CN-121989263-A - Machine vision-based curved surface tectorial membrane flatness self-adaptive control method

CN121989263ACN 121989263 ACN121989263 ACN 121989263ACN-121989263-A

Abstract

The application discloses a curved surface tectorial membrane flatness self-adaptive control method based on machine vision, and relates to the technical field of mechanical arm control. The system comprises a machine vision sensing module, a robot end effector, a composite controller and a distributed clamping jaw array. The method comprises the steps of obtaining a stripe projection image sequence and end effector dynamic track data, carrying out phase resolving and three-dimensional reconstruction on the image sequence, constructing a digital elevation model, calculating flatness error data, inputting the dynamic track data into a feedforward controller to generate a compensation instruction, inputting the flatness error data into a feedback controller to generate a correction instruction, superposing the flatness error data to obtain a comprehensive correction instruction, obtaining a strain sensitivity matrix, and distributing the comprehensive correction instruction into displacement execution parameters of each clamping jaw by using pseudo-inverse operation and issuing the displacement execution parameters. The application realizes the real-time perception and active intervention of dynamic deformation, and remarkably improves the yield and batch consistency of the curved surface coating.

Inventors

XIE HAN
WANG CHUANYANG
WANG ZIYAN
WU YUKAI
ZHAO TIANFENG
LI ZHIYUAN

Assignees

苏州大学

Dates

Publication Date: 20260508
Application Date: 20260410

Claims (10)

1. The utility model provides a curved surface tectorial membrane roughness self-adaptation control method based on machine vision, is characterized in that is applied to curved surface tectorial membrane control system, the system includes machine vision perception module, robot end effector, compound controller and distributed clamping jaw array, the method includes: Acquiring a stripe projection image sequence of a film covering operation area acquired by the machine vision sensing module, and synchronously acquiring dynamic track data of the robot end effector; Carrying out phase calculation and three-dimensional reconstruction on the fringe projection image sequence to construct a digital elevation model representing the surface morphology of the film; Calculating flatness error data based on the digital elevation model; Inputting the dynamic track data to a feedforward controller in the composite controller to generate a compensation instruction for counteracting the predictable disturbance; Inputting the flatness error data to a feedback controller in the composite controller to generate a correction instruction for correcting the unmodeled residual error; superposing the compensation instruction and the correction instruction to generate a comprehensive correction instruction; Acquiring a strain sensitivity matrix of the distributed clamping jaw array, and distributing the comprehensive correction instruction into displacement execution parameters of each clamping jaw by pseudo-inverse operation; and converting the displacement execution parameters into cooperative control instructions, and sending the cooperative control instructions to a servo controller of each clamping jaw for execution.
2. The machine vision-based curved surface tectorial membrane flatness self-adaptive control method of claim 1, wherein the performing phase resolving and three-dimensional reconstruction on the fringe projection image sequence to construct a digital elevation model representing the film surface morphology comprises: filtering and background correcting a plurality of images in the fringe projection image sequence, wherein the fringe projection image sequence comprises a plurality of fringe projection images with continuous phase shifting in sequence; extracting folding phase distribution at each pixel by adopting a four-step phase shifting algorithm; Recovering the folded phase distribution into absolute phase distribution by using a phase unwrapping algorithm; mapping the absolute phase distribution into a height change field of the film relative to a reference surface based on a pre-calibrated system geometric parameter; And generating the digital elevation model according to the elevation change field.
3. The machine vision-based curved surface tectorial membrane flatness adaptive control method of claim 2, wherein the formula for extracting the folding phase distribution at each pixel by adopting a four-step phase shifting algorithm is as follows: ; Wherein, the For the folding phase at coordinates (x, y), I 1 to I 4 are the light intensity values of four consecutive fringe projection images at the corresponding pixel points.
4. The machine vision-based curved surface film flatness adaptive control method of claim 1, wherein calculating flatness error data based on the digital elevation model comprises: Calculating global root mean square error of the relative mean value of the film surface height deviation based on the digital elevation model; And calculating the maximum surface fluctuation amplitude in the digital elevation model, and generating local peak-to-valley errors.
5. The machine vision-based curved surface tectorial membrane flatness adaptive control method of claim 1, wherein the feedforward controller is constructed based on a first-order inertia plus pure delay model of a film flattening process, and a transfer function of the first-order inertia plus pure delay model is: ; Wherein, the In order to achieve a process gain, For a time constant, θ is the pure delay time, s is the Laplacian; The feedforward controller generates the compensation instruction by using the dynamic trajectory data and the number of look-ahead steps determined by the pure delay time.
6. The machine vision-based curved surface film flatness adaptive control method of claim 5, further comprising: Adopting a recursive least square method with forgetting factors to update a disturbance matrix in the first-order inertia plus pure delay model on line; The update law formula of the disturbance matrix is as follows: ; ; ; Wherein lambda is a forgetting factor, d (k) is a measurable disturbance vector composed of the dynamic trajectory data, The disturbance matrix estimation value at the k moment.
7. The machine vision-based curved surface film flatness adaptive control method of claim 1, wherein obtaining the strain sensitivity matrix of the distributed jaw array, and assigning the integrated correction command to the displacement execution parameters of each jaw by pseudo-inverse operation comprises: Adopting a recursive least square method to identify and update a strain sensitivity matrix representing the mapping relation between the tiny displacement of the clamping jaw and the global flatness on line; solving a molar-Peng Resi generalized inverse matrix for the strain sensitivity matrix; Multiplying the generalized inverse matrix by the comprehensive correction instruction to obtain an optimal adjustment displacement vector which minimizes the sum of squares of displacement of each clamping jaw, and taking the vector as the displacement execution parameter.
8. The machine vision-based adaptive control method for flatness of a curved surface film according to claim 1, wherein the feedback controller adopts an incremental digital PID control law, and the gain scheduling rule comprises: The proportional gain increases nonlinearly with the flatness error data; The integral gain is automatically reduced when the compensation instruction has obvious effect; And the differential gain is adjusted in real time according to the flatness error data and the sign of the product of the change rate of the flatness error data.
9. Machine vision-based curved surface tectorial membrane roughness self-adaptation control device, characterized in that, the device includes: The data acquisition module is used for acquiring the stripe projection image sequence of the laminating operation area acquired by the machine vision sensing module and synchronously acquiring the dynamic track data of the robot end effector; The model construction module is used for carrying out phase resolving and three-dimensional reconstruction on the fringe projection image sequence to construct a digital elevation model for representing the surface morphology of the film; the error calculation module is used for calculating flatness error data based on the digital elevation model; the first instruction module is used for inputting the dynamic track data to a feedforward controller in the composite controller and generating a compensation instruction for counteracting the predictable disturbance; The second instruction module is used for inputting the flatness error data to a feedback controller in the composite controller and generating a correction instruction for correcting the unmodeled residual error; the instruction superposition module is used for superposing the compensation instruction and the correction instruction and generating a comprehensive correction instruction; the instruction distribution module is used for acquiring a strain sensitivity matrix of the distributed clamping jaw array and distributing the comprehensive correction instruction into displacement execution parameters of each clamping jaw by pseudo-inverse operation; and the instruction execution module is used for converting the displacement execution parameters into cooperative control instructions and sending the cooperative control instructions to the servo controllers of the clamping jaws for execution.
10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the processor implements the machine vision-based curved surface coating flatness adaptive control method of any one of claims 1 to 8 when executing the computer program.

