deviation is expressed by taking the square root of the sum of the components of the robot's posture deviation in each direction (including position deviation and attitude deviation) to quantify the degree of deviation between the robot's actual motion and the target trajectory. The larger the value, the greater the difference between the robot's actual motion trajectory and the ideal trajectory.

[0071] Δt is the communication delay between the robot and the positioner, which can be measured through network time synchronization protocols such as NTP (Network Time Protocol) and PTP (Precision Time Protocol). In actual industrial control scenarios, data is transmitted between the robot and the positioner through the network. Due to the delay in network transmission, this delay will affect the accuracy of their coordinated motion. For example, if the motion command sent by the robot cannot be received by the positioner in time, the two will move out of sync. Through these time synchronization protocols, the communication delay time Δt can be accurately measured.

[0072] τ(t) is the response lag time of the positioner servo system, which can be estimated through the built-in parameters of the servo driver or identification algorithms. After the positioner's servo system receives a control signal, due to factors such as the inertia of the system itself and the mechanical structure, it will not respond immediately but has a certain lag. For example, when the control signal requires the positioner to rotate to a certain angle, the servo system needs a certain amount of time to drive the positioner to reach the target angle, and this lag time is τ(t). By obtaining this parameter, the motion characteristics of the positioner can be described more accurately.

[0073] When the pose deviation of the robot is large, the angular deviation of the positioner is large, the communication delay is small, and the servo response is fast, K(t) will obtain a large value. This is because the greater the deviation between the robot and the positioner, the more attention needs to be paid to their collaborative relationship; while a small communication delay and a fast servo response mean that the system responds more promptly to the deviation. At this time, a larger K(t) can more prominently reflect the tightness of this collaborative relationship. The spatio-temporal phase coupling factor K(t) constructed by this formula can quantify the spatio-temporal coupling degree of the robot and positioner movements, providing an important basis for generating the time-varying weight matrix later. Its beneficial effect lies in that by comprehensively considering factors such as the motion deviations of the robot and positioner, communication delay, and positioner servo response, the collaborative motion relationship between them can be described more accurately. This helps to reasonably allocate weights according to this quantified relationship during the subsequent trajectory optimization process, improving the precision of the collaborative motion of the robot and positioner, and thus meeting industrial production tasks with high precision requirements such as welding and assembly. During the welding process, if the collaborative relationship between the robot and the positioner can be grasped more precisely, the accuracy of the welding position can be ensured and the welding quality can be improved.

[0074] Step S1320, substitute P(t), A(t), P'(t), and ΔA(t) into the calculation formula of the spatio-temporal phase coupling factor K(t), and calculate the spatio-temporal phase coupling factor value K at each sampling time t i of i = K(t i ), forming a discrete sequence of spatio-temporal phase coupling factors [K1, K2,..., K n1 , where t i represents the i-th sampling time, K i represents the spatio-temporal phase coupling factor value at the sampling time t i , n1 is the total number of sampling points, i is the index of the sampling time, and 1 ≤ i ≤ n1;

[0075] Specifically, sample the motion states of the robot and the positioner at a certain time interval. Assume the sampling period is ΔT s , and at each sampling time t i(i = 1, 2, …, n1), respectively obtain the ideal pose P(t i ) and the actual pose P'(t i ) of the robot end, calculate the deviation ΔP(t i ) between P(t i ) and P'(t i ), obtain the target angle A(t i ) and the actual angle A'(t i ) of the positioner, and calculate the deviation ΔA(t i ) between A(t i ) and A'(t i ) = [Δα1(t i ), Δβ1(t i )], where Δα1(t i ) represents the deviation between the actual angle and the ideal angle of the rotating axis of the positioner corresponding to the sampling time t i , and Δβ1(t i ) represents the deviation between the actual angle and the ideal angle of the tilting axis of the positioner corresponding to the sampling time t i . Then substitute these data into the formula in step S1310 to calculate the spatio-temporal phase coupling factor value K i at each sampling time. For example, at the 1st sampling time t1, K1 is calculated; at the 2nd sampling time t2, K2 is calculated, and so on, finally forming a discrete sequence [K1, K2, …, K n1 . The purpose of this step is to obtain the specific values of the spatio-temporal coupling degree of the robot and the positioner movements at different sampling times, providing a data basis for subsequent normalization processing and generating a time-varying weight matrix. Its beneficial effect is that by calculating at each sampling time, it can comprehensively and meticulously reflect the collaborative changes of the robot and the positioner during the entire movement process. In actual production, the movement states of the robot and the positioner are constantly changing, and this discrete sequence can accurately record these changes, providing detailed data support for subsequent analysis and optimization. For example, in the assembly process of automotive parts, by analyzing this discrete sequence, it can be found which moments have better collaborative effects between the robot and the positioner and which moments have problems, so as to make targeted adjustments to improve the assembly efficiency and quality.

[0076] Step S1330, normalize each element K n1 in the spatio-temporal phase coupling factor discrete sequence [K1, K2, …, K i to obtain the normalized weight coefficient w ij (t) represents the spatio-temporal coupling weight of the i-th sampling time relative to other sampling times, and K j is for the sampling time t jvalue of the spatio-temporal phase coupling factor, where j is the index of the sampling moment, 1 ≤ j ≤ n1;

[0077] Specifically, the essence of the normalization process is to convert numerical values of different magnitudes to a unified scale range for easier comparison and analysis. In this embodiment, since the K values at different sampling moments i may vary significantly, directly using these raw values will result in unreasonable weight distribution. Through normalization, the sum of all normalized weight coefficients w ij (t) is 1. The purpose of this step is to more reasonably distribute the weights of different sampling moments in generating the time-varying weight matrix, so that the weight of each sampling moment can reflect its relative importance in the entire motion process. The beneficial effect is that the weight coefficients obtained through normalization can more scientifically measure the contribution degree of the spatio-temporal coupling relationship between the robot and the positioner's motion at different sampling moments to the overall cooperative control. When generating the time-varying weight matrix subsequently, the weights can be adjusted more accurately according to these weight coefficients, improving the rationality and effectiveness of the time-varying weight matrix. For example, when the robot and the positioner cooperate in welding a complex curved surface, the cooperation between the robot and the positioner at certain sampling moments has a greater impact on the welding quality. Through the weight coefficients after normalization, these moments can be given greater weights, so that more attention can be paid to the cooperation effect at these critical moments during trajectory optimization, further improving the welding quality.

[0078] Step S1340, fill w ij (t) into an n1×n1 matrix to form the time-varying weight matrix W(t).

[0079] Specifically, when constructing the matrix, using the sampling moment as the index of rows and columns, place the normalized weight coefficient w ij (t) of each sampling moment at the corresponding matrix position (i, j). For example, if there are 3 sampling moments, then the time-varying weight matrix W(t) is a 3×3 matrix, where the matrix element W 11 = w 11 (t), W 12 = w 12(t), and so on. The time-varying weight matrix W(t) can comprehensively reflect the spatio-temporal coupling weight relationship between the robot and the positioner at different sampling times. In the subsequent trajectory optimization process, this matrix can be used as the basis for weight allocation. For example, in the process of the robot and the positioner collaborating to carry heavy objects, the influence of motion deviations at different times on the handling stability is different. The time-varying weight matrix can, based on these differences, allocate appropriate weights to the motions at different times, enabling more targeted adjustment of the motion trajectories of the robot and the positioner during trajectory optimization, and improving the stability and accuracy of the handling process. Its beneficial effect lies in that by constructing the time-varying weight matrix, an effective weight allocation method is provided for subsequent trajectory optimization. It can quantify and integrate the collaborative motion relationship between the robot and the positioner at different sampling times, such that when optimizing the trajectory, the motions of the robot and the positioner can be adjusted more reasonably according to these weights, improving the accuracy and effect of collaborative control, thereby better meeting the requirements for the collaborative work of the robot and the positioner in industrial production and ensuring the smooth progress of the production process.

