A method for constructing a motion trajectory model of a large forging manipulator

Abstract

The application discloses a large forging manipulator motion trajectory model construction method, and relates to the technical field of large forging equipment control. The method comprises the following steps: a manipulator kinematics model is established based on an improved D-H parameter method, and model parameters are updated in real time through online calibration; an analytical Jacobian matrix is constructed to identify and smooth the transition singular configuration; in the SE(3) space, spherical linear interpolation or quaternion interpolation is adopted for attitude interpolation, and a smooth trajectory is generated in combination with multi-objective optimization; a multi-source nonlinear constraint model covering machinery, driving, process and safety is established, integrated decision is made based on model predictive control; finite element and multi-body dynamics simulation are used to predict the response, and model parameters are optimized based on measured data through Bayesian updating or least square method. The application significantly improves the end position accuracy, trajectory smoothness and production efficiency, and solves the problems of inaccurate modeling, attitude mutation, incomplete constraint consideration and lack of continuous optimization in the traditional method.

Description

Technical Field

[0001] This invention belongs to the field of control technology for large forging equipment, and specifically relates to a method for constructing a motion trajectory model of a large forging manipulator. Background Technology

[0002] Large forging manipulators are key pieces of equipment in modern heavy manufacturing, widely used in metallurgy, energy, aerospace, and other fields. The quality of the manipulator's motion trajectory directly affects the forming accuracy, surface quality, and production efficiency of forgings. In recent years, with the development of automation technology, the motion control technology of forging manipulators has made significant progress.

[0003] Regarding trajectory planning for forging manipulators, Central South University disclosed a method for planning the linkage trajectory between the manipulator and press in Chinese patent CN102091752B. This method designs a flat anvil drawing process, plans the linkage trajectory between the manipulator and press, and evaluates the rationality of the linkage trajectory by assessing the deformation uniformity through strain calculation. This patent provides a methodological basis for trajectory planning of forging manipulators, but it mainly focuses on process design and trajectory rationality evaluation, paying insufficient attention to kinematic modeling accuracy and smooth attitude transition.

[0004] In manipulator motion modeling, the traditional Denavit-Hartenberg (DH) parametric method is widely used for robot kinematics modeling. Lozano-Perez, in his paper "A Simple Motion-Planning Algorithm for General Robot Manipulators," proposed a configuration space-based general manipulator motion planning algorithm that achieves collision detection and path search through obstacle slice projection representation. However, traditional DH modeling methods primarily consider ideal rigid linkage structures, making it difficult to accurately describe the complex factors present in actual mechanical structures, such as joint flexibility, clearance, and temperature drift, thus limiting modeling accuracy.

[0005] In terms of attitude transition control, existing technologies mostly employ linear interpolation or simple angle interpolation methods. Papadopoulos et al., in their paper "TRAJECTORY PLANNING AND CONTROL FOR MOBILE MANIPULATOR SYSTEMS," proposed a trajectory planning method based on nonholonomic constraints for mobile manipulator systems and designed a model controller to eliminate tracking errors. However, linear interpolation methods are prone to producing discontinuities or abrupt changes in attitude space, especially during large-angle rotations, which can lead to uneven manipulator motion and affect the surface quality of forgings.

[0006] Regarding constraint handling, Zahroof et al. proposed a perceptual-constrained robot manipulator planning method in their paper "Perception-Constrained Robot Manipulator Planning for Satellite Servicing." This method uses the ROS MoveIt! framework to integrate sampling planning algorithms, considering perceptual constraints such as field of view and line of sight. While this method provides a reference for trajectory planning under constraints, it primarily targets satellite service scenarios and does not fully consider the combined effects of multi-source nonlinear constraints such as mechanical structure constraints, drive system limitations, process requirements, and safety boundaries during the forging process.

[0007] Furthermore, existing technologies also have shortcomings in model verification. Although some studies use finite element analysis or dynamic simulation for verification, they lack a continuous improvement mechanism that combines simulation results with measured data and optimizes model parameters through feedback. This makes it difficult for model accuracy to continuously improve with the accumulation of production practice.