Description

Machine vision-based curved surface tectorial membrane flatness self-adaptive control method Technical Field The invention relates to the technical field of mechanical arm control, in particular to a curved surface tectorial membrane flatness self-adaptive control method based on machine vision. Background In recent years, the cross fusion of flexible electronics and a robot body is continuously deepened, the evolution of the robot body form to a bionic flexible direction is promoted, and a batch of emerging applications such as electronic skin, a fitting type biological sensing array, a seamless human-computer interface and the like are emerged. The key requirement of such applications is that the electronic functional layer must dynamically conform to the curved surface of the robot and maintain a stable electromechanical response during continuous motion. Therefore, the precision and reliability of the curved surface film coating process directly determine the performance and service life of the final product. However, when the robot dynamically executes the curved surface film coating task, the film can be subjected to the superposition of multiple complex stresses, namely, the inertia and acceleration effect caused by the movement of the end effector can cause wide fluctuation of the overall tension distribution of the film, the continuous change of the curvature of the substrate can enable the local bending stress to be in a real-time evolution state, and the adhesion force between the film and the substrate can generate transient interface shearing in the dynamic application process. These factors are coupled to each other, so that a multimode transient deformation field covering stretching, compression and shearing is formed inside the film, and the deformation process has triple characteristics of strong nonlinearity, rapid time variation and space non-uniformity. In view of the above challenges, the prior art mainly employs a "dwell-measure-adjust" static or quasi-static control strategy. The method generally relies on suspending the flow after the robot moves to a preset stop point, measuring the height or shape of a fixed point of the film by using a discrete point laser range finder or an off-line vision system, calculating the deviation, and then adjusting the clamp once and continuing to operate. This mode treats visual detection and execution control as two independent decision domains, resulting in control instructions that lag severely behind the evolution process of dynamic deformation. The control effect depends on random factors such as selection of a pause position and measurement of a transient state to a great extent, real-time tracking and active intervention of full-field dynamic deformation under high-speed continuous motion are difficult to realize, stable reproduction among batches cannot be ensured, and therefore the integrated control requirement of high precision and high reliability of flexible coating of a complex curved surface is difficult to meet. Disclosure of Invention The invention aims to provide a curved surface tectorial membrane flatness self-adaptive control method based on machine vision, which is characterized in that a stripe projection image sequence and end effector dynamic track data are synchronously acquired, visual perception and motion information are fused in depth, a feedforward controller is utilized to offset predictable inertia and curvature change disturbance in advance, an unmodeled residual error is corrected in real time through a feedback controller, dynamic response speed and control precision are obviously improved in a cooperative manner, and a comprehensive correction instruction is optimally distributed to a distributed clamping jaw array through pseudo-inverse operation, so that multi-degree-of-freedom cooperative refined stress compensation is realized. Therefore, the invention obviously improves the yield and batch consistency of the curved surface coating. In order to achieve the above purpose, the present invention provides the following technical solutions: In a first aspect, the present invention provides a machine vision-based adaptive control method for flatness of a curved surface film, which is applied to a curved surface film control system, wherein the system comprises a machine vision sensing module, a robot end effector, a composite controller and a distributed clamping jaw array, and the method comprises: The method comprises the steps of acquiring a stripe projection image sequence of a film covering operation area acquired by a machine vision sensing module, and synchronously acquiring dynamic track data of a robot end effector; carrying out phase calculation and three-dimensional reconstruction on the fringe projection image sequence to construct a digital elevation model for representing the surface morphology of the film; Calculating flatness error data based on the digital elevation model; inputting the dynamic track