In the process of industrial automation, the smart welding robot visual recognition system is a key technology for achieving precise welding. It can obtain information such as the position and shape of the weld in real time and guide the robot to complete high-quality welding operations. However, in practical applications, the visual recognition system faces many technical challenges, and solving these problems is crucial to improving the intelligence level of the smart welding robot.
During the welding process, strong arc light, flying sparks and smoke will be generated, which will seriously interfere with the visual recognition system to obtain clear images. The high-intensity light of the arc light will overexpose the image, resulting in the loss of weld details; flying sparks and smoke will form noise in the image, affecting subsequent image analysis and feature extraction. In addition, different welding environments (such as indoor and outdoor, different workshop lighting conditions) will also change the light intensity and distribution, further increasing the difficulty of image quality control.
The shape, size and position of the weld are diverse, and there are thermal deformation, surface oxidation and other conditions during the welding process, making it difficult to accurately identify the weld features. For example, in welding of some complex joint forms (such as lap joints and fillet joints), the weld edge is irregular, and traditional image recognition algorithms are difficult to accurately extract edge information; and thermal deformation will cause the weld position and shape to change. If the visual recognition system cannot adapt to this change in time, it will cause welding deviation.
Smart welding robot requires the visual recognition system to quickly process image data and output accurate weld position and posture information in a short time to ensure the continuity and stability of the welding process. However, with the improvement of image resolution and the increase of algorithm complexity, the time required for data processing will also be extended accordingly, resulting in a decrease in the real-time performance of the system; if the algorithm is simplified in pursuit of real-time performance, the accuracy of recognition may be sacrificed, and it is difficult to meet the needs of high-precision welding.
In order to improve the performance of the visual recognition system, multi-sensor fusion technology is usually used, such as combining visual sensors with laser sensors, tactile sensors, etc. However, the data collected by different sensors differ in time and space. How to achieve accurate registration and effective fusion of multi-source data, so that various sensors can work together and give full play to their respective advantages is a major technical difficulty currently faced.
For the illumination problem, an adaptive image preprocessing algorithm can be used. By dynamically adjusting the brightness, contrast and color balance of the image, the interference of arc light and spatter is suppressed; the noise in the image is removed by using denoising algorithms (such as median filtering and wavelet denoising) to enhance the weld characteristics. In addition, the installation of special filter devices and protective covers can reduce the direct irradiation of the welding arc to the visual sensor and improve the imaging conditions.
Introduce deep learning algorithms to build a special weld recognition model. The model is trained with a large amount of annotated welding image data so that it can automatically learn the characteristic patterns of the weld, thereby accurately identifying welds of various complex shapes and positions. For example, convolutional neural networks (CNNs) can effectively extract local features of images, and can achieve high-precision edge detection and feature extraction even when the weld is thermally deformed or the surface is uneven.
At the algorithm level, lightweight image recognition algorithms and parallel computing technologies are used to reduce data processing time; in terms of hardware, high-performance graphics processing units (GPUs) and field programmable gate arrays (FPGAs) are used for acceleration to achieve rapid processing of image data. At the same time, a real-time feedback mechanism is established to dynamically adjust the parameters of the recognition algorithm according to the actual situation of the welding process, and improve the real-time response capability of the system while ensuring accuracy. In addition, by improving the multi-sensor fusion algorithm and establishing a unified data coordinate system, the synchronous collection and fusion processing of multi-source data can be achieved, and the overall performance of the visual recognition system can be improved.