[0008] In summary, existing technologies for modeling the motion trajectory of large forging manipulators suffer from the following main problems: (1) Traditional DH modeling methods are insufficient to accurately describe the complexity of actual mechanical structures, including factors such as joint flexibility, clearance, and temperature drift; (2) Discontinuities or abrupt changes occur during posture transitions, affecting forging quality; (3) Path planning does not fully consider multi-source nonlinear constraints such as mechanical structure, drive system, process requirements, and safety boundaries; (4) There is a lack of effective model verification and correction mechanisms, making it difficult to achieve continuous model optimization. These problems result in low forging forming accuracy, poor surface quality, and low production efficiency, hindering the further development of large forging technology. Summary of the Invention

[0009] To address the aforementioned problems, the present invention aims to provide a method for constructing a motion trajectory model of a large forging manipulator, thereby solving the problems of traditional DH modeling methods in the prior art, which are difficult to accurately describe the complexity of actual mechanical structures, the existence of discontinuities or abrupt changes in the attitude transition process affecting the quality of forgings, the path planning not fully considering multi-source nonlinear constraints, and the lack of effective model verification and correction mechanisms.

[0010] To achieve the above objectives, the present invention provides a method for constructing a motion trajectory model of a large forging manipulator, comprising the following steps:

[0011] Step S1: Establish a kinematic model of the manipulator based on the improved DH parameter method. The improved DH parameter method adds joint flexibility parameters, clearance compensation terms and temperature drift coefficients to the existing DH parameters, and performs online calibration through laser tracking or visual measurement to update the model parameters in real time.

[0012] Step S2: Construct an analytical Jacobian matrix based on the improved DH model, identify singular configurations through Jacobian determinant analysis, and achieve smooth transition of singular points through joint space optimization and path replanning;

[0013] Step S3: In the SE(3) space, the position and attitude of the end effector are uniformly represented. The attitude is interpolated by spherical linear interpolation SLERP or quaternion interpolation method. The smooth motion trajectory is generated by combining the time-optimal and energy-optimal criteria.

[0014] Step S4: Establish a multi-source nonlinear constraint model including mechanical structure constraints, drive system constraints, process requirement constraints, and safety boundary constraints. Based on the model predictive control (MPC) design path, trajectory, and attitude integrated decision framework, use the interior point method or sequential quadratic programming (SQP) to solve the constraint optimization problem.

[0015] Step S5: Predict the deformation of the forging using finite element analysis, establish a multibody dynamics model of the manipulator to analyze the dynamic response, and optimize and correct the joint flexibility parameters, clearance compensation term and temperature drift coefficient based on simulation data and measured data using Bayesian update or least squares method.

[0016] Step S1 further includes: establishing a parameter error propagation model, analyzing the influence weight of each parameter on the end pose accuracy, and determining the parameter priority for online calibration based on the influence weight.

[0017] The singularity smooth transition in step S2 specifically involves: when the absolute value of the Jacobian determinant is less than a preset threshold, it is determined to be a singular configuration, and singular region avoidance is achieved by adding redundant degree-of-freedom constraints in the joint space or adjusting the orientation of the path points.

[0018] The formula for calculating the spherical linear interpolation SLERP in step S3 is as follows:

[0019] q(t)=sin[(1-t)θ] / sinθ·q0+sin(tθ) / sinθ·q1

[0020] Where q0 and q1 are quaternion representations of the initial and final attitudes, θ is the angle between the two quaternions, and t is the interpolation parameter and t∈[0,1].

[0021] The multi-source nonlinear constraint model in step S4 specifically includes: joint angle constraint, joint velocity constraint, joint acceleration constraint, end effector workspace constraint, driving torque constraint, collision avoidance constraint, forging process temperature field constraint, and forging deformation coordination constraint.

[0022] In step S4, the decision framework based on model predictive control (MPC) sets the prediction time domain range to 5 to 20 sampling periods and the control time domain range to 1 to 5 sampling periods, and achieves real-time adjustment of path, trajectory and attitude through rolling optimization.

[0023] The model parameter optimization and correction in step S5 includes: collecting actual operating data of the manipulator, calculating the deviation between the measured end pose value and the model prediction value, starting the parameter optimization program when the deviation exceeds a set threshold, updating the joint flexibility parameters and gap compensation terms through the Bayesian update method, and optimizing the temperature drift coefficient through the least squares method.

[0024] A system for constructing motion trajectory models of large forging manipulators, used to implement the method described in any of the above-mentioned embodiments, comprising:

[0025] The parameter modeling and calibration module is used to establish the kinematic model of the manipulator and perform online calibration through laser tracking or visual measurement.

[0026] The kinematics analysis module is used to construct an analytical Jacobian matrix based on the kinematic model of the manipulator, identify singular configurations, and perform smooth transition processing.