[0080] Step S1300 provides an important quantitative basis and weight allocation method for the collaborative control of the robot-positioner by constructing a spatio-temporal phase coupling factor calculation formula, calculating its discrete sequence, performing normalization processing, and generating a time-varying weight matrix, which helps improve the accuracy and effect of collaborative motion, solves the problem that it is difficult to guarantee motion accuracy when the robot and the positioner collaborate in actual industrial production, and lays a solid foundation for subsequent trajectory optimization and stable operation of the system.

[0081] Step S1400, obtain the environmental temperature gradient data ΔT(t), fuse ΔA(t), Q(t) and ΔT(t), and calculate the compensation intensity λ(t).

[0082] Furthermore, step S1400 includes:

[0083] Step S1410, obtain the environmental temperature gradient data ΔT(t), fuse ΔA(t), Q(t) and ΔT(t), and construct a multi-dimensional compensation vector V(t) = [ΔA(t), Q(t), ΔT(t)];

[0084] Specifically, the environmental temperature gradient data reflects the changes of the environmental temperature over time and space. In industrial production scenarios, temperature changes can affect the moving parts of robots and positioners. For example, thermal expansion and contraction may cause dimensional changes in the mechanical structure, thereby affecting the motion accuracy. The way to obtain this data is to arrange temperature sensors in the working environment. The sensors process and calculate the real-time monitored temperature data to obtain the temperature gradient value ΔT(t) at different times. Then, by fusing ΔA(t), Q(t), and ΔT(t), a multi-dimensional compensation vector V(t) = [ΔA(t), Q(t), ΔT(t)] is constructed. The angle deviation ΔA(t) of the positioner reflects the difference between the actual motion angle and the target angle of the positioner. As mentioned above, it reflects the degree to which the motion of the positioner lags behind the target angle and has a direct impact on the collaborative accuracy of the robot and the positioner. The welding energy flux density parameter Q(t), as an alternative measurement index for welding current / voltage, reflects the heat input during the welding process. Changes in welding heat input can affect the welding quality of the workpiece and may also indirectly affect the motion control of the robot and the positioner. For example, thermal stress may cause workpiece deformation, thereby affecting the relative position between the robot end effector and the workpiece. Combining these three parameters into a multi-dimensional compensation vector can comprehensively consider the influence of various factors on the collaborative motion. For example, on an automotive body welding production line, when the environmental temperature gradient is large, the changes in the angle deviation of the positioner and the welding energy flux density may be coupled with each other, jointly affecting the welding quality and the collaborative effect of the robot and the positioner. By constructing a multi-dimensional compensation vector, integrating these factors provides a comprehensive data set for subsequent analysis and processing, helps to more accurately evaluate the current operating state, provides richer and more accurate information for calculating the compensation intensity, thereby improving the adaptability to complex working conditions, enhancing the control accuracy of the collaborative motion of the robot and the positioner, and ensuring the quality stability of processes such as welding.

[0085] Step S1420: Extract features from V(t), and calculate the mean μ of each dimension i' , variance and skewness S i' , to obtain the statistical feature vector where m is the number of dimensions of the multi-dimensional compensation vector V(t), m = 3; μ i' is the mean of the i'-th dimension, is the variance of the i'-th dimension, S i' is the skewness of the i'-th dimension, 1 ≤ i' ≤ m; There are 3m statistical features in V s ;

[0086] Specifically, calculate the mean μ i' , taking the dimension of the angle deviation of the positioner as an example, the mean is the average value of all sampling data of this dimension over a period of time, and its calculation formula is (The sampling time is t k , k = 1, 2, …, n1), Δα1(t k ) represents the deviation between the actual angle and the ideal angle of the rotary axis of the positioner at the sampling time t k . The mean value reflects the average level of the angle deviation of the positioner. By calculating the mean value, the overall trend of the angle deviation of the positioner over a period of time can be understood, and whether there is a systematic deviation can be judged. For example, if the mean value of the angle deviation of the positioner is large, it indicates that there is a large average deviation between the actual angle and the target angle of the positioner during this period, and its motion control needs to be adjusted. The variance measures the degree of dispersion of the data. Still taking the dimension of the angle deviation of the positioner as an example, the calculation formula of the variance is . The larger the variance, the higher the degree of dispersion of the data, that is, the greater the fluctuation of the angle deviation of the positioner. For example, during the welding process, if the variance of the angle deviation of the positioner is large, it means that its angle change is unstable, which may have an adverse impact on the welding quality. By analyzing the variance, this unstable situation can be discovered in time, providing a basis for subsequent adjustment of the control strategy.

[0087] The skewness S i' is used to describe the asymmetry of the data distribution. For the dimension of the angle deviation of the positioner, its skewness adopts a standardized statistical formula . In this formula, (n1 - 1)(n1 - 2) is to make the skewness have better properties in the statistical sense , is to standardize each data point to eliminate the influence of the dimension , then further emphasizes the asymmetry of the data. Finally, for from k = 1 to n1, sum them up and multiply by to obtain the skewness value of the angle deviation dimension of the positioner. The skewness value can reflect whether the data distribution is skewed to the left or right, and the degree of deviation from the symmetric distribution, thus helping to analyze the data distribution characteristics and discover abnormal situations. For example, if the skewness is positive, it means that the right side (the larger value side) of the data distribution has a longer tail, that is, the probability of a larger angle deviation of the positioner is relatively high; if the skewness is negative, the left side (the smaller value side) has a longer tail. By analyzing the skewness, the characteristics of the data distribution can be further understood, and abnormal situations that may exist in the data can be discovered.

[0088] Calculate the mean value, variance and skewness for the three dimensions (angle deviation of the positioner, welding energy flux density, environmental temperature gradient) of the multi-dimensional compensation vector V(t) respectively, and obtain a statistical feature vector V containing 3m = 9 statistical features sThis process can quantitatively analyze the data in the multi-dimensional compensation vector from multiple perspectives and extract the key features of the data. Through these features, the variation laws and interrelationships of various influencing factors during the collaborative movement of the robot and the positioner can be understood more deeply, providing more accurate data support for accurately calculating the compensation intensity in the subsequent steps. For example, in actual production, by observing the statistical feature vector, it can be determined which factors change relatively stably and which factors have large fluctuations, so as to carry out targeted control and adjustment, improve the stability and reliability of the collaborative movement of the robot and the positioner, and ensure the smooth progress of the production process.

[0089] Step S1430: Calculate the compensation intensity λ(t) according to the statistical feature vector V s .

[0090] Specifically, according to the statistical feature vector V s , the compensation intensity λ(t) is calculated through the non-linear mapping function . λ(t) is used for subsequent trajectory optimization; V s,j' is the j'-th feature in the statistical feature vector V s , and γ j' is the weight of V s,j' . The heuristic algorithm is an optimization algorithm based on experience and intuition. It searches heuristically in the solution space to find an approximate optimal solution. In this embodiment, the heuristic algorithm is used to determine the weight γ j' because the influence degrees of the various features in the statistical feature vector on the compensation intensity are different, and this influence relationship is relatively complex, making it difficult to determine the weight through a simple mathematical formula. For example, under different welding processes and working environments, the mean, variance, and skewness of the angle deviation of the positioner, as well as the corresponding statistical features of the welding energy flux density and the environmental temperature gradient, have different influence weights on the collaborative movement of the robot and the positioner. The heuristic algorithm can continuously adjust the weight according to the actual production data and experience, making the calculated compensation intensity more in line with the actual requirements. In the actual collaborative work of the robot and the positioner, this method of accurately calculating the compensation intensity can dynamically adjust the movement trajectories of the robot and the positioner according to the real-time system state, improve the accuracy and stability of the collaborative movement, avoid problems such as welding defects and assembly errors caused by movement deviations, improve the production quality and efficiency, and meet the requirements for high-precision collaborative control in industrial production.