[0027] The trajectory planning module is used to generate smooth motion trajectories in SE(3) space using spherical linear interpolation SLERP or quaternion interpolation methods;

[0028] The constraint decision module is used to establish the multi-source nonlinear constraint model and make integrated decisions on path, trajectory and attitude based on model predictive control (MPC).

[0029] The simulation verification module is used to evaluate trajectory performance through finite element analysis and multibody dynamics simulation.

[0030] The model correction module is used to optimize model parameters based on simulation and measured data using Bayesian updates or least squares methods.

[0031] The trajectory execution module is used to convert the optimized trajectory into control commands and send them to the actuators of the manipulator.

[0032] The parameter modeling and calibration module includes an error analysis unit, which is used to establish a parameter error propagation model and analyze the influence weight of each parameter on the end pose accuracy.

[0033] The constraint decision module uses the interior point method or the sequential quadratic programming (SQP) algorithm to solve the constraint optimization problem. Its optimization objective function comprehensively considers trajectory smoothness, motion time, and energy consumption.

[0034] The beneficial effects of this invention are as follows: 1. By adding joint flexibility parameters, gap compensation terms, and temperature drift coefficients to the existing DH parameters, this invention can more accurately describe the complex characteristics of actual mechanical structures, significantly improve the end pose accuracy, and thus improve the forming accuracy of forgings. 2. This invention uses spherical linear interpolation SLERP or quaternion interpolation methods in SE(3) space for attitude interpolation, avoiding the discontinuity or abrupt change in attitude transition in existing methods, ensuring the smoothness of the motion trajectory, and effectively improving the surface quality of forgings. 3. This invention comprehensively considers multi-source nonlinear constraints: it establishes a multi-source nonlinear constraint model including mechanical structure, drive system, process requirements, and safety boundaries, and performs integrated decision-making on path, trajectory, and attitude based on model predictive control (MPC), comprehensively considering various constraints in the actual forging process, and improving the feasibility and safety of trajectory planning. 4. This invention analyzes the influence weight of parameters by establishing a parameter error transmission model, and combines online calibration and a model correction mechanism based on simulation and measured data to achieve adaptive optimization of model parameters and long-term accuracy maintenance, ensuring the stability and reliability of the system during long-term operation. 5. This invention shortens the forging cycle and improves production efficiency while ensuring trajectory quality through the comprehensive optimization of singular point smooth transition processing and time-optimal and energy-optimal criteria. Attached Figure Description

[0035] Figure 1 This is a flowchart of a method for constructing a motion trajectory model of a large forging manipulator according to the present invention.

[0036] Figure 2 This is a detailed flowchart of a method for constructing a motion trajectory model of a large forging manipulator according to the present invention. Detailed Implementation

[0037] To make the content, technical solution, and advantages of the present invention clearer, the present invention will be further described in detail below according to specific embodiments, wherein:

[0038] Example 1

[0039] like Figure 1 , Figure 2 As shown, this invention provides a method for constructing a motion trajectory model of a large forging manipulator, comprising the following steps:

[0040] Step S1: Establish a kinematic model of the manipulator based on the improved DH parameter method. The improved DH parameter method adds joint flexibility parameters, clearance compensation terms and temperature drift coefficients to the existing DH parameters, and performs online calibration through laser tracking or visual measurement to update the model parameters in real time.

[0041] Step S2: Construct an analytical Jacobian matrix based on the improved DH model, identify singular configurations through Jacobian determinant analysis, and achieve smooth transition of singular points through joint space optimization and path replanning;

[0042] Step S3: In the SE(3) space, the position and attitude of the end effector are uniformly represented. The attitude is interpolated by spherical linear interpolation SLERP or quaternion interpolation method. The smooth motion trajectory is generated by combining the time-optimal and energy-optimal criteria.

[0043] Step S4: Establish a multi-source nonlinear constraint model including mechanical structure constraints, drive system constraints, process requirement constraints, and safety boundary constraints. Based on the model predictive control (MPC) design path, trajectory, and attitude integrated decision framework, use the interior point method or sequential quadratic programming (SQP) to solve the constraint optimization problem.

[0044] Step S5: Predict the deformation of the forging using finite element analysis, establish a multibody dynamics model of the manipulator to analyze the dynamic response, and optimize and correct the joint flexibility parameters, clearance compensation term and temperature drift coefficient based on simulation data and measured data using Bayesian update or least squares method.

[0045] Step S1 further includes: establishing a parameter error propagation model, analyzing the influence weight of each parameter on the end pose accuracy, and determining the parameter priority for online calibration based on the influence weight.

[0046] The singularity smooth transition in step S2 specifically involves: when the absolute value of the Jacobian determinant is less than a preset threshold, it is determined to be a singular configuration, and singular region avoidance is achieved by adding redundant degree-of-freedom constraints in the joint space or adjusting the orientation of the path points.

[0047] The formula for calculating the spherical linear interpolation SLERP in step S3 is as follows:

[0048] q(t)=sin[(1-t)θ] / sinθ·q0+sin(tθ) / sinθ·q1

[0049] Where q0 and q1 are quaternion representations of the initial and final attitudes, θ is the angle between the two quaternions, and t is the interpolation parameter and t∈[0,1].

[0050] The multi-source nonlinear constraint model in step S4 specifically includes: joint angle constraint, joint velocity constraint, joint acceleration constraint, end effector workspace constraint, driving torque constraint, collision avoidance constraint, forging process temperature field constraint, and forging deformation coordination constraint.

[0051] In step S4, the decision framework based on model predictive control (MPC) sets the prediction time domain range to 5 to 20 sampling periods and the control time domain range to 1 to 5 sampling periods, and achieves real-time adjustment of path, trajectory and attitude through rolling optimization.

[0052] The model parameter optimization and correction in step S5 includes: collecting actual operating data of the manipulator, calculating the deviation between the measured end pose value and the model prediction value, starting the parameter optimization program when the deviation exceeds a set threshold, updating the joint flexibility parameters and gap compensation terms through the Bayesian update method, and optimizing the temperature drift coefficient through the least squares method.

[0053] A system for constructing motion trajectory models of large forging manipulators, used to implement the method described in any of the above-mentioned embodiments, comprising:

[0054] The parameter modeling and calibration module is used to establish the kinematic model of the manipulator and perform online calibration through laser tracking or visual measurement.

[0055] The kinematics analysis module is used to construct an analytical Jacobian matrix based on the kinematic model of the manipulator, identify singular configurations, and perform smooth transition processing.

[0056] The trajectory planning module is used to generate smooth motion trajectories in SE(3) space using spherical linear interpolation SLERP or quaternion interpolation methods;

[0057] The constraint decision module is used to establish the multi-source nonlinear constraint model and make integrated decisions on path, trajectory and attitude based on model predictive control (MPC).

[0058] The simulation verification module is used to evaluate trajectory performance through finite element analysis and multibody dynamics simulation.

[0059] The model correction module is used to optimize model parameters based on simulation and measured data using Bayesian updates or least squares methods.

[0060] The trajectory execution module is used to convert the optimized trajectory into control commands and send them to the actuators of the manipulator.

[0061] The parameter modeling and calibration module includes an error analysis unit, which is used to establish a parameter error propagation model and analyze the influence weight of each parameter on the end pose accuracy.

[0062] The constraint decision module uses the interior point method or the sequential quadratic programming (SQP) algorithm to solve the constraint optimization problem. Its optimization objective function comprehensively considers trajectory smoothness, motion time, and energy consumption.

[0063] Example 2

[0064] Using the motion trajectory model construction method of a large forging manipulator as described in Example 1, this example details the implementation process of the improved DH parameter method based on trajectory planning using the improved DH parameter method and SLERP interpolation.

[0065] First, based on the existing DH parameter method, a joint flexibility parameter (stiffness coefficient k = 1.2 × 10⁻⁶) is added. 6 N·m / rad), gap compensation term (δ=0.05 mm), and temperature drift coefficient (α=1.5×10). -5 / °C); Online calibration was performed using a laser tracker (Leica AT960) with a sampling frequency of 100 Hz; A parameter error propagation model was established to analyze the influence weight of each parameter on the end pose accuracy, and the calibration priority was determined as: joint flexibility parameters > clearance compensation term > temperature drift coefficient;

[0066] An analytical Jacobian matrix is ​​constructed based on the improved DH model. When the absolute value of the Jacobian determinant is less than 0.001, it is determined to be a singular configuration. This can be avoided by adding redundant degrees of freedom constraints in the joint space.

[0067] Quaternion interpolation is performed in the SE(3) space using the SLERP formula: q(t)=sin[(1-t)θ] / sinθ·q0+sin(tθ) / sinθ·q1, where θ=30°, t∈[0,1];

[0068] A multi-source nonlinear constraint model was established, including joint angle constraints (±90°), joint velocity constraints (0.5 rad / s), and end-effector workspace constraints (2m×2m×1m). Based on the MPC framework, the prediction time domain was set to 10 sampling periods, and the control time domain was set to 3 sampling periods.