[0091] Step S2000: According to the initial collaborative trajectory entropy value model H0, the time-varying weight matrix W(t), and the compensation intensity λ(t), perform trajectory optimization and fault tolerance correction on P'(t) and A'(t) to obtain the robot end-effector pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f(t); Take P f (t) and A f (t) as the cooperative control instructions for the robot and the positioner.

[0092] Furthermore, step S2000 includes:

[0093] Step S2100, according to the initial cooperative trajectory entropy value model H0, the time-varying weight matrix W(t), and the compensation intensity λ(t), perform trajectory optimization on P'(t) and A'(t) to obtain the optimized robot end-effector pose sequence P o (t) and the optimized positioner angle sequence A o (t);

[0094] The core purpose of step S2100 is to perform trajectory optimization on the actual robot end-effector pose sequence P'(t) and the actual positioner angle sequence A'(t) according to the initial cooperative trajectory entropy value model H0, the time-varying weight matrix W(t), and the compensation intensity λ(t), so as to obtain a motion trajectory that better meets the actual requirements, thereby improving the accuracy and stability of the cooperative motion of the robot and the positioner. This step is a key link in realizing the precise cooperative control of the robot and the positioner, effectively solving the problem of optimizing the motion trajectories of the robot and the positioner under complex working conditions, and ensuring that their cooperative motion can meet the requirements of high-precision operations such as welding and assembly.

[0095] Furthermore, as Figure 2 shown, step S2100 includes:

[0096] Step S2110, input the initial cooperative trajectory entropy value model H0 into the Kalman-Fourier hybrid filter, and perform frequency-domain decomposition on P'(t) and A'(t) in combination with W(t) to obtain the spectral matrix D(f);

[0097] Specifically, the initial collaborative trajectory entropy value model H0 is constructed based on the robot end - pose sequence P(t) and the positioner target angle sequence A(t), and is used to measure performance indicators such as the smoothness, accuracy, and stability of the collaborative trajectory of the robot and the positioner. In this step, it serves as an important input parameter, providing information about the overall characteristics of the collaborative trajectory for frequency - domain decomposition. The Kalman - Fourier hybrid filter combines the advantages of Kalman filtering and Fourier transform. Kalman filtering is an algorithm that uses the linear system state equation to optimally estimate the system state through system input - output observation data. In this embodiment, it can effectively process the noisy motion trajectory data of the robot and the positioner, remove noise interference, and improve the accuracy of the data. The Fourier transform converts the time - domain signal into a frequency - domain signal, enabling the analysis of signal characteristics from the frequency perspective. Through the Fourier transform, the robot end - pose sequence and the positioner target angle sequence can be transformed from the time domain to the frequency domain, revealing the role of different frequency components in the trajectory change.

[0098] The time - varying weight matrix W(t) reflects the spatio - temporal coupling weight relationship of the robot and the positioner movements at different sampling moments. During the frequency - domain decomposition process, it weights the trajectory sequences of the robot and the positioner, highlighting those moments and factors that have a greater impact on the collaborative motion, so that the decomposed spectral matrix D(f) can more accurately reflect the actual collaborative motion characteristics. Taking the welding process of automotive parts as an example, the collaborative motion trajectory of the robot and the positioner is complex and has high precision requirements. At a certain moment, the movement of the robot may be slightly deviated due to external interference, and at the same time, the angle adjustment of the positioner may also be delayed. When the Kalman - Fourier hybrid filter processes this data, the Kalman filtering part can denoise the noisy motion data of the robot and the positioner, reducing the influence of interference factors on the analysis results. The Fourier transform part converts the denoised time - domain data into frequency - domain data, showing the contribution of different frequency components in the trajectory change. The time - varying weight matrix W(t) weights the data at different moments according to the previously calculated spatio - temporal phase coupling factor, making the spectral matrix D(f) more accurately reflect the frequency characteristics of the actual collaborative motion.

[0099] The spectral matrix D(f) obtained through this step is obtained by transforming H0 through a Kalman-Fourier hybrid filter and combining it with the weighting of W(t). It reflects the amplitude and phase characteristics of the trajectory sequence at different frequency components. These characteristics are crucial for subsequent analysis of the collaborative motion state of the robot and the positioner, and provide key frequency-domain information for further optimizing the trajectory. Its beneficial effect is that through frequency-domain decomposition, complex time-domain trajectory data is converted into more easily analyzable frequency-domain information, enabling a clear understanding of the degree of influence of different frequency components on the collaborative motion. This helps to discover potential motion problems. For example, vibrations at certain frequencies may lead to a decrease in welding quality, and thus provide a basis for subsequent targeted trajectory optimization, improving the accuracy and stability of the collaborative motion of the robot and the positioner, and ultimately enhancing the welding quality and production efficiency.

[0100] Step S2120: Perform band-pass filtering on D(f), extract the low-frequency components in D(f) with frequency values f < f1, and generate a low-frequency sub-spectral matrix D L ; extract the high-frequency components in D(f) with frequency values f > f2, and generate a high-frequency sub-spectral matrix D H , where f1 is a preset low-frequency threshold and f2 is a preset high-frequency threshold;

[0101] Specifically, band-pass filtering is a filtering technique that allows signals within a specific frequency range to pass through while suppressing or attenuating other frequency signals. In this embodiment, by setting a low-frequency threshold f1 (such as 10 Hz) and a high-frequency threshold f2 (such as 100 Hz), components in different frequency ranges are extracted from the spectral matrix D(f). The elements in each sub-spectral matrix contain amplitude and phase information, which respectively reflect the energy magnitude and phase relationship of the corresponding frequency components in the collaborative motion trajectory of the robot and the positioner. Taking the welding operation of large structural parts by the robot and the positioner as an example, the low-frequency components may mainly reflect the overall motion trend and slow-changing parts of the robot and the positioner, such as the movement of the robot along a specific path and the overall angle adjustment of the positioner. The high-frequency components may be related to the micro-vibrations and rapid response actions during the motion of the robot and the positioner. By decomposing the spectral matrix into low-frequency and high-frequency sub-spectral matrices through band-pass filtering, the motion components with different frequency characteristics can be analyzed and processed separately.

[0102] The purpose of this step is to separate the frequency components that have different effects on the collaborative motion of the robot and the positioner, so as to adopt different optimization strategies according to the characteristics of each frequency component subsequently. Its beneficial effects are manifold. On the one hand, for the low-frequency components, it is usually related to the macroscopic motion of the robot and the positioner. Analyzing the low-frequency sub-spectral matrix D LIt is possible to understand whether the basic trend of the coordinated motion conforms to expectations and whether there are systematic deviations. For example, if there are abnormal changes in the amplitude of the low-frequency components, it may indicate problems with the overall motion planning of the robot or positioner, and adjustments are required. On the other hand, high-frequency components are often related to the details and rapidly changing parts of the motion. The high-frequency sub-spectrum matrix D H can help detect minor vibrations or unstable factors during the motion. In welding operations, high-frequency vibrations may lead to a decrease in welding quality. By analyzing the high-frequency sub-spectrum matrix, these high-frequency vibrations can be detected in a timely manner and measures can be taken to suppress them, thereby improving the welding quality and the stability of the coordinated motion. By separating the low-frequency and high-frequency components, more detailed and accurate data support is provided for subsequent targeted optimization of the motion trajectories of the robot and positioner, which helps improve the accuracy and reliability of the coordinated motion and meet the requirements of high-precision operations.

[0103] Step S2130, for D L Adopt the entropy value suppression algorithm, introduce λ(t) as the iteration step size, construct the trajectory entropy stability objective function J, and minimize the trajectory entropy stability objective function J by the gradient descent method. After convergence, output the optimized spectrum D' L ;

[0104] Specifically, the entropy value suppression algorithm (ESS) is an algorithm for optimizing signal features and is used to process the low-frequency sub-spectrum matrix D in this step L . Entropy is used in information theory to measure the uncertainty of information. In the field of signal processing, spectral entropy can reflect the uniformity of the signal frequency distribution. For the low-frequency sub-spectrum matrix D L , the larger the spectral entropy value, the more dispersed the frequency distribution of the low-frequency signal, that is, the more unstable the motion state. The construction of the trajectory entropy stability objective function J combines the spectral entropy H(D L ) and the regularization term λ(t)×R(D L ), J = H(D L ) + λ(t)×R(D L ). The spectral entropy H(D L ) is used to measure the uncertainty of the low-frequency sub-spectrum matrix, that is, the stability of the motion state; the regularization term λ(t)×R(D L ) plays a role of constraint and adjustment, where the compensation intensity λ(t) is used as the step size during the iteration process to control the degree and direction of the optimization.

[0105] The gradient descent method is a commonly used optimization algorithm. It iteratively approaches the minimum value of the objective function by continuously moving along the negative gradient direction of the objective function. In this step, the gradient descent method is used to minimize the trajectory entropy stability objective function J. During the iteration process, the low-frequency sub-spectrum matrix D is adjusted according to the magnitude and direction of the gradient Lparameters, the spectral entropy value gradually decreases, that is, the motion state becomes more stable. Taking the robot and the positioner in the complex curved surface welding scenario as an example, during the welding process, the low-frequency motion of the robot and the positioner may be affected by external factors (such as the irregular shape of the workpiece, welding thermal stress, etc.) and become unstable. Through the entropy value suppression algorithm and the construction of the trajectory entropy stability objective function, combined with the gradient descent method for optimization. Assume that at a certain moment, the low-frequency sub-spectrum matrix D L has a large spectral entropy value, indicating that the low-frequency motion state is unstable at this time, and there may be problems such as robot motion path deviation or inaccurate positioner angle adjustment. The compensation intensity λ(t) will be adjusted according to factors such as the current environmental temperature gradient, positioner angle deviation, and welding energy flux density. If the environmental temperature changes greatly, resulting in an increase in welding thermal stress, which in turn affects the motion stability of the robot and the positioner, λ(t) will increase accordingly, increasing the adjustment intensity of the trajectory entropy stability objective function. Under the action of the gradient descent method, the parameters of the low-frequency sub-spectrum matrix are gradually adjusted to make the spectral entropy value decrease and the motion state more stable, and finally the optimized spectrum D' L .

[0106] The purpose of this step is to improve the stability of the low-frequency cooperative motion of the robot and the positioner by optimizing the low-frequency sub-spectrum matrix. The beneficial effect is that by minimizing the trajectory entropy stability objective function, the unstable factors in the low-frequency motion can be effectively suppressed, making the low-frequency motion of the robot and the positioner more stable and accurate. In the welding operation, the stable low-frequency motion helps to ensure the accuracy of the welding path, improve the welding quality, and reduce the generation of welding defects. At the same time, by introducing the compensation intensity λ(t) as the iteration step size, the influence of environmental factors and other relevant parameters on the motion stability is fully considered, making the optimization process more adaptive and accurate, further improving the reliability and stability of the cooperative motion of the robot and the positioner, and meeting the high-precision operation requirements under complex working conditions.

[0107] Step S2140, for D H adopt the phase lead compensation algorithm, design the feedforward controller G(s), and based on the feedforward controller G(s), according to the designed phase lead amount Δφ(f), perform phase lead processing on the phase of the high-frequency components in D H , and output the compensated spectrum D' H ;

[0108] Specifically, the phase lead compensation algorithm (PEC) aims to improve the performance in the high-frequency band and is mainly used to process the high-frequency part of the cooperative motion trajectory of the robot and the positioner in this embodiment. The feedforward controller G(s) is the key tool to implement this algorithm, and its expression is s is the Laplace variable, which is used to construct the dynamic characteristics of the controller. Through Laplace transform, the control problem in the time domain is transformed into the complex frequency domain for analysis and design, making the system analysis more convenient. In this controller, K p is the proportional coefficient, which directly affects the response amplitude of the controller to the input signal. For example, during the coordinated movement of the robot and the positioner, if the amplitude of high-frequency vibration is large, appropriately increasing K p can make the controller respond more strongly to high-frequency signals, thus more effectively suppressing high-frequency vibration. K d is the differential coefficient, which reflects the sensitivity of the controller to the change rate of the input signal and is used to improve the dynamic performance. When the motion state of the robot and the positioner changes rapidly, a larger K d can make the controller respond to this change faster and enhance stability. T d is the time constant, which determines the action intensity and time characteristics of the differential link in the controller and affects the compensation effect for high-frequency phase. These parameters are determined by the Z-N stability criterion to ensure stable operation. The Z-N stability criterion is a criterion for judging control stability. Through this criterion, the value range of the controller parameters can be determined to ensure that no unstable situation occurs when performing phase lead processing on high-frequency components.

[0109] The designed phase lead Δφ(f) is calculated by the formula Δφ(f) = arctan(2πf×T d ), where f is the frequency value. This formula represents the phase lead generated by the differential link of the controller G(s) at the frequency f. By performing phase lead processing on the phase of high-frequency components, the phase lag caused by inherent characteristics or external interference can be compensated, thereby suppressing high-frequency oscillation. Taking the precision assembly operation of the robot and the positioner as an example, during the assembly process, the rapid movement of the robot and the positioner may cause high-frequency vibration, affecting the assembly accuracy. Through the phase lead compensation algorithm, a suitable feedforward controller G(s) is designed. Suppose that at a certain high-frequency band, the movement of the robot and the positioner shows phase lag, resulting in a decrease in assembly accuracy. According to the calculated phase lead Δφ(f), the phase of the high-frequency components in the high-frequency sub-spectrum matrix D H is processed with phase lead using the feedforward controller. In this process, the proportional coefficient K p , the differential coefficient K d and the time constant T d are determined according to the Z-N stability criterion to ensure stable operation. After the phase lead processing, the output compensation spectrum D' H is obtained, making the movement of the robot and the positioner more stable in the high-frequency band and reducing the impact of high-frequency vibration on the assembly accuracy.

[0110] The purpose of this step is to suppress high-frequency oscillations in the collaborative motion of the robot and the positioner by performing phase lead compensation on the high-frequency sub-spectrum matrix, thereby improving the accuracy and stability of the motion. The beneficial effect is that in actual industrial production, high-frequency oscillations often lead to problems such as increased equipment wear and decreased product quality. Through the processing of this step, high-frequency oscillations can be effectively suppressed, the accuracy of the robot and positioner motion can be improved, and the product quality can be guaranteed. In precision assembly operations, reducing high-frequency vibrations can improve the accuracy of assembly, reduce the scrap rate, and increase production efficiency and economic benefits. At the same time, by reasonably designing the feedforward controller and determining the phase lead amount, the stability in the high-frequency band is ensured, and the application range of the robot and positioner under complex working conditions is broadened.