[0069] The deformation of the forging was predicted by finite element analysis (maximum deformation 0.8 mm), and the dynamic response was analyzed by establishing a multibody dynamics model (maximum vibration amplitude 0.02 mm). Based on the measured data, parameter optimization was initiated when the end pose deviation exceeded 0.1 mm, and the joint flexibility parameters and clearance compensation terms were updated by Bayesian update method.

[0070] Technical results: End-effector pose accuracy reaches ±0.08 mm (compared to ±0.15 mm using traditional methods), attitude transition smoothness is improved by 40%, singularity avoidance success rate is 100%, and computational efficiency is improved by 25%.

[0071] Example 3

[0072] Using the motion trajectory model construction method for a large forging manipulator as described in Example 1, this example employs trajectory planning based on quaternion interpolation and SQP optimization. Quaternion interpolation is used instead of SLERP in this example, with other conditions remaining the same as in Example 2. The quaternion interpolation parameters are set to θ = 45°, t ∈ [0, 1]. Constraint optimization uses the Sequential Quadratic Programming (SQP) algorithm, with the objective function comprehensively considering trajectory smoothness (weight 0.4), motion time (weight 0.3), and energy consumption (weight 0.3). Forging process temperature field constraints (800-1200°C) and deformation compatibility constraints (maximum allowable deformation 1 mm) are added to the multi-source constraints. The MPC framework is set to a prediction time domain of 15 sampling periods and a control time domain of 5 sampling periods.

[0073] Technical Results: Under complex process constraints, the trajectory planning success rate is increased to 98% (95% in Example 1), energy consumption is reduced by 15%, and temperature field stability is improved by 20%.

[0074] Example 4

[0075] Using the motion trajectory model construction method for a large forging manipulator as described in Example 1, trajectory optimization under high load conditions is performed. This example is specifically designed for high-load conditions of large forgings weighing over 50 tons. The joint flexibility parameter is adjusted to 2.0 × 10⁻⁶. 6 The N·m / rad interval compensation term is increased to 0.1 mm. The singularity threshold is relaxed to 0.005 to accommodate larger deformations. The MPC prediction time domain is shortened to 5 sampling periods to improve real-time performance. A hybrid optimization strategy is adopted: interior point method is used in the coarse planning stage, and SQP is used in the fine planning stage.

[0076] Technical results: Under a 50-ton load, the system stability remains above 95%, the trajectory tracking error is controlled within ±0.15mm, and the real-time calculation delay is <50 ms.

[0077] Actual test of the method of the present invention: Experimental conditions: using the parameter settings of Example 2, the test platform is a 20MN forging manipulator, the forging material is Ti-6Al-4V, the test temperature is 1000°C, and the experimental results are: end pose accuracy ±0.08 mm, attitude transition time 0.8 s, energy consumption 85 kJ, and calculation time 12 s.

[0078] Test using the existing DH parameter method: Experimental conditions: Same test platform and operating conditions, using the existing standard DH parameter method, Euler angle attitude interpolation, and simple constraint considerations. Experimental results: End-effector pose accuracy ±0.15 mm, attitude transition time 1.2 s, energy consumption 100 kJ, computation time 16 s.

[0079] This invention, by adding joint flexibility parameters, clearance compensation terms, and temperature drift coefficients to the existing DH parameters, can more accurately describe the complex characteristics of actual mechanical structures, significantly improve end-effector pose accuracy, and thus enhance forging forming accuracy.

[0080] Obviously, the specific embodiments described above are only some of the embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing a motion trajectory model of a large forging manipulator, characterized in that: Includes the following steps: Step S1: Establish a kinematic model of the manipulator based on the improved DH parameter method. The improved DH parameter method adds joint flexibility parameters, clearance compensation terms and temperature drift coefficients to the existing DH parameters, and performs online calibration through laser tracking or visual measurement to update the model parameters in real time. Step S2: Construct an analytical Jacobian matrix based on the kinematic model of the manipulator, identify singular configurations through Jacobian determinant analysis, and achieve smooth transition of singular points through joint space optimization and path replanning; Step S3: In the SE(3) space, the position and attitude of the end effector are uniformly represented. The attitude is interpolated by spherical linear interpolation SLERP or quaternion interpolation method. The smooth motion trajectory is generated by combining the time-optimal and energy-optimal criteria. Step S4: Establish a multi-source nonlinear constraint model including mechanical structure constraints, drive system constraints, process requirement constraints, and safety boundary constraints. Based on the model predictive control (MPC) design path, trajectory, and attitude integrated decision framework, use the interior point method or sequential quadratic programming (SQP) to solve the constraint optimization problem. Step S5: Predict the deformation of the forging using finite element analysis, establish a multibody dynamics model of the manipulator to analyze the dynamic response, and optimize and correct the joint flexibility parameters, clearance compensation term and temperature drift coefficient based on simulation data and measured data using Bayesian update or least squares method.

2. The method for constructing a motion trajectory model of a large forging manipulator according to claim 1, characterized in that: Step S1 further includes: establishing a parameter error propagation model, analyzing the influence weight of each parameter on the end pose accuracy, and determining the parameter priority for online calibration based on the influence weight.

3. The method for constructing a motion trajectory model of a large forging manipulator according to claim 1, characterized in that: The singularity smooth transition in step S2 specifically involves: when the absolute value of the Jacobian determinant is less than a preset threshold, it is determined to be a singular configuration, and singular region avoidance is achieved by adding redundant degree-of-freedom constraints in the joint space or adjusting the orientation of the path points.

4. The method for constructing a motion trajectory model of a large forging manipulator according to claim 1, characterized in that: The formula for calculating the spherical linear interpolation SLERP in step S3 is as follows: q(t)=sin[(1-t)θ] / sinθ·q0+sin(tθ) / sinθ·q1 Where q0 and q1 are quaternion representations of the initial and final attitudes, θ is the angle between the two quaternions, and t is the interpolation parameter and t∈[0,1].

5. The method for constructing a motion trajectory model of a large forging manipulator according to claim 1, characterized in that: The multi-source nonlinear constraint model in step S4 specifically includes: joint angle constraint, joint velocity constraint, joint acceleration constraint, end effector workspace constraint, driving torque constraint, collision avoidance constraint, forging process temperature field constraint, and forging deformation coordination constraint.

6. The method for constructing a motion trajectory model of a large forging manipulator according to claim 1, characterized in that: In step S4, the decision framework based on model predictive control (MPC) sets the prediction time domain range to 5 to 20 sampling periods and the control time domain range to 1 to 5 sampling periods, and achieves real-time adjustment of path, trajectory and attitude through rolling optimization.

7. The method for constructing a motion trajectory model of a large forging manipulator according to claim 1, characterized in that: The model parameter optimization and correction in step S5 includes: collecting actual operating data of the manipulator, calculating the deviation between the measured end pose value and the model prediction value, starting the parameter optimization program when the deviation exceeds a set threshold, updating the joint flexibility parameters and gap compensation terms through the Bayesian update method, and optimizing the temperature drift coefficient through the least squares method.

8. A motion trajectory model construction system for a large forging manipulator, used to implement the method according to any one of claims 1-7, characterized in that, include: The parameter modeling and calibration module is used to establish the kinematic model of the manipulator and perform online calibration through laser tracking or visual measurement. The kinematics analysis module is used to construct an analytical Jacobian matrix based on the kinematic model of the manipulator, identify singular configurations, and perform smooth transition processing. The trajectory planning module is used to generate smooth motion trajectories in SE(3) space using spherical linear interpolation SLERP or quaternion interpolation methods; The constraint decision module is used to establish the multi-source nonlinear constraint model and make integrated decisions on path, trajectory and attitude based on model predictive control (MPC). The simulation verification module is used to evaluate trajectory performance through finite element analysis and multibody dynamics simulation. The model correction module is used to optimize model parameters based on simulation and measured data using Bayesian updates or least squares methods. The trajectory execution module is used to convert the optimized trajectory into control commands and send them to the actuators of the manipulator.

9. The motion trajectory model construction system for a large forging manipulator according to claim 8, characterized in that: The parameter modeling and calibration module includes an error analysis unit, which is used to establish a parameter error propagation model and analyze the influence weight of each parameter on the end pose accuracy.

10. The motion trajectory model construction system for a large forging manipulator according to claim 8, characterized in that: The constraint decision module uses the interior point method or the sequential quadratic programming (SQP) algorithm to solve the constraint optimization problem. Its optimization objective function comprehensively considers trajectory smoothness, motion time, and energy consumption.

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