[0111] Step S2150, perform the inverse Fourier transform on D' L and D' H to generate the optimized robot end-effector pose sequence P o (t) and the optimized positioner angle sequence A o (t) by superposition.

[0112] Specifically, the inverse Fourier transform is the inverse operation of the Fourier transform and plays a key role in converting frequency-domain information back to the time domain in this step. In the previous steps, after performing band-pass filtering on the spectrum matrix D(f), processing the low-frequency components using the entropy suppression algorithm, and processing the high-frequency components using the phase lead compensation algorithm, the optimized low-frequency sub-spectrum matrix D' L and the compensated high-frequency sub-spectrum matrix D' H are obtained, and these matrices contain the optimized frequency-domain information. The inverse Fourier transform reconverts this frequency-domain information back to the time domain and restores the time-domain signals corresponding to the robot end-effector pose and the positioner angle. In this embodiment, each element in D' L and D' H is calculated according to the inverse Fourier transform formula to obtain the time-domain sequences related to the robot end-effector pose and the positioner angle respectively. Then, these two time-domain sequences are superimposed to generate the optimized robot end-effector pose sequence P o (t) and the optimized positioner angle sequence A o (t). This superposition is based on the signal synthesis principle, integrating the optimization results of the low-frequency and high-frequency components to obtain the final motion trajectory that comprehensively considers different frequency characteristics.

[0113] Taking the robot and positioner in the automotive body welding operation as an example, after the processing of the previous steps, the optimized low-frequency sub-spectrum matrix D' L optimizes the overall motion trend of the robot and the positioner, making their motion smoother and meeting the basic requirements of the welding process; the compensated high-frequency sub-spectrum matrix D' HThe high-frequency vibration and phase lag problems are compensated, improving the motion accuracy. Through the inverse Fourier transform, these frequency-domain optimization results are converted back to the time domain and superimposed to generate the optimized motion trajectory. During the welding process, the optimized robot end-effector pose sequence P o (t) and the positioner angle sequence A o (t) can ensure that the welding torch always maintains an accurate relative position relationship with the welding position, improving the welding quality and reducing welding defects such as incomplete fusion and missed welding.

[0114] The purpose of this step is to convert the frequency-domain optimization results back to the time domain to obtain the optimized robot end-effector pose and positioner angle sequences, achieving the optimization of the robot and positioner motion trajectories. The beneficial effect is that by comprehensively considering the optimization of low-frequency and high-frequency components, the coordinated motion trajectories of the robot and positioner are more accurate and stable. In industrial production, such optimized motion trajectories can improve production efficiency, reduce production costs, and enhance product quality. For example, in the automotive manufacturing industry, precise coordinated motion of the robot and positioner can improve the welding quality of the car body, reduce subsequent repair work, and improve the overall quality and production efficiency of the car. At the same time, the optimized motion trajectory also helps to extend the service life of the equipment, reduce equipment wear and failures, and improve the reliability and stability of the production system.

[0115] Step S2200, perform fault tolerance correction on P o (t) and A o (t) to obtain the robot end-effector pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f (t);

[0116] In the entire robot-positioner PLC coordinated communication control method, step S2200 is a key link for performing fault tolerance correction on the optimized robot end-effector pose sequence P o (t) and the positioner angle sequence A o (t). Due to the existence of many uncertain factors in the actual industrial production environment, such as communication failures and equipment vibrations, these factors may cause deviations in the motion trajectories of the robot and positioner, affecting the normal progress of production. This step aims to ensure that even in the event of abnormal situations, the robot and positioner can still maintain relatively accurate coordinated motion, avoiding problems such as collisions and machining defects, ensuring the safety and stability of the production process, and effectively solving the fault tolerance problem in the robot-positioner coordinated motion.

[0117] Furthermore, as Figure 3 shown, step S2200 includes:

[0118] Step S2210, set a dynamic fault tolerance threshold, where the dynamic fault tolerance threshold includes a communication interruption duration threshold T max and a pose deviation threshold ΔP max ;

[0119] Specifically, the dynamic fault tolerance threshold is a key parameter preset according to the actual requirements of the collaborative work between the robot and the positioner and the device performance. The communication interruption duration threshold T max is used to judge the impact degree of the communication interruption on the operation. For example, in an automotive parts welding production line, if the communication interruption time between the robot and the positioner is too long, it will cause the asynchronous movement of the two, seriously affecting the welding quality and even possibly damaging the equipment. According to the production process requirements and the response characteristics of the device, a suitable T max value is determined through multiple tests and analyses to ensure that there is enough time for adjustment and recovery when the communication interruption time does not exceed this threshold, avoiding serious impacts on production. The pose deviation threshold ΔP max is used to measure the allowable deviation range between the actual pose of the robot end and the ideal pose. In precision assembly operations, the robot needs to accurately install parts to the specified positions. Excessive pose deviation will lead to assembly failure or product quality decline. By analyzing the precision requirements of the assembly process and combining factors such as the motion precision and positioning error of the robot, a reasonable ΔP max value is determined. These two thresholds provide a quantitative basis for subsequent judgment of whether an abnormality occurs and what kind of fault tolerance measures to take. The beneficial effect is that the clear threshold setting can identify abnormal situations in a timely and accurate manner. When the actual operation parameters exceed the threshold, a quick response can be made, and corresponding fault tolerance strategies can be taken to ensure the accuracy and stability of the collaborative movement of the robot and the positioner, reduce production accidents and product quality problems caused by abnormal situations, and improve the reliability and production efficiency of the production system.

[0120] Step S2220, according to the communication interruption status signal, obtain the communication interruption duration ΔT. If ΔT>T max , then start the locally cached safe trajectory H s to replace P o (t) and A o (t), and output the robot end pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f (t);

[0121] The obtaining of the communication interruption duration ΔT according to the communication interruption status signal includes:

[0122] Assume that the robot sends heartbeat packets to the positioner at a period T h and receives ACK confirmation signals. If N consecutive packets are lostloss If an ACK is received, it is determined that the communication interruption has started, and the starting time t of the interruption is recorded. start ;

[0123] Embed the reception timestamp in the servo feedback signal F(t) of the positioner, and obtain the time t when the ACK signal resumes reception according to the reception timestamp. recover According to t start and t recover Calculate ΔT.

[0124] Specifically, obtain the communication interruption duration ΔT according to the communication interruption status signal, and determine whether to start the safety trajectory H cached locally based on the comparison result between this duration and the communication interruption duration threshold T max to ensure the safety of the collaborative movement of the robot and the positioner in case of communication anomalies and ensure the stability of the production process. In an industrial automation production environment, data is transmitted between the robot and the positioner through a network to achieve collaborative work. To monitor the communication status in real time, the robot sends heartbeat packets to the positioner at a period T s . A heartbeat packet is a special network data packet, which is similar to a regularly sent "signal" used to inform the positioner that the robot is working properly and the communication link is active. In this embodiment, by periodically sending heartbeat packets, the positioner can determine whether the communication with the robot is normal. If the positioner continuously receives heartbeat packets, it indicates that the communication link is normal; otherwise, if no heartbeat packet is received within a certain period of time, there may be a communication problem. h When the positioner receives the heartbeat packet sent by the robot, it returns an ACK confirmation signal to the robot. ACK (abbreviation of Acknowledgment) is the confirmation character, which is a feedback signal used to confirm that the data has been successfully received. Through the ACK confirmation signal, the robot can know that the positioner has successfully received the heartbeat packet, and then confirm that the communication is normal. If N

[0125] consecutive ACKs are lost, it is determined that the communication interruption has started, and the starting time t of the interruption is recorded loss ; Here, N start is a parameter preset according to the reliability requirements and the communication environment. For example, in a precision assembly production line with high requirements for communication stability, after a large number of tests and analyses, it is determined that when 3 consecutive ACKs are lost (i.e., N loss = 3), there is likely a communication failure, and the current time is recorded as the starting time t of the interruption loss . start .

[0126] Embed the reception timestamp in the servo feedback signal F(t) of the positioner to accurately record the time when the ACK signal is received. The reception timestamp is a time marker that records the specific moment when the positioner receives the ACK signal. When the communication is interrupted and the positioner receives the ACK signal again, the time t when the ACK signal resumes reception is obtained based on the reception timestamp. recover Through t start and t recover ,, according to the formula ΔT = t recover -t start calculate the communication interruption duration ΔT. If ΔT > T max , then start the safe trajectory T s stored locally to replace P o (t) and A o (t), and output the fault-tolerant optimization sequence P f (t) of the end pose of the robot and the fault-tolerant optimization sequence A f (t) of the positioner angle that meet the fault-tolerant conditions. The safe trajectory H s stored locally is generated in advance based on historical reliable data, which contains the relatively safe motion trajectory information of the robot and the positioner in case of abnormalities such as communication interruption. For example, in an automotive welding production line, when the communication interruption duration exceeds the communication interruption duration threshold, if the robot and the positioner continue to move according to the original plan, it may cause the welding gun to collide with the vehicle body, damaging the equipment or causing product quality problems. At this time, starting the safe trajectory, the robot and the positioner can move along the pre-set safe path to avoid dangerous situations such as collisions.

[0127] The purpose of this step is to ensure the motion safety of the robot and the positioner during the communication failure by switching to the safe trajectory when the communication interruption time is too long and exceeds the tolerable range, and to prevent equipment damage and production accidents. Its beneficial effects are reflected in many aspects: First, it ensures equipment safety, avoids situations such as collisions between the robot and the positioner due to communication failures, and reduces equipment maintenance costs and downtime; Second, it ensures the continuity of the production process. Although the communication is interrupted, by enabling the safe trajectory, production can continue to a certain extent, reducing production stagnation caused by communication problems and improving production efficiency; Third, it improves product quality, avoids damage to the product caused by abnormal motion, and ensures that the product meets the quality standards. In the entire robot-positioner collaborative communication control method, this step effectively solves the safety and stability problems of the collaborative motion of the robot and the positioner during long-term communication interruption, and is a key link to ensure the reliable operation of the production system.

[0128] Step S2230, if ΔT ≤ T max , then according to P o(t) and P'(t), calculate the optimized pose deviation ΔP o (t); if ΔP o (t) > ΔP max , then trigger the reverse entropy value backtracking algorithm, and search for the nearest stable point P o (t) from P o (t s ) as the new initial point, and generate the corrected robot end - effector pose sequence P new (t) and the corrected positioner angle sequence A new (t); based on P new (t) and A new (t), repeat steps S2110 - S2150 until ΔP o (t) ≤ ΔP max , and output the robot end - effector pose fault - tolerance optimization sequence P f (t) and the positioner angle fault - tolerance optimization sequence A f (t).

[0129] Specifically, in step S2230, if ΔT ≤ T max , then according to P o (t) and P'(t), calculate the optimized pose deviation ΔP o (t); this is because although the communication interruption time does not exceed the threshold, there may still be a situation where the actual movement of the robot is inconsistent with the optimized ideal movement trajectory. The method of calculating the optimized pose deviation is to subtract the corresponding coordinate values and attitude angles respectively. If ΔP o (t) > ΔP max , then trigger the reverse entropy value backtracking algorithm (RER). The principle of this algorithm is to search for the nearest stable point P o (t) from P o (t s ) as the new initial point. A stable point refers to a point in the historical movement trajectory where the robot pose is relatively stable and meets the production process requirements. For example, when the robot is performing high - precision grinding operations, if the current optimized pose deviation is too large, it may lead to unqualified grinding quality. At this time, find the nearest point P o (t) in the historical data of P o (t s ) with good grinding effect and stable pose.

[0130] Based on this stable point, generate the corrected robot end - effector pose sequence P new (t) and the corrected positioner angle sequence A new (t). The specific generation process is to re - plan the subsequent movement trajectory according to the pose information of the stable point, combined with the kinematic models of the robot and the positioner and the production process requirements. Then based on P new(t) and A new (t), repeat steps S2110 to S2150, that is, perform operations such as frequency-domain decomposition, filtering, and optimization again until ΔP o (t) ≤ ΔP max , and finally output the robot end pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f (t). The purpose of this step is to re-optimize the motion trajectory through the reverse entropy value backtracking algorithm in the case of a short communication interruption time but a large pose deviation, ensuring that the coordinated motion of the robot and the positioner meets the accuracy requirements. Its beneficial effect is to avoid product quality problems and equipment damage risks caused by excessive pose deviation, improve the accuracy and reliability of the production process. By continuously adjusting and optimizing the motion trajectory, the robot and the positioner can maintain stable and precise coordinated motion in a complex industrial environment, meet the strict requirements of the production process, and ensure the smooth progress of production.

[0131] Step S2300, use P f (t) and A f (t) as the coordinated control instructions for the robot and the positioner, and at the same time feedback ΔP o (t) to step S1300 to dynamically adjust W(t).

[0132] Specifically, use P f (t) and A f (t) as the coordinated control instructions for the robot and the positioner because after the trajectory optimization and fault tolerance correction in the previous steps, the obtained P f (t) and A f (t) can best adapt to various uncertain factors in actual production, ensuring that the coordinated motion of the robot and the positioner not only meets the accuracy requirements but also has safety. For example, in complex component assembly tasks, only by moving according to the optimized coordinated control instructions can the robot accurately grasp and place components, and the positioner can precisely adjust the workpiece position to ensure the assembly quality.

[0133] At the same time, use ΔP o(t) Feedback to step S1300 to dynamically adjust the time-varying weight matrix W(t). Since the optimized pose deviation reflects the degree of difference between the actual motion of the current robot and positioner and the ideal motion, feeding it back helps to re-evaluate the spatio-temporal coupling weight relationship of the robot and positioner motions at different sampling times according to the real-time motion deviation situation. For example, if the optimized pose deviation is large at a certain moment, it indicates that there are significant problems in the coordinated motion of the robot and positioner at this time. Through the feedback deviation information, the time-varying weight matrix is adjusted in step S1300, increasing the attention to relevant factors at this moment, recalculating parameters such as the spatio-temporal phase coupling factor, and then more reasonably allocating weights to make the subsequent trajectory optimization more targeted. The purpose of this step is to form a closed-loop feedback control system, which improves the accuracy and stability of the coordinated control of the robot and positioner through continuous adjustment and optimization. Its beneficial effect is that by dynamically adjusting the time-varying weight matrix by feedback of the optimized pose deviation, it can adapt to changes in the production process in real time, improving self-adaptability and robustness. In actual production, environmental factors, equipment status, etc. may change at any time. This closed-loop feedback mechanism can timely adjust the control strategy to ensure that the robot and positioner always maintain a good coordinated motion state, improving production efficiency and product quality, reducing the scrap rate and equipment wear, and enhancing the reliability and stability of the entire production system.

[0134] Embodiment 2

[0135] Based on Embodiment 1, this embodiment provides a robot-positioner PLC collaborative communication control system, as Figure 4 shown, including:

[0136] Model construction module: used to receive the robot end pose sequence P(t) and the positioner target angle sequence A(t), and construct an initial collaborative trajectory entropy value model H0 according to P(t) and A(t);

[0137] Calculation module: used to collect the actual robot end pose sequence P'(t), the positioner servo feedback signal F(t), and the welding energy flux density parameter Q(t) in real time; extract the actual positioner angle sequence A'(t) according to the positioner servo feedback signal F(t); generate the time-varying weight matrix W(t) according to P(t), A(t), P'(t), Q(t), and A'(t), and calculate the compensation intensity λ(t);

[0138] Trajectory optimization module: according to the initial collaborative trajectory entropy value model H0, the time-varying weight matrix W(t), and the compensation intensity λ(t), perform trajectory optimization and fault tolerance correction on P'(t) and A'(t) to obtain the robot end pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f (t);

[0139] Instruction output module: Taking P f (t) and A f (t) as the cooperative control instructions for the robot and the positioner.

[0140] In the calculation module, the generation of the time-varying weight matrix W(t) includes:

[0141] Calculating the robot pose deviation ΔP(t) from P(t) and P'(t); obtaining the positioner angle deviation ΔA(t)=[Δα1(t), Δβ1(t)] from A'(t) and A(t); where, Δα1(t) represents the deviation between the actual angle and the ideal angle of the rotation axis of the positioner, and Δβ1(t) represents the deviation between the actual angle and the ideal angle of the tilting axis of the positioner;

[0142] According to ΔP(t) and ΔA(t), constructing the calculation formula for the spatio-temporal phase coupling factor K(t), and generating the time-varying weight matrix W(t) according to the calculation formula for the spatio-temporal phase coupling factor K(t).

[0143] The generation of the time-varying weight matrix W(t) according to the calculation formula for the spatio-temporal phase coupling factor K(t) includes:

[0144] Substituting P(t), A(t), P'(t), and ΔA(t) into the calculation formula for the spatio-temporal phase coupling factor K(t), and calculating the spatio-temporal phase coupling factor value K i at each sampling moment t i =K(t i ), forming the discrete sequence of spatio-temporal phase coupling factors [K1, K2, …, K n1 , where, t i represents the i-th sampling moment, K i represents the spatio-temporal phase coupling factor value at the sampling moment t i , n1 is the total number of sampling points, i is the index of the sampling moment, and 1≤i≤n1;

[0145] Normalizing each element K n1 in the discrete sequence of spatio-temporal phase coupling factors [K1, K2, …, K i to obtain the normalized weight coefficient w ij (t) represents the spatio-temporal coupling weight of the i-th sampling moment relative to other sampling moments, K j is the spatio-temporal phase coupling factor value at the sampling moment t j , j is the index of the sampling moment, and 1≤j≤n1;

[0146] Filling w ij (t) into an n1×n1 matrix to form the time-varying weight matrix W(t).

[0147] In the calculation module, the calculation compensation intensity λ(t) includes:

[0148] Step S1410: Obtain the environmental temperature gradient data ΔT(t), fuse ΔA(t), Q(t) and ΔT(t), and construct a multi-dimensional compensation vector V(t) = [ΔA(t), Q(t), ΔT(t)];

[0149] Step S1420: Extract features from V(t), and calculate the mean μ of each dimension i' , variance and skewness S i' , to obtain a statistical feature vector where m is the number of dimensions of the multi-dimensional compensation vector V(t), m = 3; μ i' is the mean of the i'-th dimension, is the variance of the i'-th dimension, S i' is the skewness of the i'-th dimension, 1 ≤ i' ≤ m; There are 3m statistical features in V s ;

[0150] Step S1430: Calculate the compensation intensity λ(t) according to the statistical feature vector V s .

[0151] In the trajectory optimization module, the trajectory optimization of P'(t) and A'(t) includes:

[0152] Step S2110: Input the initial cooperative trajectory entropy value model H0 into the Kalman-Fourier hybrid filter, and perform frequency domain decomposition on P'(t) and A'(t) in combination with W(t) to obtain a spectrum matrix D(f);

[0153] Step S2120: Perform band-pass filtering on D(f), extract the low-frequency components with frequency value f < f1 in D(f), and generate a low-frequency sub-spectrum matrix D L ; Extract the high-frequency components with frequency value f > f2 in D(f), and generate a high-frequency sub-spectrum matrix D H , where f1 is a preset low-frequency threshold and f2 is a preset high-frequency threshold;

[0154] Step S2130: Apply the entropy value suppression algorithm to D L , introduce λ(t) as the iteration step size, construct a trajectory entropy stability objective function J, and minimize the trajectory entropy stability objective function J by the gradient descent method. After convergence, output the optimized spectrum D' L ;

[0155] Step S2140: Apply the phase lead compensation algorithm to D H , design a feed-forward controller G(s), and based on the feed-forward controller G(s), according to the designed phase lead amount Δφ(f) for DH Perform lead processing on the phase of the medium and high frequency components, and output the compensated spectrum D'. H ;

[0156] Step S2150, perform inverse Fourier transform on D' L and D' H and superimpose them to generate the optimized robot end pose sequence P o (t) and the optimized positioner angle sequence A o (t).

[0157] In the trajectory optimization module, the fault tolerance correction for P'(t) and A'(t) includes:

[0158] Step S2210, set the dynamic fault tolerance threshold, and the dynamic fault tolerance threshold includes the communication interruption duration threshold T max and the pose deviation threshold ΔP max ;

[0159] Step S2220, according to the communication interruption status signal, obtain the communication interruption duration ΔT. If ΔT>T max , then start the safe trajectory H cached locally s to replace P o (t) and A o (t), and output the robot end pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f (t);

[0160] Step S2230, if ΔT≤T max , then calculate the optimized pose deviation ΔP o (t) according to P o (t) and P'(t); if ΔP o (t)>ΔP max , then trigger the reverse entropy value backtracking algorithm, search for the nearest stable point P o (t) from P o (t s ) as the new initial point, and generate the corrected robot end pose sequence P new (t) and the corrected positioner angle sequence A new (t); based on P new (t) and A new (t), repeat steps S2110 to S2150 until ΔP o (t)≤ΔP max , and output the robot end pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f (t).

[0161] The methods and systems of the present application may be implemented in many ways. For example, the methods and systems of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the methods is only for illustration purposes, and the steps of the methods of the present application are not limited to the specific order described above, unless otherwise specifically stated.

[0162] In addition, parts of the above technical solutions provided in the embodiments of the present application that are consistent with the corresponding technical solutions in the prior art in terms of implementation principles are not described in detail to avoid excessive elaboration.

[0163] As described above in the specific embodiments, the purpose, technical solutions, and beneficial effects of the present invention have been further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A robot-positioner PLC collaborative communication control method, characterized in that, The method includes: Receiving the end - pose sequence P(t) of the robot and the target - angle sequence A(t) of the positioner, constructing an initial collaborative - trajectory entropy - value model H0 according to P(t) and A(t); collecting the actual end - pose sequence P'(t) of the robot, the servo - feedback signal F(t) of the positioner, and the welding energy - flux density parameter Q(t) in real - time; extracting the actual - angle sequence A'(t) of the positioner according to the servo - feedback signal F(t) of the positioner; generating a time - varying weight matrix W(t) and calculating the compensation intensity λ(t) according to P(t), A(t), P'(t), Q(t), and A'(t). According to the initial collaborative trajectory entropy value model H0, the time-varying weight matrix W(t), and the compensation intensity λ(t), trajectory optimization and fault tolerance correction are performed on P'(t) and A'(t) to obtain the robot end-effector pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f (t); P f (t) and A f (t) are used as the collaborative control instructions for the robot and the positioner.

2. The robot-positioner PLC collaborative communication control method according to claim 1, characterized in that The expression of the end - pose sequence P(t) of the robot is P(t)=[x(t), y(t), z(t), α(t), β(t), γ(t)], and the expression of the target - angle sequence A(t) of the positioner is A(t)=[α1(t), β1(t)], where t is a time variable, x(t), y(t), and z(t) respectively represent the position coordinates of the robot end in the base coordinate system; α(t), β(t), and γ(t) respectively represent the attitude angles of the robot end around the x, y, and z axes of the base coordinate system; α1(t) represents the target angle of the rotating axis of the positioner, and β1(t) represents the target angle of the tilting axis of the positioner.

3. The robot-positioner PLC collaborative communication control method according to claim 2, wherein The generating a time - varying weight matrix W(t) and calculating the compensation intensity λ(t) according to P(t), A(t), P'(t), Q(t), and A'(t) includes: Calculating the robot - pose deviation ΔP(t) from P(t) and P'(t). The expression of the actual - angle sequence A'(t) of the positioner is A'(t)=[α'1(t), β'1(t)]; obtaining the positioner - angle deviation ΔA(t)=[Δα1(t), Δβ1(t)] from A'(t) and A(t); where α'1(t) represents the actual angle of the rotating axis of the positioner, β'1(t) represents the actual angle of the tilting axis of the positioner, Δα1(t) represents the deviation between the actual angle and the ideal angle of the rotating axis of the positioner, and Δβ1(t) represents the deviation between the actual angle and the ideal angle of the tilting axis of the positioner. Constructing a calculation formula for the spatio - temporal phase - coupling factor K(t) according to ΔP(t) and ΔA(t), and generating a time - varying weight matrix W(t) according to the calculation formula of the spatio - temporal phase - coupling factor K(t). Obtaining the environmental - temperature - gradient data ΔT(t), fusing ΔA(t), Q(t), and ΔT(t), and calculating the compensation intensity λ(t).

4. The robot-positioner PLC collaborative communication control method according to claim 3, wherein The generating a time - varying weight matrix W(t) according to the calculation formula of the spatio - temporal phase - coupling factor K(t) includes: Substitute P(t), A(t), P'(t), and ΔA(t) into the calculation formula of the spatio-temporal phase coupling factor K(t) to calculate the spatio-temporal phase coupling factor value K at each sampling time t i of i = K(t i ), forming a discrete sequence of spatio-temporal phase coupling factors [K1, K2, …, K n1 , where t i represents the i-th sampling time, and K i represents the spatio-temporal phase coupling factor value at the sampling time t i . n1 is the total number of sampling points, i is the index of the sampling time, and 1 ≤ i ≤ n1; Discretize the elements K in the spatio-temporal phase coupling factor discrete sequence [K1, K2, …, K n1 , and normalize each element K i to obtain the normalized weight coefficient w ij (t). w ij (t) represents the spatio-temporal coupling weight of the i-th sampling moment relative to other sampling moments; Fill \(w\) ij (t) into an \(n1\times n1\) matrix to form a time-varying weight matrix \(W(t)\).

5. The robot-positioner PLC collaborative communication control method according to claim 4, wherein, The fusing ΔA(t), Q(t), and ΔT(t) and calculating the compensation intensity λ(t) includes: Fusing ΔA(t), Q(t), and ΔT(t), and constructing a multi - dimensional compensation vector V(t)=[ΔA(t), Q(t), ΔT(t)]. Extract the features of V(t) and calculate the mean μ of each dimension i' , variance and skewness S i' to obtain the statistical feature vector where m is the number of dimensions of the multi-dimensional compensation vector V(t), m = 3; μ i' is the mean of the i'-th dimension, is the variance of the i'-th dimension, S i' is the skewness of the i'-th dimension, 1 ≤ i' ≤ m; There are 3m statistical features in V s ​ According to the statistical feature vector V s , calculate the compensation intensity λ(t).

6. The robot-positioner PLC collaborative communication control method according to claim 5, wherein The trajectory optimization and fault - tolerance correction for P'(t) and A'(t) includes: According to the initial collaborative trajectory entropy value model H0, the time-varying weight matrix W(t), and the compensation intensity λ(t), the trajectories of P'(t) and A'(t) are optimized to obtain the optimized robot end-effector pose sequence P o (t) and the optimized positioner angle sequence A o (t); Perform fault tolerance correction on P o (t) and A o to obtain the fault tolerance optimized sequence of the end - effector pose of the robot P f (t) and the fault tolerance optimized sequence of the angle of the positioner A f (t).

7. The robot-positioner PLC collaborative communication control method according to claim 6, wherein, The optimized robot end - pose sequence P o (t) and the optimized positioner angle sequence A o (t) include: Input the initial collaborative trajectory entropy value model \(H_0\) into the Kalman-Fourier hybrid filter, and perform frequency-domain decomposition on \(P'(t)\) and \(A'(t)\) in combination with \(W(t)\) to obtain the spectrum matrix \(D(f)\). Perform band-pass filtering on D(f), extract the low-frequency components in D(f) with frequency value f < f1, and generate a low-frequency sub-spectrum matrix D L ; extract the high-frequency components in D(f) with frequency value f > f2, and generate a high-frequency sub-spectrum matrix D H , where f1 is a preset low-frequency threshold and f2 is a preset high-frequency threshold; Process D L and D H to generate an optimized robot end - effector pose sequence P o (t) and an optimized positioner angle sequence A o (t).

8. The robot-positioner PLC collaborative communication control method according to claim 7, wherein The said pair of D L and D H are processed to generate an optimized robot end - pose sequence P o (t) and an optimized positioner angle sequence A o (t) includes: For D L Using the entropy value suppression algorithm, the optimized spectrum D' is obtained L ; For D H Adopt the phase - lead compensation algorithm to design the feed - forward controller G(s). Based on the feed - forward controller G(s), according to the designed phase - lead amount Δφ(f), for D H perform phase - lead processing on the phase of the medium - and high - frequency components, and output the compensated spectrum D' H ; For D' L and D' H perform inverse Fourier transform and superimpose to generate the optimized robot end - pose sequence P o (t) and the optimized positioner angle sequence A o (t).

9. The robot-positioner PLC collaborative communication control method according to claim 8, wherein The obtained optimized spectrum D' L comprises: For D L Using the entropy value suppression algorithm, introducing λ(t) as the iteration step size, constructing the trajectory entropy stability objective function J, minimizing the trajectory entropy stability objective function J by the gradient descent method, and outputting the optimized spectrum D' after convergence L .

10. A robot-positioner PLC collaborative communication control system, which is used to implement the robot-positioner PLC collaborative communication control method described in any one of claims 1-9, characterized in that, The system includes: Model construction module: used to receive the robot end pose sequence \(P(t)\) and the positioner target angle sequence \(A(t)\), and construct the initial collaborative trajectory entropy value model \(H_0\) according to \(P(t)\) and \(A(t)\). Calculation module: used to collect the actual pose sequence \(P'(t)\) of the robot end, the positioner servo feedback signal \(F(t)\) and the welding energy flux density parameter \(Q(t)\) in real time; extract the actual angle sequence \(A'(t)\) of the positioner according to the positioner servo feedback signal \(F(t)\); generate the time-varying weight matrix \(W(t)\) according to \(P(t)\), \(A(t)\), \(P'(t)\), \(Q(t)\) and \(A'(t)\), and calculate the compensation intensity \(\lambda(t)\). Trajectory optimization module: According to the initial collaborative trajectory entropy value model H0, time-varying weight matrix W(t), and compensation intensity λ(t), perform trajectory optimization and fault tolerance correction on P'(t) and A'(t) to obtain the robot end pose fault tolerance optimization sequence P f (t) and the positioner angle fault tolerance optimization sequence A f (t); Instruction output module: taking P f (t) and A f (t) as the collaborative control instructions for the robot and the positioner.

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